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Panda SK, Pani P, Sen Gupta PS, Mahanandia N, Kumar Rana M. Computational Assessment of Clinical Drugs against SARS-CoV-2: Foreseeing Molecular Mechanisms and Potent Mpro Inhibitors. Chemphyschem 2025; 26:e202400814. [PMID: 39468850 DOI: 10.1002/cphc.202400814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2024] [Revised: 10/20/2024] [Accepted: 10/28/2024] [Indexed: 10/30/2024]
Abstract
The emergence of new SARS-CoV-2 variants of concern (VOC) is a propulsion for accelerated potential therapeutic discovery. SARS-CoV-2's main protease (Mpro), essential for host cell viral replication, is a pre-eminent druggable protein target. Here, we perform extensive drug re-profiling of the comprehensive Excelra database, which compiles various under-trial drug candidates for COVID-19 treatment. For mechanistic understanding, the most promising screened-out molecules with targets are subjected to molecular dynamics (MD) simulations. Post-MD analyses demonstrate Darunavir, Ponatinib, and Tomivosertib forming a stable complex with Mpro, characterized by less fluctuation of Cα atoms, smooth and stable root-mean-square deviation (RMSD), and robust contact with the active site residues. Likewise, they all have lower binding free energy with Mpro, demonstrating strong affinity. In free energy landscape profiles, the distances from His41 and Cys145 exhibit a single energy minima basin, implying their preponderance in proximity to Mpro's catalytic dyad. Overall, the computational assessment earmarks promising candidates from the Excelra database, emphasizing on carrying out exhaustive biochemical experiments along with clinical trials. The work lays the foundation for potential therapeutic interventions in treating COVID-19.
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Affiliation(s)
- Saroj Kumar Panda
- Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER), Berhampur, Ganjam, Odisha, 760010, India
| | - Pratyush Pani
- Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER), Berhampur, Ganjam, Odisha, 760010, India
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER), Berhampur, Ganjam, Odisha, 760010, India
| | - Parth Sarthi Sen Gupta
- Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER), Berhampur, Ganjam, Odisha, 760010, India
- School of Biosciences and Bioengineering, D Y Patil International University (DYPIU), Akurdi, Pune, Maharashtra, 411044, India
| | - Nimai Mahanandia
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, 110012, India
| | - Malay Kumar Rana
- Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER), Berhampur, Ganjam, Odisha, 760010, India
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Thai QM, Nguyen TH, Lenon GB, Thu Phung HT, Horng JT, Tran PT, Ngo ST. Estimating AChE inhibitors from MCE database by machine learning and atomistic calculations. J Mol Graph Model 2025; 134:108906. [PMID: 39561662 DOI: 10.1016/j.jmgm.2024.108906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 08/17/2024] [Accepted: 11/06/2024] [Indexed: 11/21/2024]
Abstract
Acetylcholinesterase (AChE) is one of the most successful targets for the treatment of Alzheimer's disease (AD). Inhibition of AChE can result in preventing AD. In this context, the machine-learning (ML) model, molecular docking, and molecular dynamics calculations were employed to characterize the potential inhibitors for AChE from MedChemExpress (MCE) database. The trained ML model was initially employed for estimating the inhibitory of MCE compounds. Atomistic simulations including molecular docking and molecular dynamics simulations were then used to confirm ML outcomes. In particular, the physical insights into the ligand binding to AChE were clarified over the calculations. Two compounds, PubChem ID of 130467298 and 132020434, were indicated that they can inhibit AChE.
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Affiliation(s)
- Quynh Mai Thai
- Laboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Trung Hai Nguyen
- Laboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
| | - George Binh Lenon
- School of Health and Biomedical Sciences, RMIT University, Bundoora, Victoria, Australia
| | - Huong Thi Thu Phung
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam
| | - Jim-Tong Horng
- Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Phuong-Thao Tran
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, 008404, Viet Nam
| | - Son Tung Ngo
- Laboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
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3
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Villanueva Guzman MDM, LoMascolo NJ, May D, Thomas CE, Stacey SP, Mounce BC. Rapid Screening to Identify Antivirals against Persistent and Acute Coxsackievirus B3 Infection. ACS Infect Dis 2024; 10:4222-4232. [PMID: 39588796 DOI: 10.1021/acsinfecdis.4c00532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2024]
Abstract
Enteroviruses cause significant morbidity and mortality worldwide, and Coxsackievirus B3 (CVB3) is one of the most commonly reported. Coxsackieviruses establish persistent infection, characterized as infection that is not cleared from host cells generating a continuous infection. No antivirals targeting persistent or acute infection are available, and CVB3 may respond differently depending on the type of infection. Therefore, there is an urgent need for new antiviral drugs to combat acute and persistent CVB3 infection. We developed a system to study persistent CVB3 infection with pancreatic ductal cell line PANC-1, and we used an epithelial cell line, Vero-E6 cells, to study acute CVB3 infection. We maintained persistently infected cells for over a year. Now, in an effort to identify antivirals, using the National Institutes of Health's Developmental Therapeutics Program (DTP), we screened thousands of compounds for activity against acute and persistent CVB3 infection, and among the hits was Ro 5-3335, a 1,4-benzodiazepine nordazepam that acts as a RUNX1-CBFβ leukemia inhibitor. Ro 5-3335 has previously been reported to inhibit HIV-1 gene expression through interference with Tat-mediated transactivation. We confirmed Ro 5-3335's antiviral activity against CVB3 in both acute and persistent infection, in several cell types and at pharmacologically favorable conditions. We show that Ro 5-3335 has minimal cytotoxicity and is antiviral over several rounds of replication. We identified viral egress as a putative target. We also show efficacy against other RNA viruses, but it is ineffective against a model DNA virus. Overall, Ro 5-3335 is a promising antiviral that may target CVB3 infection.
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Affiliation(s)
- Maria Del Mar Villanueva Guzman
- Department of Microbiology and Immunology, Loyola University Chicago, Maywood, Illinois 60153, United States
- Infectious Disease and Immunology Research Institute, Loyola University Chicago, Maywood, Illinois 60153, United States
| | - Natalie J LoMascolo
- Department of Microbiology and Immunology, Loyola University Chicago, Maywood, Illinois 60153, United States
| | - Delaina May
- Department of Microbiology and Immunology, Loyola University Chicago, Maywood, Illinois 60153, United States
| | - Caroline E Thomas
- Department of Microbiology and Immunology, Loyola University Chicago, Maywood, Illinois 60153, United States
| | - Samantha P Stacey
- Department of Microbiology and Immunology, Loyola University Chicago, Maywood, Illinois 60153, United States
| | - Bryan C Mounce
- Department of Microbiology and Immunology, Loyola University Chicago, Maywood, Illinois 60153, United States
- Infectious Disease and Immunology Research Institute, Loyola University Chicago, Maywood, Illinois 60153, United States
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4
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Thai QM, Pham MQ, Tran PT, Nguyen TH, Ngo ST. Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240546. [PMID: 39359466 PMCID: PMC11444763 DOI: 10.1098/rsos.240546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/08/2024] [Accepted: 08/28/2024] [Indexed: 10/04/2024]
Abstract
Targeting acetylcholinesterase is one of the most important strategies for developing therapeutics against Alzheimer's disease. In this work, we have employed a new approach that combines machine learning models, a multi-step similarity search of the PubChem library and molecular dynamics simulations to investigate potential inhibitors for acetylcholinesterase. Our search strategy has been shown to significantly enrich the set of compounds with strong predicted binding affinity to acetylcholinesterase. Both machine learning prediction and binding free energy calculation, based on linear interaction energy, suggest that the compound CID54414454 would bind strongly to acetylcholinesterase and hence is a promising inhibitor.
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Affiliation(s)
- Quynh Mai Thai
- Laboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam
| | - Minh Quan Pham
- Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam
| | - Phuong-Thao Tran
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi 100000, Vietnam
| | - Trung Hai Nguyen
- Laboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam
| | - Son Tung Ngo
- Laboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam
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Nguyen TH, Thai QM, Pham MQ, Minh PTH, Phung HTT. Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds. Mol Divers 2024; 28:553-561. [PMID: 36823394 PMCID: PMC9950021 DOI: 10.1007/s11030-023-10601-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/04/2023] [Indexed: 02/25/2023]
Abstract
To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.
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Affiliation(s)
- Trung Hai Nguyen
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Quynh Mai Thai
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Minh Quan Pham
- Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Pham Thi Hong Minh
- Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Huong Thi Thu Phung
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
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Guo S, Liu Y, Sun Y, Zhou H, Gao Y, Wang P, Zhi H, Zhang Y, Gan J, Ning S. Metabolic-Related Gene Prognostic Index for Predicting Prognosis, Immunotherapy Response, and Candidate Drugs in Ovarian Cancer. J Chem Inf Model 2024; 64:1066-1080. [PMID: 38238993 DOI: 10.1021/acs.jcim.3c01473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2024]
Abstract
Ovarian cancer (OC) is a highly heterogeneous disease, with patients at different tumor staging having different survival times. Metabolic reprogramming is one of the key hallmarks of cancer; however, the significance of metabolism-related genes in the prognosis and therapy outcomes of OC is unclear. In this study, we used weighted gene coexpression network analysis and differential expression analysis to screen for metabolism-related genes associated with tumor staging. We constructed the metabolism-related gene prognostic index (MRGPI), which demonstrated a stable prognostic value across multiple clinical trial end points and multiple validation cohorts. The MRGPI population had its distinct molecular features, mutational characteristics, and immune phenotypes. In addition, we investigated the response to immunotherapy in MRGPI subgroups and found that patients with low MRGPI were prone to benefit from anti-PD-1 checkpoint blockade therapy and exhibited a delayed treatment effect. Meanwhile, we identified four candidate therapeutic drugs (ABT-737, crizotinib, panobinostat, and regorafenib) for patients with high MRGPI, and we evaluated the pharmacokinetics and safety of the candidate drugs. In summary, the MRGPI was a robust clinical feature that could predict patient prognosis, immunotherapy response, and candidate drugs, facilitating clinical decision making and therapeutic strategy of OC.
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Affiliation(s)
- Shuang Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China
| | - Yuwei Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yue Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hanxiao Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yue Gao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Peng Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hui Zhi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yakun Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Jing Gan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shangwei Ning
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
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7
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Wang H. Prediction of protein-ligand binding affinity via deep learning models. Brief Bioinform 2024; 25:bbae081. [PMID: 38446737 PMCID: PMC10939342 DOI: 10.1093/bib/bbae081] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/31/2024] [Indexed: 03/08/2024] Open
Abstract
Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein-ligand binding affinity. We start with a brief introduction to protein-ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein-ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein-ligand binding affinity via DL models.
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Affiliation(s)
- Huiwen Wang
- School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China
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Gasmi A, Noor S, Menzel A, Khanyk N, Semenova Y, Lysiuk R, Beley N, Bolibrukh L, Gasmi Benahmed A, Storchylo O, Bjørklund G. Potential Drugs in COVID-19 Management. Curr Med Chem 2024; 31:3245-3264. [PMID: 37461346 DOI: 10.2174/0929867331666230717154101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/27/2023] [Accepted: 06/05/2023] [Indexed: 11/18/2023]
Abstract
The SARS-CoV-2 virus first emerged in China in December 2019 and quickly spread worldwide. Despite the absence of a vaccination or authorized drug specifically developed to combat this infection, certain medications recommended for other diseases have shown potential effectiveness in treating COVID-19, although without definitive confirmation. This review aims to evaluate the existing literature on the efficacy of these medications against COVID-19. The review encompasses various potential treatments, including antiviral medications, anti-malaria and anti-rheumatic drugs, vaccines, corticosteroids, non-steroidal anti-inflammatory drugs (NSAIDs), antipyretic and analgesic medicines, antiparasitic drugs, and statins. The analysis also addresses the potential benefits and drawbacks of these medications, as well as their effects on hypertension and diabetes. Although these therapies hold promise against COVID-19, further research, including suitable product production or clinical testing, is needed to establish their therapeutic efficacy.
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Affiliation(s)
- Amin Gasmi
- Société Francophone de Nutrithérapie et de Nutrigénétique Appliquée, Villeurbanne, France
| | - Sadaf Noor
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan
| | | | - Nataliia Khanyk
- Danylo Halytsky Lviv National Medical University, Lviv, Ukraine
- CONEM Ukraine Life Science Research Group, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine
| | - Yuliya Semenova
- Nazarbayev University School of Medicine, Astana, Kazakhstan
| | - Roman Lysiuk
- Danylo Halytsky Lviv National Medical University, Lviv, Ukraine
- CONEM Ukraine Life Science Research Group, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine
| | - Nataliya Beley
- I. Ya. Horbachevsky Ternopil National Medical University, Ternopil, Ukraine
| | | | | | - Olha Storchylo
- Medical Chemistry Department, Odessa National Medical University, Odesa, Ukraine
| | - Geir Bjørklund
- Council for Nutritional and Environmental Medicine (CONEM), Mo i Rana, Norway
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Wu Y, Li K, Li M, Pu X, Guo Y. Attention Mechanism-Based Graph Neural Network Model for Effective Activity Prediction of SARS-CoV-2 Main Protease Inhibitors: Application to Drug Repurposing as Potential COVID-19 Therapy. J Chem Inf Model 2023; 63:7011-7031. [PMID: 37960886 DOI: 10.1021/acs.jcim.3c01280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Compared to de novo drug discovery, drug repurposing provides a time-efficient way to treat coronavirus disease 19 (COVID-19) that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 main protease (Mpro) has been proved to be an attractive drug target due to its pivotal involvement in viral replication and transcription. Here, we present a graph neural network-based deep-learning (DL) strategy to prioritize the existing drugs for their potential therapeutic effects against SARS-CoV-2 Mpro. Mpro inhibitors were represented as molecular graphs ready for graph attention network (GAT) and graph isomorphism network (GIN) modeling for predicting the inhibitory activities. The result shows that the GAT model outperforms the GIN and other competitive models and yields satisfactory predictions for unseen Mpro inhibitors, confirming its robustness and generalization. The attention mechanism of GAT enables to capture the dominant substructures and thus to realize the interpretability of the model. Finally, we applied the optimal GAT model in conjunction with molecular docking simulations to screen the Drug Repurposing Hub (DRH) database. As a result, 18 drug hits with best consensus prediction scores and binding affinity values were identified as the potential therapeutics against COVID-19. Both the extensive literature searching and evaluations on adsorption, distribution, metabolism, excretion, and toxicity (ADMET) illustrate the premium drug-likeness and pharmacokinetic properties of the drug candidates. Overall, our work not only provides an effective GAT-based DL prediction tool for inhibitory activity of SARS-CoV-2 Mpro inhibitors but also provides theoretical guidelines for drug discovery in the COVID-19 treatment.
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Affiliation(s)
- Yanling Wu
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Kun Li
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu 610064, China
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Duan L, Tang B, Luo S, Xiong D, Wang Q, Xu X, Zhang JZH. Entropy driven cooperativity effect in multi-site drug optimization targeting SARS-CoV-2 papain-like protease. Cell Mol Life Sci 2023; 80:313. [PMID: 37796323 PMCID: PMC11072831 DOI: 10.1007/s00018-023-04985-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/07/2023] [Accepted: 09/26/2023] [Indexed: 10/06/2023]
Abstract
Papain-like protease (PLpro), a non-structural protein encoded by SARS-CoV-2, is an important therapeutic target. Regions 1 and 5 of an existing drug, GRL0617, can be optimized to produce cooperativity with PLpro binding, resulting in stronger binding affinity. This work investigated the origin of the cooperativity using molecular dynamics simulations combined with the interaction entropy (IE) method. The regions' improvement exhibits cooperativity by calculating the binding free energies between the complex of PLpro-inhibitor. The thermodynamic integration method further verified the cooperativity generated in the drug improvement. To further determine the specific source of cooperativity, enthalpy and entropy in the complexes were calculated using molecular mechanics/generalized Born surface area and IE. The results show that the entropic change is an important contributor to the cooperativity. Our study also identified residues P248, Q269, and T301 that play a significant role in cooperativity. The optimization of the inhibitor stabilizes these residues and minimizes the entropic loss, and the cooperativity observed in the binding free energy can be attributed to the change in the entropic contribution of these residues. Based on our research, the application of cooperativity can facilitate drug optimization, and provide theoretical ideas for drug development that leverage cooperativity by reducing the contribution of entropy through multi-locus binding.
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Affiliation(s)
- Lili Duan
- School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China.
| | - Bolin Tang
- School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
| | - Song Luo
- School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
| | - Danyang Xiong
- School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
| | - Qihang Wang
- School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
| | - Xiaole Xu
- School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China
| | - John Z H Zhang
- Faculty of Synthetic Biology and Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China.
- Department of Chemistry, New York University, New York, NY, 10003, USA.
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11
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Elkashlan M, Ahmad RM, Hajar M, Al Jasmi F, Corchado JM, Nasarudin NA, Mohamad MS. A review of SARS-CoV-2 drug repurposing: databases and machine learning models. Front Pharmacol 2023; 14:1182465. [PMID: 37601065 PMCID: PMC10436567 DOI: 10.3389/fphar.2023.1182465] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/06/2023] [Indexed: 08/22/2023] Open
Abstract
The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.
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Affiliation(s)
- Marim Elkashlan
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Rahaf M Ahmad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Malak Hajar
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Fatma Al Jasmi
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Juan Manuel Corchado
- Departamento de Informática y Automática, Facultad de Ciencias, Grupo de Investigación BISITE, Instituto de Investigación Biomédica de Salamanca, University of Salamanca, Salamanca, Spain
| | - Nurul Athirah Nasarudin
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
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12
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Zaeri N. Artificial intelligence and machine learning responses to COVID-19 related inquiries. J Med Eng Technol 2023; 47:301-320. [PMID: 38625639 DOI: 10.1080/03091902.2024.2321846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 02/18/2024] [Indexed: 04/17/2024]
Abstract
Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.
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Affiliation(s)
- Naser Zaeri
- Faculty of Computer Studies, Arab Open University, Kuwait
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13
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Feng H, Jiang J, Wei GW. Machine-learning repurposing of DrugBank compounds for opioid use disorder. Comput Biol Med 2023; 160:106921. [PMID: 37178605 DOI: 10.1016/j.compbiomed.2023.106921] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/30/2023] [Accepted: 04/13/2023] [Indexed: 05/15/2023]
Abstract
Opioid use disorder (OUD) is a chronic and relapsing condition that involves the continued and compulsive use of opioids despite harmful consequences. The development of medications with improved efficacy and safety profiles for OUD treatment is urgently needed. Drug repurposing is a promising option for drug discovery due to its reduced cost and expedited approval procedures. Computational approaches based on machine learning enable the rapid screening of DrugBank compounds, identifying those with the potential to be repurposed for OUD treatment. We collected inhibitor data for four major opioid receptors and used advanced machine learning predictors of binding affinity that fuse the gradient boosting decision tree algorithm with two natural language processing (NLP)-based molecular fingerprints and one traditional 2D fingerprint. Using these predictors, we systematically analyzed the binding affinities of DrugBank compounds on four opioid receptors. Based on our machine learning predictions, we were able to discriminate DrugBank compounds with various binding affinity thresholds and selectivities for different receptors. The prediction results were further analyzed for ADMET (absorption, distribution, metabolism, excretion, and toxicity), which provided guidance on repurposing DrugBank compounds for the inhibition of selected opioid receptors. The pharmacological effects of these compounds for OUD treatment need to be tested in further experimental studies and clinical trials. Our machine learning studies provide a valuable platform for drug discovery in the context of OUD treatment.
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Affiliation(s)
- Hongsong Feng
- Department of Mathematics, Michigan State University, MI 48824, USA
| | - Jian Jiang
- Department of Mathematics, Michigan State University, MI 48824, USA; Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, 430200, PR China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA.
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14
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Feng H, Elladki R, Jiang J, Wei GW. Machine-learning analysis of opioid use disorder informed by MOR, DOR, KOR, NOR and ZOR-based interactome networks. Comput Biol Med 2023; 157:106745. [PMID: 36924727 DOI: 10.1016/j.compbiomed.2023.106745] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/11/2023] [Accepted: 03/04/2023] [Indexed: 03/17/2023]
Abstract
Opioid use disorder (OUD) continuously poses major public health challenges and social implications worldwide with dramatic rise of opioid dependence leading to potential abuse. Despite that a few pharmacological agents have been approved for OUD treatment, the efficacy of said agents for OUD requires further improvement in order to provide safer and more effective pharmacological and psychosocial treatments. Proteins including mu, delta, kappa, nociceptin, and zeta opioid receptors are the direct targets of opioids and play critical roles in therapeutic treatments. The protein-protein interaction (PPI) networks of the these receptors increase the complexity in the drug development process for an effective opioid addiction treatment. The report below presents a PPI-network informed machine-learning study of OUD. We have examined more than 500 proteins in the five opioid receptor networks and subsequently collected 74 inhibitor datasets. Machine learning models were constructed by pairing gradient boosting decision tree (GBDT) algorithm with two advanced natural language processing (NLP)-based autoencoder and Transformer fingerprints for molecules. With these models, we systematically carried out evaluations of screening and repurposing potential of more than 120,000 drug candidates for four opioid receptors. In addition, absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were also considered in the screening of potential drug candidates. Our machine-learning tools determined a few inhibitor compounds with desired potency and ADMET properties for nociceptin opioid receptors. Our approach offers a valuable and promising tool for the pharmacological development of OUD treatments.
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Affiliation(s)
- Hongsong Feng
- Department of Mathematics, Michigan State University, MI 48824, USA
| | - Rana Elladki
- Department of Mathematics, Michigan State University, MI 48824, USA
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, 430200, PR China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA.
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15
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Feng H, Wei GW. Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models. Comput Biol Med 2023; 153:106491. [PMID: 36599209 PMCID: PMC10120853 DOI: 10.1016/j.compbiomed.2022.106491] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 11/29/2022] [Accepted: 12/27/2022] [Indexed: 12/29/2022]
Abstract
The human ether-a-go-go (hERG) potassium channel (Kv11.1) plays a critical role in mediating cardiac action potential. The blockade of this ion channel can potentially lead fatal disorder and/or long QT syndrome. Many drugs have been withdrawn because of their serious hERG-cardiotoxicity. It is crucial to assess the hERG blockade activity in the early stage of drug discovery. We are particularly interested in the hERG-cardiotoxicity of compounds collected in the DrugBank database considering that many DrugBank compounds have been approved for therapeutic treatments or have high potential to become drugs. Machine learning-based in silico tools offer a rapid and economical platform to virtually screen DrugBank compounds. We design accurate and robust classifiers for blockers/non-blockers and then build regressors to quantitatively analyze the binding potency of the DrugBank compounds on the hERG channel. Molecular sequences are embedded with two natural language processing (NLP) methods, namely, autoencoder and transformer. Complementary three-dimensional (3D) molecular structures are embedded with two advanced mathematical approaches, i.e., topological Laplacians and algebraic graphs. With our state-of-the-art tools, we reveal that 227 out of the 8641 DrugBank compounds are potential hERG blockers, suggesting serious drug safety problems. Our predictions provide guidance for the further experimental interrogation of DrugBank compounds' hERG-cardiotoxicity.
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Affiliation(s)
- Hongsong Feng
- Department of Mathematics, Michigan State University, MI 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA.
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16
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A Clinical Insight on New Discovered Molecules and Repurposed Drugs for the Treatment of COVID-19. Vaccines (Basel) 2023; 11:vaccines11020332. [PMID: 36851211 PMCID: PMC9967525 DOI: 10.3390/vaccines11020332] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/28/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began churning out incredulous terror in December 2019. Within several months from its first detection in Wuhan, SARS-CoV-2 spread to the rest of the world through droplet infection, making it a pandemic situation and a healthcare emergency across the globe. The available treatment of COVID-19 was only symptomatic as the disease was new and no approved drug or vaccine was available. Another challenge with COVID-19 was the continuous mutation of the SARS-CoV-2 virus. Some repurposed drugs, such as hydroxychloroquine, chloroquine, and remdesivir, received emergency use authorization in various countries, but their clinical use is compromised with either severe and fatal adverse effects or nonavailability of sufficient clinical data. Molnupiravir was the first molecule approved for the treatment of COVID-19, followed by Paxlovid™, monoclonal antibodies (MAbs), and others. New molecules have variable therapeutic efficacy against different variants or strains of SARS-CoV-2, which require further investigations. The aim of this review is to provide in-depth information on new molecules and repurposed drugs with emphasis on their general description, mechanism of action (MOA), correlates of protection, dose and dosage form, route of administration, clinical trials, regulatory approval, and marketing authorizations.
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17
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Nguyen TH, Tam NM, Tuan MV, Zhan P, Vu VV, Quang DT, Ngo ST. Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations. Chem Phys 2023; 564:111709. [PMID: 36188488 PMCID: PMC9511900 DOI: 10.1016/j.chemphys.2022.111709] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/11/2022] [Accepted: 09/21/2022] [Indexed: 11/28/2022]
Abstract
Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of R = 0.748 ± 0.044 . Atomistic simulations were then used to refine the outcome of the ML model. Using LIE/FEP calculations, nine compounds from the top 100 ML inhibitors were suggested to bind well to the protease with the domination of van der Waals interactions. Furthermore, the binding affinity of these compounds is also higher than that of nirmatrelvir, which was recently approved by the US FDA to treat COVID-19. In addition, the ligands altered the catalytic triad Cys145 - His41 - Asp187, possibly disturbing the biological activity of SARS-CoV-2.
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Affiliation(s)
- Trung Hai Nguyen
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
| | - Nguyen Minh Tam
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
| | - Mai Van Tuan
- Department of Microbiology, Hue Central Hospital, Hue City, Viet Nam
| | - Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, PR China
| | - Van V Vu
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam
| | - Duong Tuan Quang
- Department of Chemistry, Hue University, Thua Thien Hue Province, Hue City, Viet Nam
| | - Son Tung Ngo
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
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18
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Dong J, Varbanov M, Philippot S, Vreken F, Zeng WB, Blay V. Ligand-based discovery of coronavirus main protease inhibitors using MACAW molecular embeddings. J Enzyme Inhib Med Chem 2023; 38:24-35. [PMID: 36305272 PMCID: PMC9621234 DOI: 10.1080/14756366.2022.2132486] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Ligand-based drug design methods are thought to require large experimental datasets to become useful for virtual screening. In this work, we propose a computational strategy to design novel inhibitors of coronavirus main protease, Mpro. The pipeline integrates publicly available screening and binding affinity data in a two-stage machine-learning model using the recent MACAW embeddings. Once trained, the model can be deployed to rapidly screen large libraries of molecules in silico. Several hundred thousand compounds were virtually screened and 10 of them were selected for experimental testing. From these 10 compounds, 8 showed a clear inhibitory effect on recombinant Mpro, with half-maximal inhibitory concentration values (IC50) in the range 0.18–18.82 μM. Cellular assays were also conducted to evaluate cytotoxic, haemolytic, and antiviral properties. A promising lead compound against coronavirus Mpro was identified with dose-dependent inhibition of virus infectivity and minimal toxicity on human MRC-5 cells.
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Affiliation(s)
- Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, P. R. China
| | - Mihayl Varbanov
- Université de Lorraine, CNRS, Nancy, France
- Laboratoire de Virologie, CHRU de Nancy Brabois, Vandoeuvre-lès-Nancy, France
| | | | | | - Wen-bin Zeng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, P. R. China
| | - Vincent Blay
- Department of Microbiology and Environmental Toxicology, University of California at Santa Cruz, Santa Cruz, CA, USA
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19
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Ngo ST, Nguyen TH, Tung NT, Vu VV, Pham MQ, Mai BK. Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches. Phys Chem Chem Phys 2022; 24:29266-29278. [PMID: 36449268 DOI: 10.1039/d2cp04476e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.
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Affiliation(s)
- Son Tung Ngo
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Trung Hai Nguyen
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Nguyen Thanh Tung
- Institute of Materials Science, Vietnam Academy of Science and Technology, Hanoi, Vietnam. .,Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Van V Vu
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
| | - Minh Quan Pham
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam.,Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Binh Khanh Mai
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
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20
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Xiong D, Zhao X, Luo S, Zhang JZH, Duan L. Molecular Mechanism of the Non-Covalent Orally Targeted SARS-CoV-2 M pro Inhibitor S-217622 and Computational Assessment of Its Effectiveness against Mainstream Variants. J Phys Chem Lett 2022; 13:8893-8901. [PMID: 36126063 DOI: 10.1021/acs.jpclett.2c02428] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Convenient and efficient therapeutic agents are urgently needed to block the continued spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, the mechanism for the novel orally targeted SARS-CoV-2 main protease (Mpro) inhibitor S-217622 is revealed through a molecular dynamics simulation. The difference in the movement modes of the S-217622-Mpro complex and apo-Mpro suggested S-217622 could inhibit the motility intensity of Mpro, thus maintaining their stable binding. Subsequent energy calculations showed that the P2 pharmacophore possessed the highest energy contribution among the three pharmacophores of S-217622. Additionally, hot-spot residues H41, M165, C145, E166, and H163 have strong interactions with S-217622. To further investigate the resistance of S-217622 to six mainstream variants, the binding modes of S-217622 with these variants were elucidated. The subtle differences in energy compared to that of the wild type implied that the binding patterns of these systems were similar, and S-217622 still inhibited these variants. We hope this work will provide theoretical insights for optimizing novel targeted Mpro drugs.
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Affiliation(s)
- Danyang Xiong
- School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250014, China
| | - Xiaoyu Zhao
- School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250014, China
| | - Song Luo
- School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250014, China
| | - John Z H Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Lili Duan
- School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250014, China
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21
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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22
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Gao K, Wang R, Chen J, Cheng L, Frishcosy J, Huzumi Y, Qiu Y, Schluckbier T, Wei X, Wei GW. Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. Chem Rev 2022; 122:11287-11368. [PMID: 35594413 PMCID: PMC9159519 DOI: 10.1021/acs.chemrev.1c00965] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.
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Affiliation(s)
- Kaifu Gao
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jiahui Chen
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Limei Cheng
- Clinical
Pharmacology and Pharmacometrics, Bristol
Myers Squibb, Princeton, New Jersey 08536, United States
| | - Jaclyn Frishcosy
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuta Huzumi
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuchi Qiu
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Tom Schluckbier
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Xiaoqi Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
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23
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Cong Y, Endo T. Multi-Omics and Artificial Intelligence-Guided Drug Repositioning: Prospects, Challenges, and Lessons Learned from COVID-19. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:361-371. [PMID: 35759424 DOI: 10.1089/omi.2022.0068] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Drug repurposing is of interest for therapeutics innovation in many human diseases including coronavirus disease 2019 (COVID-19). Methodological innovations in drug repurposing are currently being empowered by convergence of omics systems science and digital transformation of life sciences. This expert review article offers a systematic summary of the application of artificial intelligence (AI), particularly machine learning (ML), to drug repurposing and classifies and introduces the common clustering, dimensionality reduction, and other methods. We highlight, as a present-day high-profile example, the involvement of AI/ML-based drug discovery in the COVID-19 pandemic and discuss the collection and sharing of diverse data types, and the possible futures awaiting drug repurposing in an era of AI/ML and digital technologies. The article provides new insights on convergence of multi-omics and AI-based drug repurposing. We conclude with reflections on the various pathways to expedite innovation in drug development through drug repurposing for prompt responses to the current COVID-19 pandemic and future ecological crises in the 21st century.
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Affiliation(s)
- Yi Cong
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Toshinori Endo
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
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24
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Nguyen TH, Tran PT, Pham NQA, Hoang VH, Hiep DM, Ngo ST. Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies. ACS OMEGA 2022; 7:20673-20682. [PMID: 35755364 PMCID: PMC9219098 DOI: 10.1021/acsomega.2c00908] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 05/27/2022] [Indexed: 05/30/2023]
Abstract
Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease (AD) treatment. In this work, a machine learning model was trained to rapidly and accurately screen large chemical databases for the potential inhibitors of AChE. The obtained results were then validated via in vitro enzyme assay. Moreover, atomistic simulations including molecular docking and molecular dynamics simulations were then used to understand molecular insights into the binding process of ligands to AChE. In particular, two compounds including benzyl trifluoromethyl ketone and trifluoromethylstyryl ketone were indicated as highly potent inhibitors of AChE because they established IC50 values of 0.51 and 0.33 μM, respectively. The obtained IC50 of two compounds is significantly lower than that of galantamine (2.10 μM). The predicted log(BB) suggests that the compounds may be able to traverse the blood-brain barrier. A good agreement between computational and experimental studies was observed, indicating that the hybrid approach can enhance AD therapy.
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Affiliation(s)
- Trung Hai Nguyen
- Laboratory
of Theoretical and Computational Biophysics, Advanced Institute of
Materials Science, Ton Duc Thang
University, Ho Chi Minh City, Vietnam
- Faculty
of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Phuong-Thao Tran
- Hanoi
University of Pharmacy, 13-15 Le Thanh Tong, Hanoi 008404, Vietnam
| | - Ngoc Quynh Anh Pham
- Faculty
of Chemical Engineering, Ho Chi Minh City
University of Technology (HCMUT), Ho Chi Minh City 700000, Vietnam
| | - Van-Hai Hoang
- Faculty
of Pharmacy, Phenikka University, Hanoi 008404, Vietnam
- Phenikka
Institute for Advanced Study, Phenikka University, Hanoi 008404, Vietnam
| | - Dinh Minh Hiep
- Department
of Agriculture and Rural Development, Ho Chi Minh City 700000, Vietnam
| | - Son Tung Ngo
- Laboratory
of Theoretical and Computational Biophysics, Advanced Institute of
Materials Science, Ton Duc Thang
University, Ho Chi Minh City, Vietnam
- Faculty
of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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25
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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26
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Peng H, Ding C, Jiang L, Tang W, Liu Y, Zhao L, Yi Z, Ren H, Li C, He Y, Zheng X, Tang H, Chen Z, Qi Z, Zhao P. Discovery of potential anti-SARS-CoV-2 drugs based on large-scale screening in vitro and effect evaluation in vivo. SCIENCE CHINA. LIFE SCIENCES 2022; 65:1181-1197. [PMID: 34962614 PMCID: PMC8713546 DOI: 10.1007/s11427-021-2031-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/17/2021] [Indexed: 12/22/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global crisis. Clinical candidates with high efficacy, ready availability, and that do not develop resistance are in urgent need. Despite that screening to repurpose clinically approved drugs has provided a variety of hits shown to be effective against SARS-CoV-2 infection in cell culture, there are few confirmed antiviral candidates in vivo. In this study, 94 compounds showing high antiviral activity against SARS-CoV-2 in Vero E6 cells were identified from 2,580 FDA-approved small-molecule drugs. Among them, 24 compounds with low cytotoxicity were selected, and of these, 17 compounds also effectively suppressed SARS-CoV-2 infection in HeLa cells transduced with human ACE2. Six compounds disturb multiple processes of the SARS-CoV-2 life cycle. Their prophylactic efficacies were determined in vivo using Syrian hamsters challenged with SARS-CoV-2 infection. Seven compounds reduced weight loss and promoted weight regain of hamsters infected not only with the original strain but also the D614G variant. Except for cisatracurium, six compounds reduced hamster pulmonary viral load, and IL-6 and TNF-α mRNA when assayed at 4 d postinfection. In particular, sertraline, salinomycin, and gilteritinib showed similar protective effects as remdesivir in vivo and did not induce antiviral drug resistance after 10 serial passages of SARS-CoV-2 in vitro, suggesting promising application for COVID-19 treatment.
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Affiliation(s)
- Haoran Peng
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China
| | - Cuiling Ding
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China
| | - Liangliang Jiang
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China
| | - Wanda Tang
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China
| | - Yan Liu
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China
| | - Lanjuan Zhao
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China
| | - Zhigang Yi
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Hao Ren
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China
| | - Chong Li
- Cancer Institute, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200000, China
| | - Yanhua He
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China
| | - Xu Zheng
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China
| | - Hailin Tang
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China
| | - Zhihui Chen
- Department of Infectious Disease, Changhai Hospital, Shanghai, 200433, China.
| | - Zhongtian Qi
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China.
| | - Ping Zhao
- Department of Microbiology, Second Military Medical University, Shanghai Key Laboratory of Medical Biodefense, Shanghai, 200433, China.
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27
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Wang H, Liu H, Ning S, Zeng C, Zhao Y. DLSSAffinity: protein-ligand binding affinity prediction via a deep learning model. Phys Chem Chem Phys 2022; 24:10124-10133. [PMID: 35416807 DOI: 10.1039/d1cp05558e] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Evaluating the protein-ligand binding affinity is a substantial part of the computer-aided drug discovery process. Most of the proposed computational methods predict protein-ligand binding affinity using either limited full-length protein 3D structures or simple full-length protein sequences as the input features. Thus, protein-ligand binding affinity prediction remains a fundamental challenge in drug discovery. In this study, we proposed a novel deep learning-based approach, DLSSAffinity, to accurately predict the protein-ligand binding affinity. Unlike the existing methods, DLSSAffinity uses the pocket-ligand structural pairs as the local information to predict short-range direct interactions. Besides, DLSSAffinity also uses the full-length protein sequence and ligand SMILES as the global information to predict long-range indirect interactions. We tested DLSSAffinity on the PDBbind benchmark. The results showed that DLSSAffinity achieves Pearson's R = 0.79, RMSE = 1.40, and SD = 1.35 on the test set. Comparing DLSSAffinity with the existing state-of-the-art deep learning-based binding affinity prediction methods, the DLSSAffinity model outperforms other models. These results demonstrate that combining global sequence and local structure information as the input features of a deep learning model can improve the accuracy of protein-ligand binding affinity prediction.
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Affiliation(s)
- Huiwen Wang
- School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China.
| | - Haoquan Liu
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
| | - Shangbo Ning
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
| | - Chengwei Zeng
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
| | - Yunjie Zhao
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
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28
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Firouzi R, Ashouri M, Karimi‐Jafari MH. Structural insights into the substrate‐binding site of main protease for the structure‐based COVID‐19 drug discovery. Proteins 2022; 90:1090-1101. [DOI: 10.1002/prot.26318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/30/2022] [Accepted: 01/31/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Rohoullah Firouzi
- Department of Physical Chemistry Chemistry and Chemical Engineering Research Center of Iran Tehran Iran
| | - Mitra Ashouri
- Department of Physical Chemistry, School of Chemistry, College of Science University of Tehran Tehran Iran
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29
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Thai QM, Pham TNH, Hiep DM, Pham MQ, Tran PT, Nguyen TH, Ngo ST. Searching for AChE inhibitors from natural compounds by using machine learning and atomistic simulations. J Mol Graph Model 2022; 115:108230. [DOI: 10.1016/j.jmgm.2022.108230] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/14/2022]
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30
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Simončič M, Lukšič M, Druchok M. Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking. J Mol Liq 2022; 353:118759. [PMID: 35273421 PMCID: PMC8903148 DOI: 10.1016/j.molliq.2022.118759] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.
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Affiliation(s)
- Matjaž Simončič
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Večna pot 113, SI-1000 Ljubljana, Slovenia
| | - Miha Lukšič
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Večna pot 113, SI-1000 Ljubljana, Slovenia
| | - Maksym Druchok
- Institute for Condensed Matter Physics, 1 Svientsitskii Str., UA-79011 Lviv, Ukraine
- SoftServe Inc., 2d Sadova Str., UA-79021 Lviv, Ukraine
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31
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Panahi Y, Dadkhah M, Talei S, Gharari Z, Asghariazar V, Abdolmaleki A, Matin S, Molaei S. Can anti-parasitic drugs help control COVID-19? Future Virol 2022. [PMID: 35359702 PMCID: PMC8940209 DOI: 10.2217/fvl-2021-0160] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 02/28/2022] [Indexed: 01/18/2023]
Abstract
Novel COVID-19 is a public health emergency that poses a serious threat to people worldwide. Given the virus spreading so quickly, novel antiviral medications are desperately needed. Repurposing existing drugs is the first strategy. Anti-parasitic drugs were among the first to be considered as a potential treatment option for this disease. Even though many papers have discussed the efficacy of various anti-parasitic drugs in treating COVID-19 separately, so far, no single study comprehensively discussed these drugs. This study reviews some anti-parasitic recommended drugs to treat COVID-19, in terms of function and in vitro as well as clinical results. Finally, we briefly review the advanced techniques, such as artificial intelligence, that have been used to find effective drugs for the treatment of COVID-19.
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Affiliation(s)
- Yasin Panahi
- Department of Pharmacology & Toxicology, School of Pharmacy, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Masoomeh Dadkhah
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Sahand Talei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Gharari
- Department of Biotechnology, Faculty of Biological Sciences, Al-Zahra University, Tehran, Iran
| | - Vahid Asghariazar
- Deputy of Research & Technology, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Arash Abdolmaleki
- Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran.,Bio Science & Biotechnology Research center (BBRC), Sabalan University of Advanced Technologies (SUAT), Namin, Iran
| | - Somayeh Matin
- Department of Internal Medicine, Imam Khomeini Hospital, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Soheila Molaei
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran.,Zoonoses Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
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32
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Airas J, Bayas CA, N'Ait Ousidi A, Ait Itto MY, Auhmani A, Loubidi M, Esseffar M, Pollock JA, Parish CA. Investigating novel thiazolyl-indazole derivatives as scaffolds for SARS-CoV-2 M Pro inhibitors. EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY REPORTS 2022; 4:100034. [PMID: 37519829 PMCID: PMC8828376 DOI: 10.1016/j.ejmcr.2022.100034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/20/2021] [Accepted: 02/07/2022] [Indexed: 11/29/2022]
Abstract
COVID-19 is a global pandemic caused by infection with the SARS-CoV-2 virus. Remdesivir, a SARS-CoV-2 RNA polymerase inhibitor, is the only drug to have received widespread approval for treatment of COVID-19. The SARS-CoV-2 main protease enzyme (MPro), essential for viral replication and transcription, remains an active target in the search for new treatments. In this study, the ability of novel thiazolyl-indazole derivatives to inhibit MPro is evaluated. These compounds were synthesized via the heterocyclization of phenacyl bromide with (R)-carvone, (R)-pulegone and (R)-menthone thiosemicarbazones. The binding affinity and binding interactions of each compound were evaluated through Schrödinger Glide docking, AMBER molecular dynamics simulations, and MM-GBSA free energy estimation, and these results were compared with similar calculations of MPro binding various 5-mer substrates (VKLQA, VKLQS, VKLQG) and a previously identified MPro tight-binder X77. From these simulations, we can see that binding is driven by residue specific interactions such as π-stacking with His41, and S/π interactions with Met49 and Met165. The compounds were also experimentally evaluated in a MPro biochemical assay and the most potent compound containing a phenylthiazole moiety inhibited protease activity with an IC50 of 92.9 μM. This suggests that the phenylthiazole scaffold is a promising candidate for the development of future MPro inhibitors.
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Affiliation(s)
- Justin Airas
- Department of Chemistry, University of Richmond, Gottwald Center for the Sciences, Richmond, VA, 23173, USA
| | - Catherine A Bayas
- Department of Chemistry, University of Richmond, Gottwald Center for the Sciences, Richmond, VA, 23173, USA
| | - Abdellah N'Ait Ousidi
- Département de Chimie, Faculté des Sciences Semlalia, Cadi Ayyad University, BP, 2390, Marrakech, Morocco
| | - Moulay Youssef Ait Itto
- Département de Chimie, Faculté des Sciences Semlalia, Cadi Ayyad University, BP, 2390, Marrakech, Morocco
| | - Aziz Auhmani
- Département de Chimie, Faculté des Sciences Semlalia, Cadi Ayyad University, BP, 2390, Marrakech, Morocco
| | - Mohamed Loubidi
- Département de Chimie, Faculté des Sciences Semlalia, Cadi Ayyad University, BP, 2390, Marrakech, Morocco
| | - M'hamed Esseffar
- Département de Chimie, Faculté des Sciences Semlalia, Cadi Ayyad University, BP, 2390, Marrakech, Morocco
| | - Julie A Pollock
- Department of Chemistry, University of Richmond, Gottwald Center for the Sciences, Richmond, VA, 23173, USA
| | - Carol A Parish
- Department of Chemistry, University of Richmond, Gottwald Center for the Sciences, Richmond, VA, 23173, USA
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33
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Iacopetta D, Ceramella J, Catalano A, Saturnino C, Pellegrino M, Mariconda A, Longo P, Sinicropi MS, Aquaro S. COVID-19 at a Glance: An Up-to-Date Overview on Variants, Drug Design and Therapies. Viruses 2022; 14:573. [PMID: 35336980 PMCID: PMC8950852 DOI: 10.3390/v14030573] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 02/06/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the Coronavirus family which caused the worldwide pandemic of human respiratory illness coronavirus disease 2019 (COVID-19). Presumably emerging at the end of 2019, it poses a severe threat to public health and safety, with a high incidence of transmission, predominately through aerosols and/or direct contact with infected surfaces. In 2020, the search for vaccines began, leading to the obtaining of, to date, about twenty COVID-19 vaccines approved for use in at least one country. However, COVID-19 continues to spread and new genetic mutations and variants have been discovered, requiring pharmacological treatments. The most common therapies for COVID-19 are represented by antiviral and antimalarial agents, antibiotics, immunomodulators, angiotensin II receptor blockers, bradykinin B2 receptor antagonists and corticosteroids. In addition, nutraceuticals, vitamins D and C, omega-3 fatty acids and probiotics are under study. Finally, drug repositioning, which concerns the investigation of existing drugs for new therapeutic target indications, has been widely proposed in the literature for COVID-19 therapies. Considering the importance of this ongoing global public health emergency, this review aims to offer a synthetic up-to-date overview regarding diagnoses, variants and vaccines for COVID-19, with particular attention paid to the adopted treatments.
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Affiliation(s)
- Domenico Iacopetta
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavacata di Rende, Italy; (D.I.); (J.C.); (M.P.); (M.S.S.); (S.A.)
| | - Jessica Ceramella
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavacata di Rende, Italy; (D.I.); (J.C.); (M.P.); (M.S.S.); (S.A.)
| | - Alessia Catalano
- Department of Pharmacy-Drug Sciences, University of Bari “Aldo Moro”, 70126 Bari, Italy
| | - Carmela Saturnino
- Department of Science, University of Basilicata, 85100 Potenza, Italy; (C.S.); (A.M.)
| | - Michele Pellegrino
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavacata di Rende, Italy; (D.I.); (J.C.); (M.P.); (M.S.S.); (S.A.)
| | - Annaluisa Mariconda
- Department of Science, University of Basilicata, 85100 Potenza, Italy; (C.S.); (A.M.)
| | - Pasquale Longo
- Department of Chemistry and Biology, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy;
| | - Maria Stefania Sinicropi
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavacata di Rende, Italy; (D.I.); (J.C.); (M.P.); (M.S.S.); (S.A.)
| | - Stefano Aquaro
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavacata di Rende, Italy; (D.I.); (J.C.); (M.P.); (M.S.S.); (S.A.)
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34
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Enayathullah MG, Parekh Y, Banu S, Ram S, Nagaraj R, Kumar BK, Idris MM. Gramicidin S and melittin: potential anti-viral therapeutic peptides to treat SARS-CoV-2 infection. Sci Rep 2022; 12:3446. [PMID: 35236909 PMCID: PMC8891299 DOI: 10.1038/s41598-022-07341-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 02/16/2022] [Indexed: 01/02/2023] Open
Abstract
The COVID19 pandemic has led to multipronged approaches for treatment of the disease. Since de novo discovery of drugs is time consuming, repurposing of molecules is now considered as one of the alternative strategies to treat COVID19. Antibacterial peptides are being recognized as attractive candidates for repurposing to treat viral infections. In this study, we describe the anti-SARS-CoV-2 activity of the well-studied antibacterial peptides gramicidin S and melittin obtained from Bacillus brevis and bee venom respectively. The EC50 values for gramicidin S and melittin were 1.571 µg and 0.656 µg respectively based on in vitro antiviral assay. Significant decrease in the viral load as compared to the untreated group with no/very less cytotoxicity was observed. Both the peptides treated to the SARS-CoV-2 infected Vero cells showed viral clearance from 12 h onwards with a maximal viral clearance after 24 h post infection. Proteomics analysis indicated that more than 250 proteins were differentially regulated in the gramicidin S and melittin treated SARS-CoV-2 infected Vero cells against control SARS-CoV-2 infected Vero cells after 24 and 48 h post infection. The identified proteins were found to be associated in the metabolic and mRNA processing of the Vero cells post-treatment and infection. Both these peptides could be attractive candidates for repurposing to treat SARS-CoV-2 infection.
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Affiliation(s)
| | - Yash Parekh
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana, 500007, India
| | - Sarena Banu
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana, 500007, India
| | - Sushma Ram
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana, 500007, India
| | - Ramakrishnan Nagaraj
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana, 500007, India
| | - Bokara Kiran Kumar
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana, 500007, India.
| | - Mohammed M Idris
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana, 500007, India.
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35
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Nikolaienko T, Gurbych O, Druchok M. Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network. J Comput Chem 2022; 43:728-739. [PMID: 35201629 DOI: 10.1002/jcc.26831] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/04/2022] [Accepted: 02/09/2022] [Indexed: 12/12/2022]
Abstract
Drug discovery pipelines typically involve high-throughput screening of large amounts of compounds in a search of potential drugs candidates. As a chemical space of small organic molecules is huge, a "navigation" over it urges for fast and lightweight computational methods, thus promoting machine-learning approaches for processing huge pools of candidates. In this contribution, we present a graph-based deep neural network for prediction of protein-drug binding affinity and assess its predictive power under thorough testing conditions. Within the suggested approach, both protein and drug molecules are represented as graphs and passed to separate graph sub-networks, then concatenated and regressed towards a binding affinity. The neural network is trained on two binding affinity datasets-PDBbind and data imported from RCSB Protein Data Bank. In order to explore the generalization capabilities of the model we go beyond traditional random or leave-cluster-out techniques and demonstrate the need for more elaborate model performance assessment - six different strategies for test/train data partitioning (random, time- and property-arranged, protein- and ligand-clustered) with a k-fold cross-validation are engaged. Finally, we discuss the model performance in terms of a set of metrics for different split strategies and fold arrangement. Our code is available at https://github.com/SoftServeInc/affinity-by-GNN.
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Affiliation(s)
- Tymofii Nikolaienko
- SoftServe, Inc., Lviv, Ukraine.,Faculty of Physics, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
| | - Oleksandr Gurbych
- Blackthorn AI Ltd., London, UK.,Department of Artificial Intelligence Systems, Lviv Polytechnic National University, Lviv, Ukraine
| | - Maksym Druchok
- SoftServe, Inc., Lviv, Ukraine.,Institute for Condensed Matter Physics, NAS of Ukraine, Lviv, Ukraine
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36
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Abstract
The COVID-19 pandemic has prompted several institutions to offer free, dedicated websites and tools to foster research and access to urgently needed innovative solutions by facilitating the search and analysis of information within the large amount of scientific and patent literature which was published since January 2020. This situation is clearly exceptional and challenging for patent information users searching for relevant disclosures at a given date in a reliable manner. This article provides an overview of search criteria and strategies, main databases and websites, number of publications, biological sequence information and experimental data sets covering COVID-19 findings within scientific and patent literature have been disclosed between January and August 2020. The analysis of non-patent literature has been focused on the identification, date assignment, disambiguation, and access to experimental data. The analysis of patent literature has been focused on the trends found within the earliest filed and published patent documents in representative jurisdictions worldwide. Some practical advice and strategies for technical, medical, or patentability assessment of COVID-19-related innovations across different information formats and resources are proposed.
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37
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Abstract
The main protease (Mpro) plays a crucial role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication and is highly conserved, rendering it one of the most attractive therapeutic targets for SARS-CoV-2 inhibition. Currently, although two drug candidates targeting SARS-CoV-2 Mpro designed by Pfizer are under clinical trials, no SARS-CoV-2 medication is approved due to the long period of drug development. Here, we collect a comprehensive list of 817 available SARS-CoV-2 and SARS-CoV Mpro inhibitors from the literature or databases and analyze their molecular mechanisms of action. The structure-activity relationships (SARs) among each series of inhibitors are discussed. Additionally, we broadly examine available antiviral activity, ADMET (absorption, distribution, metabolism, excretion, and toxicity), and animal tests of these inhibitors. We comment on their druggability or drawbacks that prevent them from becoming drugs. This Perspective sheds light on the future development of Mpro inhibitors for SARS-CoV-2 and future coronavirus diseases.
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Affiliation(s)
- Kaifu Gao
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jiahui Chen
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jetze J Tepe
- Department of Chemistry and Pharmacology & Toxicology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Faqing Huang
- Department of Chemistry and Biochemistry, University of Southern Mississippi, Hattiesburg, Mississippi 39406, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
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Aronskyy I, Masoudi-Sobhanzadeh Y, Cappuccio A, Zaslavsky E. Advances in the computational landscape for repurposed drugs against COVID-19. Drug Discov Today 2021; 26:2800-2815. [PMID: 34339864 PMCID: PMC8323501 DOI: 10.1016/j.drudis.2021.07.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/30/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023]
Abstract
The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.
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Affiliation(s)
- Illya Aronskyy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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Tam NM, Pham DH, Hiep DM, Tran PT, Quang DT, Ngo ST. Searching and designing potential inhibitors for SARS-CoV-2 Mpro from natural sources using atomistic and deep-learning calculations. RSC Adv 2021; 11:38495-38504. [PMID: 35493244 PMCID: PMC9044063 DOI: 10.1039/d1ra06534c] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/16/2021] [Indexed: 12/15/2022] Open
Abstract
The spread of severe acute respiratory syndrome coronavirus 2 novel coronavirus (SARS-CoV-2) worldwide has caused the coronavirus disease 2019 (COVID-19) pandemic. A hundred million people were infected, resulting in several millions of death worldwide. In order to prevent viral replication, scientists have been aiming to prevent the biological activity of the SARS-CoV-2 main protease (3CL pro or Mpro). In this work, we demonstrate that using a reasonable combination of deep-learning calculations and atomistic simulations could lead to a new approach for developing SARS-CoV-2 main protease (Mpro) inhibitors. Initially, the binding affinities of the natural compounds to SARS-CoV-2 Mpro were estimated via atomistic simulations. The compound tomatine, thevetine, and tribuloside could bind to SARS-CoV-2 Mpro with nanomolar/high-nanomolar affinities. Secondly, the deep-learning (DL) calculations were performed to chemically alter the top-lead natural compounds to improve ligand-binding affinity. The obtained results were then validated by free energy calculations using atomistic simulations. The outcome of the research will probably boost COVID-19 therapy.
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Affiliation(s)
- Nguyen Minh Tam
- Computational Chemistry Research Group, Ton Duc Thang University Ho Chi Minh City Vietnam
- Faculty of Applied Sciences, Ton Duc Thang University Ho Chi Minh City Vietnam
| | - Duc-Hung Pham
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center Cincinnati Ohio 45229 USA
| | - Dinh Minh Hiep
- Department of Agriculture and Rural Development Ho Chi Minh City 71007 Vietnam
| | | | - Duong Tuan Quang
- Department of Chemistry, Hue University, Thua Thien Hue Province Hue City Vietnam
| | - Son Tung Ngo
- Faculty of Applied Sciences, Ton Duc Thang University Ho Chi Minh City Vietnam
- Laboratory of Theoretical and Computational Biophysics, Ton Duc Thang University Ho Chi Minh City Vietnam
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Gao K, Chen D, Robison AJ, Wei GW. Proteome-Informed Machine Learning Studies of Cocaine Addiction. J Phys Chem Lett 2021; 12:11122-11134. [PMID: 34752088 PMCID: PMC9357290 DOI: 10.1021/acs.jpclett.1c03133] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
No anti-cocaine addiction drugs have been approved by the Food and Drug Administration despite decades of effort. The main challenge is the intricate molecular mechanisms of cocaine addiction, involving synergistic interactions among proteins upstream and downstream of the dopamine transporter. However, it is difficult to study so many proteins with traditional experiments, highlighting the need for innovative strategies in the field. We propose a proteome-informed machine learning (ML) platform for discovering nearly optimal anti-cocaine addiction lead compounds. We analyze proteomic protein-protein interaction networks for cocaine dependence to identify 141 involved drug targets and build 32 ML models for cross-target analysis of more than 60,000 drug candidates or experimental drugs for side effects and repurposing potentials. We further predict their ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Our platform reveals that essentially all of the existing drug candidates fail in our cross-target and ADMET screenings but identifies several nearly optimal leads for further optimization.
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Affiliation(s)
- Kaifu Gao
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Dong Chen
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Alfred J Robison
- Department of Physiology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
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Sharma PP, Bansal M, Sethi A, Poonam, Pena L, Goel VK, Grishina M, Chaturvedi S, Kumar D, Rathi B. Computational methods directed towards drug repurposing for COVID-19: advantages and limitations. RSC Adv 2021; 11:36181-36198. [PMID: 35492747 PMCID: PMC9043418 DOI: 10.1039/d1ra05320e] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 10/07/2021] [Indexed: 12/19/2022] Open
Abstract
Novel coronavirus disease 2019 (COVID-19) has significantly altered the socio-economic status of countries. Although vaccines are now available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent for COVID-19, it continues to transmit and newer variants of concern have been consistently emerging world-wide. Computational strategies involving drug repurposing offer a viable opportunity to choose a medication from a rundown of affirmed drugs against distinct diseases including COVID-19. While pandemics impede the healthcare systems, drug repurposing or repositioning represents a hopeful approach in which existing drugs can be remodeled and employed to treat newer diseases. In this review, we summarize the diverse computational approaches attempted for developing drugs through drug repurposing or repositioning against COVID-19 and discuss their advantages and limitations. To this end, we have outlined studies that utilized computational techniques such as molecular docking, molecular dynamic simulation, disease-disease association, drug-drug interaction, integrated biological network, artificial intelligence, machine learning and network medicine to accelerate creation of smart and safe drugs against COVID-19.
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Affiliation(s)
- Prem Prakash Sharma
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
| | - Meenakshi Bansal
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
| | - Aaftaab Sethi
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER) Hyderabad India
| | - Poonam
- Department of Chemistry, Miranda House, University of Delhi Delhi 110007 India
| | - Lindomar Pena
- Department of Virology, Aggeu Magalhaes, Institute (IAM), Oswaldo Cruz Foundation (Fiocruz) Recife 50670-420 Pernambuco Brazil
| | - Vijay Kumar Goel
- School of Physical Sciences, Jawaharlal Nehru University New Delhi 110067 India
| | - Maria Grishina
- South Ural State University, Laboratory of Computational Modelling of Drugs Pr. Lenina 76 454080 Russia
| | - Shubhra Chaturvedi
- Division of Cyclotron and Radiopharmaceutical Sciences, Institute of Nuclear Medicine and Allied Sciences New Delhi 110054 India
| | - Dhruv Kumar
- Amity Institute of Molecular Medicine & Stem Cell Research (AIMMSCR), Amity University Uttar Pradesh Noida 201313 India
| | - Brijesh Rathi
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
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Wang L, Zhang Y, Wang D, Tong X, Liu T, Zhang S, Huang J, Zhang L, Chen L, Fan H, Clarke M. Artificial Intelligence for COVID-19: A Systematic Review. Front Med (Lausanne) 2021; 8:704256. [PMID: 34660623 PMCID: PMC8514781 DOI: 10.3389/fmed.2021.704256] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 08/09/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.
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Affiliation(s)
- Lian Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Yonggang Zhang
- Department of Periodical Press and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.,Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dongguang Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Xiang Tong
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Tao Liu
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Shijie Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Jizhen Huang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Li Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Lingmin Chen
- Department of Anesthesiology and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University and The Research Units of West China, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hong Fan
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Mike Clarke
- Northern Ireland Methodology Hub, Queen's University Belfast, Belfast, United Kingdom
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Hijikata A, Shionyu C, Nakae S, Shionyu M, Ota M, Kanaya S, Shirai T. Current status of structure-based drug repurposing against COVID-19 by targeting SARS-CoV-2 proteins. Biophys Physicobiol 2021; 18:226-240. [PMID: 34745807 PMCID: PMC8550875 DOI: 10.2142/biophysico.bppb-v18.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/30/2021] [Indexed: 01/31/2023] Open
Abstract
More than one and half years have passed, as of August 2021, since the COVID-19 caused by the novel coronavirus named SARS-CoV-2 emerged in 2019. While the recent success of vaccine developments likely reduces the severe cases, there is still a strong requirement of safety and effective therapeutic drugs for overcoming the unprecedented situation. Here we review the recent progress and the status of the drug discovery against COVID-19 with emphasizing a structure-based perspective. Structural data regarding the SARS-CoV-2 proteome has been rapidly accumulated in the Protein Data Bank, and up to 68% of the total amino acid residues encoded in the genome were covered by the structural data. Despite a global effort of in silico and in vitro screenings for drug repurposing, there is only a limited number of drugs had been successfully authorized by drug regulation organizations. Although many approved drugs and natural compounds, which exhibited antiviral activity in vitro, were considered potential drugs against COVID-19, a further multidisciplinary investigation is required for understanding the mechanisms underlying the antiviral effects of the drugs.
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Affiliation(s)
- Atsushi Hijikata
- Faculty of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama, Shiga 526-0829, Japan
| | - Clara Shionyu
- Faculty of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama, Shiga 526-0829, Japan
| | - Setsu Nakae
- Faculty of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama, Shiga 526-0829, Japan
| | - Masafumi Shionyu
- Faculty of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama, Shiga 526-0829, Japan
| | - Motonori Ota
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Nagoya, Aichi 464-8601, Japan
| | - Shigehiko Kanaya
- Computational Biology Lab. Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Ikoma, Nara 630-0192, Japan
| | - Tsuyoshi Shirai
- Faculty of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama, Shiga 526-0829, Japan
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Xu T, Zheng W, Huang R. High-throughput screening assays for SARS-CoV-2 drug development: Current status and future directions. Drug Discov Today 2021; 26:2439-2444. [PMID: 34048893 PMCID: PMC8146264 DOI: 10.1016/j.drudis.2021.05.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/16/2021] [Accepted: 05/19/2021] [Indexed: 02/08/2023]
Abstract
In response to the ongoing coronavirus disease 2019 (COVID-19) pandemic, a panel of assays has been developed and applied to screen collections of approved and investigational drugs for anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) activity in a quantitative high-throughput screening (qHTS) format. In this review, we applied data-driven approaches to evaluate the ability of each assay to identify potential anti-SARS-CoV-2 leads. Multitarget assays were found to show advantages in terms of accuracy and efficiency over single-target assays, whereas target-specific assays were more suitable for investigating compound mechanisms of action. Moreover, strict filtering with counter screens might be more detrimental than beneficial in identifying true positives. Thus, developing novel HTS assays acting simultaneously against multiple targets in the SARS-CoV-2 life cycle will benefit anti-COVID-19 drug discovery.
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45
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Mslati H, Gentile F, Perez C, Cherkasov A. Comprehensive Consensus Analysis of SARS-CoV-2 Drug Repurposing Campaigns. J Chem Inf Model 2021; 61:3771-3788. [PMID: 34313439 PMCID: PMC8340583 DOI: 10.1021/acs.jcim.1c00384] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Indexed: 01/18/2023]
Abstract
The current COVID-19 pandemic has elicited extensive repurposing efforts (both small and large scale) to rapidly identify COVID-19 treatments among approved drugs. Herein, we provide a literature review of large-scale SARS-CoV-2 antiviral drug repurposing efforts and highlight a marked lack of consistent potency reporting. This variability indicates the importance of standardizing best practices-including the use of relevant cell lines, viral isolates, and validated screening protocols. We further surveyed available biochemical and virtual screening studies against SARS-CoV-2 targets (Spike, ACE2, RdRp, PLpro, and Mpro) and discuss repurposing candidates exhibiting consistent activity across diverse, triaging assays and predictive models. Moreover, we examine repurposed drugs and their efficacy against COVID-19 and the outcomes of representative repurposed drugs in clinical trials. Finally, we propose a drug repurposing pipeline to encourage the implementation of standard methods to fast-track the discovery of candidates and to ensure reproducible results.
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Affiliation(s)
- Hazem Mslati
- Vancouver Prostate Centre, University of
British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6,
Canada
| | - Francesco Gentile
- Vancouver Prostate Centre, University of
British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6,
Canada
| | - Carl Perez
- Vancouver Prostate Centre, University of
British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6,
Canada
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of
British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6,
Canada
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Kuroda D, Tsumoto K. Microsecond molecular dynamics suggest that a non-synonymous mutation, frequently observed in patients with mild symptoms in Tokyo, alters dynamics of the SARS-CoV-2 main protease. Biophys Physicobiol 2021; 18:215-222. [PMID: 34631338 PMCID: PMC8470906 DOI: 10.2142/biophysico.bppb-v18.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 08/19/2021] [Indexed: 12/03/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), spread rapidly around the globe. The main protease encoded by SARS-CoV-2 is essential for processing of the polyproteins translated from the viral RNA genome, making this protein a potential drug target. A recently reported mutation in the protease, P108S, may be responsible for milder symptoms observed in COVID-19 patients in Tokyo. Starting from a crystal structure of the SARS-CoV-2 main protease in the dimeric form, we performed triplicate 5.0-μs molecular dynamics simulations of the wild-type and P108S mutant. Our computational results suggest a link between the mutation P108S and dynamics of the catalytic sites in the main protease: The catalytic dyad become considerably inaccessible to substrates in the P108S mutant. Our results also demonstrate the potential of molecular dynamics simulations to complement experimental techniques and other computational methods, such as protein design calculations, which predict effects of mutations based on static crystal structures. Further studies are certainly necessary to quantitively understand the relationships between the P108S mutation and physical properties of the main protease, but the results of our study will immediately inform development of new protease inhibitors.
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Affiliation(s)
- Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo 108-8639, Japan
- Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo 108-8639, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo 108-8639, Japan
- Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo 108-8639, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
- Laboratory of Medical Proteomics, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan
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Veit-Acosta M, de Azevedo Junior WF. Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery. Curr Med Chem 2021; 29:2438-2455. [PMID: 34365938 DOI: 10.2174/0929867328666210806105810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/15/2021] [Accepted: 06/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. OBJECTIVE This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. METHOD We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. RESULTS Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. CONCLUSION All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.
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Affiliation(s)
- Martina Veit-Acosta
- Western Michigan University, 1903 Western, Michigan Ave, Kalamazoo, MI 49008. United States
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Prasad K, Kumar V. Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2. CURRENT RESEARCH IN PHARMACOLOGY AND DRUG DISCOVERY 2021; 2:100042. [PMID: 34870150 PMCID: PMC8317454 DOI: 10.1016/j.crphar.2021.100042] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/22/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022] Open
Abstract
It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.
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Affiliation(s)
- Kartikay Prasad
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, UP, 201303, India
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, UP, 201303, India
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Haneczok J, Delijewski M. Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations. J Biomed Inform 2021; 119:103821. [PMID: 34052441 PMCID: PMC8159673 DOI: 10.1016/j.jbi.2021.103821] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 04/18/2021] [Accepted: 05/16/2021] [Indexed: 12/29/2022]
Abstract
AIM Rapidly developing AI and machine learning (ML) technologies can expedite therapeutic development and in the time of current pandemic their merits are particularly in focus. The purpose of this study was to explore various ML approaches for molecular property prediction and illustrate their utility for identifying potential SARS-CoV-2 3CLpro inhibitors. MATERIALS AND METHODS We perform a series of drug discovery screenings based on supervised ML models operating in different ways on molecular representations, encompassing shallow learning methods based on fixed molecular fingerprints, Graph Convolutional Neural Network (Graph-CNN) with its self-learned molecular representations, as well as ML methods based on combining fixed and Graph-CNN learned representations. RESULTS Results of our ML models are compared both with respect to the aggregated predictive performance in terms of ROC-AUC based on the scaffold splits, as well as on the granular level of individual predictions, corresponding to the top ranked repurposing candidates. This comparison reveals both certain characteristic homogeneity regarding chemical and pharmacological classification, with a prevalence of sulfonamides and anticancer drugs, as well as identifies novel groups of potential drug candidates against COVID-19. CONCLUSIONS A series of ML approaches for molecular property prediction enables drug discovery screenings, illustrating the utility for COVID-19. We show that the obtained results correspond well with the already published research on COVID-19 treatment, as well as provide novel insights on potential antiviral characteristics inferred from in vitro data.
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Affiliation(s)
| | - Marcin Delijewski
- Department of Pharmacology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland.
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50
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Druchok M, Yarish D, Garkot S, Nikolaienko T, Gurbych O. Ensembling machine learning models to boost molecular affinity prediction. Comput Biol Chem 2021; 93:107529. [PMID: 34192653 DOI: 10.1016/j.compbiolchem.2021.107529] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/01/2021] [Accepted: 06/08/2021] [Indexed: 02/01/2023]
Abstract
This study unites six popular machine learning approaches to enhance the prediction of a molecular binding affinity between receptors (large protein molecules) and ligands (small organic molecules). Here we examine a scheme where affinity of ligands is predicted against a single receptor - human thrombin, thus, the models consider ligand features only. However, the suggested approach can be repurposed for other receptors. The methods include Support Vector Machine, Random Forest, CatBoost, feed-forward neural network, graph neural network, and Bidirectional Encoder Representations from Transformers. The first five methods use input features based on physico-chemical properties of molecules, while the last one is based on textual molecular representations. All approaches do not rely on atomic spatial coordinates, avoiding a potential bias from known structures, and are capable of generalizing for compounds with unknown conformations. Within each of the methods, we have trained two models that solve classification and regression tasks. Then, all models are grouped into a pipeline of two subsequent ensembles. The first ensemble aggregates six classification models which vote whether a ligand binds to a receptor or not. If a ligand is classified as active (i.e., binds), the second ensemble predicts its binding affinity in terms of the inhibition constant Ki.
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Affiliation(s)
- Maksym Druchok
- SoftServe, Inc., 2d Sadova Str., 79021 Lviv, Ukraine; Institute for Condensed Matter Physics, NAS of Ukraine, 1 Svientsitskii Str., 79011 Lviv, Ukraine.
| | | | - Sofiya Garkot
- SoftServe, Inc., 2d Sadova Str., 79021 Lviv, Ukraine; Ukrainian Catholic University, 17 Svientsitskii Str., 79011 Lviv, Ukraine
| | - Tymofii Nikolaienko
- SoftServe, Inc., 2d Sadova Str., 79021 Lviv, Ukraine; Taras Shevchenko National University of Kyiv, 64/13, Volodymyrska Str., 01601 Kyiv, Ukraine
| | - Oleksandr Gurbych
- SoftServe, Inc., 2d Sadova Str., 79021 Lviv, Ukraine; Lviv Polytechnic National University, 5 Kniazia Romana Str., 79005 Lviv, Ukraine
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