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Shi W, Yang H, Xie L, Yin XX, Zhang Y. A review of machine learning-based methods for predicting drug-target interactions. Health Inf Sci Syst 2024; 12:30. [PMID: 38617016 PMCID: PMC11014838 DOI: 10.1007/s13755-024-00287-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/04/2024] [Indexed: 04/16/2024] Open
Abstract
The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.
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Affiliation(s)
- Wen Shi
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Linhai Xie
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing, 102206 China
| | - Xiao-Xia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000 China
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2
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Yin Y, Hu H, Yang J, Ye C, Goh WWB, Kong AWK, Wu J. OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs. Bioinformatics 2024; 40:btae365. [PMID: 38889277 PMCID: PMC11208724 DOI: 10.1093/bioinformatics/btae365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 05/14/2024] [Accepted: 06/14/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. RESULTS We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r2) on 27/33 datasets, with an average improvement of 7.2%-22.9% against the state-of-the-art bioactivity prediction methods. AVAILABILITY AND IMPLEMENTATION The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC.
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Affiliation(s)
- Yueming Yin
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
- College of Computing and Data Science, Nanyang Technological University, 639798, Singapore
| | - Haifeng Hu
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Jitao Yang
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Chun Ye
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, 637551, Singapore
- School of Biological Sciences, Nanyang Technological University, 637551, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, 637551, Singapore
- Center for AI in Medicine, Nanyang Technological University, 639798, Singapore
- Division of Neurology, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London W12 0NN, U.K
| | - Adams Wai-Kin Kong
- College of Computing and Data Science, Nanyang Technological University, 639798, Singapore
| | - Jiansheng Wu
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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3
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Jangid H, Garg S, Kashyap P, Karnwal A, Shidiki A, Kumar G. Bioprospecting of Aspergillus sp. as a promising repository for anti-cancer agents: a comprehensive bibliometric investigation. Front Microbiol 2024; 15:1379602. [PMID: 38812679 PMCID: PMC11133633 DOI: 10.3389/fmicb.2024.1379602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024] Open
Abstract
Cancer remains a significant global health challenge, claiming nearly 10 million lives in 2020 according to the World Health Organization. In the quest for novel treatments, fungi, especially Aspergillus species, have emerged as a valuable source of bioactive compounds with promising anticancer properties. This study conducts a comprehensive bibliometric analysis to map the research landscape of Aspergillus in oncology, examining publications from 1982 to the present. We observed a marked increase in research activity starting in 2000, with a notable peak from 2005 onwards. The analysis identifies key contributors, including Mohamed GG, who has authored 15 papers with 322 citations, and El-Sayed Asa, with 14 papers and 264 citations. Leading countries in this research field include India, Egypt, and China, with King Saud University and Cairo University as the leading institutions. Prominent research themes identified are "endophyte," "green synthesis," "antimicrobial," "anti-cancer," and "biological activities," indicating a shift towards environmentally sustainable drug development. Our findings highlight the considerable potential of Aspergillus for developing new anticancer therapies and underscore the necessity for further research to harness these natural compounds for clinical use.
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Affiliation(s)
- Himanshu Jangid
- School of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, India
| | - Sonu Garg
- Department of Biotechnology, Mahatma Jyoti Rao Phoole University, Jaipur, Rajasthan, India
| | - Piyush Kashyap
- School of Agriculture, Lovely Professional University, Jalandhar, Punjab, India
| | - Arun Karnwal
- School of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, India
| | - Amrullah Shidiki
- Department of Microbiology, National Medical College & Teaching Hospital, Birgunj, Nepal
| | - Gaurav Kumar
- School of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, India
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Sulimov AV, Ilin IS, Tashchilova AS, Kondakova OA, Kutov DC, Sulimov VB. Docking and other computing tools in drug design against SARS-CoV-2. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:91-136. [PMID: 38353209 DOI: 10.1080/1062936x.2024.2306336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/10/2024] [Indexed: 02/16/2024]
Abstract
The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.
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Affiliation(s)
- A V Sulimov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - I S Ilin
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - A S Tashchilova
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - O A Kondakova
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - D C Kutov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - V B Sulimov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
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5
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Bharadwaj A, Kaur R, Gupta S. Emerging Treatment Approaches for COVID-19 Infection: A Critical Review. Curr Mol Med 2024; 24:435-448. [PMID: 37070448 DOI: 10.2174/1566524023666230417112543] [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: 09/02/2022] [Revised: 02/04/2023] [Accepted: 02/07/2023] [Indexed: 04/19/2023]
Abstract
In the present scenario, the SARS-CoV-2 virus has imposed enormous damage on human survival and the global financial system. It has been estimated that around 111 million people all around the world have been infected, and about 2.47 million people died due to this pandemic. The major symptoms were sneezing, coughing, cold, difficulty breathing, pneumonia, and multi-organ failure associated 1with SARS-CoV-2. Currently, two key problems, namely insufficient attempts to develop drugs against SARSCoV-2 and the lack of any biological regulating process, are mostly responsible for the havoc caused by this virus. Henceforth, developing a few novel drugs is urgently required to cure this pandemic. It has been noticed that the pathogenesis of COVID-19 is caused by two main events: infection and immune deficiency, that occur during the pathological process. Antiviral medication can treat both the virus and the host cells. Therefore, in the present review, the major approaches for the treatment have been divided into "target virus" and "target host" groups. These two mechanisms primarily rely on drug repositioning, novel approaches, and possible targets. Initially, we discussed the traditional drugs per the physicians' recommendations. Moreover, such therapeutics have no potential to fight against COVID-19. After that, detailed investigation and analysis were conducted to find some novel vaccines and monoclonal antibodies and conduct a few clinical trials to check their effectiveness against SARSCoV- 2 and mutant strains. Additionally, this study presents the most successful methods for its treatment, including combinatorial therapy. Nanotechnology was studied to build efficient nanocarriers to overcome the traditional constraints of antiviral and biological therapies.
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Affiliation(s)
- Alok Bharadwaj
- Department of Biotechnology, GLA University, Mathura, 281406, UP, India
| | - Rasanpreet Kaur
- Department of Biotechnology, GLA University, Mathura, 281406, UP, India
| | - Saurabh Gupta
- Department of Biotechnology, GLA University, Mathura, 281406, UP, India
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6
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Singh MP, Singh N, Mishra D, Ehsan S, Chaturvedi VK, Chaudhary A, Singh V, Vamanu E. Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review. Curr Pharm Des 2023; 29:2601-2617. [PMID: 37916490 DOI: 10.2174/0113816128259795231023193419] [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: 05/18/2023] [Accepted: 09/21/2023] [Indexed: 11/03/2023]
Abstract
The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.
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Affiliation(s)
- Mohan P Singh
- Centre of Biotechnology, University of Allahabad, Prayagraj 211002, India
| | - Nidhi Singh
- Centre of Bioinformatics, University of Allahabad, Prayagraj 211002, India
| | - Divya Mishra
- Centre of Bioinformatics, University of Allahabad, Prayagraj 211002, India
| | - Saba Ehsan
- Centre of Biotechnology, University of Allahabad, Prayagraj 211002, India
| | - Vivek K Chaturvedi
- Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Anupriya Chaudhary
- Centre of Biotechnology, University of Allahabad, Prayagraj 211002, India
| | - Veer Singh
- Department of Biochemistry, Rajendra Memorial Research Institute of Medical Sciences, Patna 800007, India
| | - Emanuel Vamanu
- Faculty of Biotechnology, University of Agricultural Sciences and Veterinary Medicine of Bucharest, Bucharest 011464, Romania
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7
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Wang L, Yu Z, Wang S, Guo Z, Sun Q, Lai L. Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen. Eur J Med Chem 2022; 244:114803. [PMID: 36209629 PMCID: PMC9528019 DOI: 10.1016/j.ejmech.2022.114803] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/20/2022] [Accepted: 09/26/2022] [Indexed: 11/28/2022]
Abstract
SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC50 values less than 12 μM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC50 of 1.4 μM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors.
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Affiliation(s)
- Liying Wang
- BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China
| | - Zhongtian Yu
- BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China
| | - Shiwei Wang
- BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China
| | - Zheng Guo
- BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China
| | - Qi Sun
- BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China,Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, 100871, PR China,Corresponding author. BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China
| | - Luhua Lai
- BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China,Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, PR China,Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, 100871, PR China,Corresponding author. BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China
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8
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Tangmanussukum P, Kawichai T, Suratanee A, Plaimas K. Heterogeneous network propagation with forward similarity integration to enhance drug-target association prediction. PeerJ Comput Sci 2022; 8:e1124. [PMID: 36262151 PMCID: PMC9575853 DOI: 10.7717/peerj-cs.1124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Identification of drug-target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug-drug and nine target-target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug-disease associations, and the cosine scores of drug-drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.
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Affiliation(s)
- Piyanut Tangmanussukum
- Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Thitipong Kawichai
- Department of Mathematics and Computer Science, Academic Division, Chulachomklao Royal Military Academy, Nakhon Nayok, Thailand
| | - Apichat Suratanee
- Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
- Intelligent and Nonlinear Dynamics Innovations Research Center, Science and Technology Research Institute, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
| | - Kitiporn Plaimas
- Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
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9
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Swain SS, Singh SR, Sahoo A, Panda PK, Hussain T, Pati S. Integrated bioinformatics-cheminformatics approach toward locating pseudo-potential antiviral marine alkaloids against SARS-CoV-2-Mpro. Proteins 2022; 90:1617-1633. [PMID: 35384056 PMCID: PMC9111047 DOI: 10.1002/prot.26341] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 03/17/2022] [Accepted: 03/30/2022] [Indexed: 12/17/2022]
Abstract
The emergence of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) with the most contagious variants, alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2), and Omicron (B.1.1.529) has continuously added a higher number of morbidity and mortality, globally. The present integrated bioinformatics-cheminformatics approach was employed to locate potent antiviral marine alkaloids that could be used against SARS-CoV-2. Initially, 57 antiviral marine alkaloids and two repurposing drugs were selected from an extensive literature review. Then, the putative target enzyme SARS-CoV-2 main protease (SARS-CoV-2-Mpro) was retrieved from the protein data bank and carried out a virtual screening-cum-molecular docking study with all candidates using PyRx 0.8 and AutoDock 4.2 software. Further, the molecular dynamics (MD) simulation of the two most potential alkaloids and a drug docking complex at 100 ns (with two ligand topology files from PRODRG and ATB server, separately), the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) free energy, and contributions of entropy were investigated. Then, the physicochemical-toxicity-pharmacokinetics-drug-likeness profiles, the frontier molecular orbitals energies (highest occupied molecular orbital, lowest unoccupied molecular orbital, and ΔE), and structural-activity relationship were assessed and analyzed. Based on binding energy, 8-hydroxymanzamine (-10.5 kcal/mol) and manzamine A (-10.1 kcal/mol) from all alkaloids with darunavir (-7.9 kcal/mol) and lopinavir (-7.4 kcal/mol) against SARS-CoV-2-Mpro were recorded. The MD simulation (RMSD, RMSF, Rg, H-bond, MM/PBSA binding energy) illustrated that the 8-hydroxymanzamine exhibits a static thermodynamic feature than the other two complexes. The predicted physicochemical, toxicity, pharmacokinetics, and drug-likeness profiles also revealed that the 8-hydroxymanzamine could be used as a potential lead candidate individually and/or synergistically with darunavir or lopinavir to combat SARS-CoV-2 infection after some pharmacological validation.
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Affiliation(s)
- Shasank S Swain
- Division of Microbiology and NCDs, ICMR-Regional Medical Research Centre, Bhubaneswar, Odisha, India
| | - Satya R Singh
- Department of Bioinformatics, Pondicherry University, Puducherry, India
| | - Alaka Sahoo
- Department of Skin & VD, Institute of Medical Sciences & SUM Hospital, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
| | - Pritam Kumar Panda
- Condensed Matter Theory Group, Materials Theory Division, Department of Physics and Astronomy, Uppsala University, Uppsala, Sweden
| | - Tahziba Hussain
- Division of Microbiology and NCDs, ICMR-Regional Medical Research Centre, Bhubaneswar, Odisha, India
| | - Sanghamitra Pati
- Division of Public Health and Research, ICMR-Regional Medical Research Centre, Bhubaneswar, Odisha, India
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10
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Liu XH, Cheng T, Liu BY, Chi J, Shu T, Wang T. Structures of the SARS-CoV-2 spike glycoprotein and applications for novel drug development. Front Pharmacol 2022; 13:955648. [PMID: 36016554 PMCID: PMC9395726 DOI: 10.3389/fphar.2022.955648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/13/2022] [Indexed: 12/14/2022] Open
Abstract
COVID-19 caused by SARS-CoV-2 has raised a health crisis worldwide. The high morbidity and mortality associated with COVID-19 and the lack of effective drugs or vaccines for SARS-CoV-2 emphasize the urgent need for standard treatment and prophylaxis of COVID-19. The receptor-binding domain (RBD) of the glycosylated spike protein (S protein) is capable of binding to human angiotensin-converting enzyme 2 (hACE2) and initiating membrane fusion and virus entry. Hence, it is rational to inhibit the RBD activity of the S protein by blocking the RBD interaction with hACE2, which makes the glycosylated S protein a potential target for designing and developing antiviral agents. In this study, the molecular features of the S protein of SARS-CoV-2 are highlighted, such as the structures, functions, and interactions of the S protein and ACE2. Additionally, computational tools developed for the treatment of COVID-19 are provided, for example, algorithms, databases, and relevant programs. Finally, recent advances in the novel development of antivirals against the S protein are summarized, including screening of natural products, drug repurposing and rational design. This study is expected to provide novel insights for the efficient discovery of promising drug candidates against the S protein and contribute to the development of broad-spectrum anti-coronavirus drugs to fight against SARS-CoV-2.
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11
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Nath M, Debnath P. Therapeutic role of traditionally used Indian medicinal plants and spices in combating COVID-19 pandemic situation. J Biomol Struct Dyn 2022:1-20. [PMID: 35773779 DOI: 10.1080/07391102.2022.2093793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The coronavirus disease (COVID-19) caused by SARS-CoV-2 is a big challenge and burning issue to the scientific community and doctors worldwide. Globally, COVID-19 has created a health disaster and adversely affects the economic growth. Although some vaccines have already emerged, no therapeutic medication has yet been approved by FDA for the treatment of COVID-19 patients. Traditionally, we have been using different medicinal plants like neem, tulsi, tea, and many spices like garlic, ginger, turmeric, black seed, onion, etc. for the treatment of flu-like diseases. In this paper, we are highlighting the recent research progress in the identification of natural products from the Indian medicinal plants and spices that have potential inhibition properties against SARS-CoV-2. This study will provide an initiative to stimulate further research by providing useful guidance to the medicinal chemists for designing new protease inhibitors effective against SARS-CoV-2 in future.
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Affiliation(s)
- Moumita Nath
- Department of Botany, Tripura University, Suryamaninagar, Tripura, India
| | - Pradip Debnath
- Department of Chemistry, Maharaja Bir Bikram College, Agartala, Tripura, India
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12
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Yang Y, Zhou D, Zhang X, Shi Y, Han J, Zhou L, Wu L, Ma M, Li J, Peng S, Xu Z, Zhu W. D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19. Brief Bioinform 2022; 23:6571526. [PMID: 35443040 PMCID: PMC9310271 DOI: 10.1093/bib/bbac147] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/13/2022] [Accepted: 03/31/2022] [Indexed: 11/25/2022] Open
Abstract
Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.
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Affiliation(s)
- Yanqing Yang
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Deshan Zhou
- Department of Computer Science, Hunan University, Changsha, 410082, China
| | - Xinben Zhang
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Yulong Shi
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Jiaxin Han
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, 210046, China
| | - Liping Zhou
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Leyun Wu
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Minfei Ma
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Jintian Li
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Shaoliang Peng
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Weiliang Zhu
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
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13
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Prastiwi R, Elya B, Hanafi M, Sauriasari R, Desmiaty Y, Dewanti E, Herowati R. The chemical constituents of Sterculia comosa (wall) Roxb woods for arginase inhibitory, antioxidant activity, and molecular docking against SARS CoV-2 protein. Heliyon 2022; 8:e08798. [PMID: 35079656 PMCID: PMC8769564 DOI: 10.1016/j.heliyon.2022.e08798] [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: 12/08/2020] [Revised: 02/16/2021] [Accepted: 01/13/2022] [Indexed: 10/27/2022] Open
Abstract
Flavonoids and phenols have an arginase inhibitory and antioxidant activity. The Sterculia genus has phenols and flavonoids content. This study aimed to investigate the arginase inhibitory and antioxidant activity of the chemical constituent of Sterculia comosa (wall) Roxb and also their binding affinities to arginase. The most active extract was methanol extract. This active extract was determined for its arginase inhibitory and antioxidant activity, determined the total phenols and total flavonoids, and identified chemical compound. The methanol extract has IC50 2.787 μg/ml for arginase inhibitory activity and IC50 4,199 μg/ml for DPPH scavenging activity. The total phenols 723.61 mg GAE/gr, total flavonoids content 28.96 mg QE/gr extract. The chemical constituent: KC4.4.6 ((-)-2-(E)-caffeoyl-D-glyceric acid) and KC4.4.5.1 (trans-isoferulic acid) have an arginase inhibitory activity KC4.4.6: 98,03 μg/ml and KC4.4.5.1: 292,58 μg/ml. Antioxidant activity with DPPH methods KC4.4.6: 48,77 μg/ml and KC4.4.5.1: 88,08 μg/ml. Antioxidant by FRAP methods KC4.4.6: 16,4 FeEAC mol/g and KC4.4.5.1: 15,79 FeEAC mol/g. The isolate trans-isoferulic acid predicted has good interaction to arginase. Isolate KC4.4.6. Predicted has good interaction to PLPro of SARS CoV-2 PLpro. However, both isolates did not show good interaction to 3CLPro, nsp12, and Spike protein of SARS CoV-2.
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Affiliation(s)
- Rini Prastiwi
- Faculty of Pharmacy and Science, Universitas Muhammadiyah Prof. Dr. Hamka, 1340, Jakarta, Indonesia
| | - Berna Elya
- Faculty of Pharmacy Universitas Indonesia, Depok, 16424, West Java, Indonesia
| | - Muhammad Hanafi
- Research Centre for Chemistry - National Research and Innovation Agency (BRIN), Indonesia.,Faculty of Pharmacy Universitas Pancasila, Jakarta, West Java, Indonesia
| | - Rani Sauriasari
- Faculty of Pharmacy Universitas Indonesia, Depok, 16424, West Java, Indonesia
| | - Yesi Desmiaty
- Faculty of Pharmacy Universitas Pancasila, Jakarta, West Java, Indonesia
| | - Ema Dewanti
- Faculty of Pharmacy and Science, Universitas Muhammadiyah Prof. Dr. Hamka, 1340, Jakarta, Indonesia
| | - Rina Herowati
- Faculty of Pharmacy Universitas Setia Budi, Surakarta, Central of Java, Indonesia
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14
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Drug-Target Interaction Prediction Based on Multisource Information Weighted Fusion. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:6044256. [PMID: 34908912 PMCID: PMC8635946 DOI: 10.1155/2021/6044256] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/22/2021] [Indexed: 01/08/2023]
Abstract
Recently, in most existing studies, it is assumed that there are no interaction relationships between drugs and targets with unknown interactions. However, unknown interactions mean the relationships between drugs and targets have just not been confirmed. In this paper, samples for which the relationship between drugs and targets has not been determined are considered unlabeled. A weighted fusion method of multisource information is proposed to screen drug-target interactions. Firstly, some drug-target pairs which may have interactions are selected. Secondly, the selected drug-target pairs are added to the positive samples, which are regarded as known to have interaction relationships, and the original interaction relationship matrix is revised. Finally, the revised datasets are used to predict the interaction derived from the bipartite local model with neighbor-based interaction profile inferring (BLM-NII). Experiments demonstrate that the proposed method has greatly improved specificity, sensitivity, precision, and accuracy compared with the BLM-NII method. In addition, compared with several state-of-the-art methods, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) of the proposed method are excellent.
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15
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Ma L, Li H, Lan J, Hao X, Liu H, Wang X, Huang Y. Comprehensive analyses of bioinformatics applications in the fight against COVID-19 pandemic. Comput Biol Chem 2021; 95:107599. [PMID: 34773807 PMCID: PMC8560182 DOI: 10.1016/j.compbiolchem.2021.107599] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/24/2021] [Accepted: 10/29/2021] [Indexed: 02/07/2023]
Abstract
Novel coronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which can be transmitted from person to person. As of September 21, 2021, over 228 million cases were diagnosed as COVID-19 infection in more than 200 countries and regions worldwide. The death toll is more than 4.69 million and the mortality rate has reached about 2.05% as it has gradually become a global plague, and the numbers are growing. Therefore, it is important to gain a deeper understanding of the genome and protein characteristics, clinical diagnostics, pathogenic mechanisms, and the development of antiviral drugs and vaccines against the novel coronavirus to deal with the COVID-19 pandemic. The traditional biology technologies are limited for COVID-19-related studies to understand the pandemic happening. Bioinformatics is the application of computational methods and analytical tools in the field of biological research which has obvious advantages in predicting the structure, product, function, and evolution of unknown genes and proteins, and in screening drugs and vaccines from a large amount of sequence information. Here, we comprehensively summarized several of the most important methods and applications relating to COVID-19 based on currently available reports of bioinformatics technologies, focusing on future research for overcoming the virus pandemic. Based on the next-generation sequencing (NGS) and third-generation sequencing (TGS) technology, not only virus can be detected, but also high quality SARS-CoV-2 genome could be obtained quickly. The emergence of data of genome sequences, variants, haplotypes of SARS-CoV-2 help us to understand genome and protein structure, variant calling, mutation, and other biological characteristics. After sequencing alignment and phylogenetic analysis, the bat may be the natural host of the novel coronavirus. Single-cell RNA sequencing provide abundant resource for discovering the mechanism of immune response induced by COVID-19. As an entry receptor, angiotensin-converting enzyme 2 (ACE2) can be used as a potential drug target to treat COVID-19. Molecular dynamics simulation, molecular docking and artificial intelligence (AI) technology of bioinformatics methods based on drug databases for SARS-CoV-2 can accelerate the development of drugs. Meanwhile, computational approaches are helpful to identify suitable vaccines to prevent COVID-19 infection through reverse vaccinology, Immunoinformatics and structural vaccinology.
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Affiliation(s)
- Lifei Ma
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China,College of Lab Medicine, Hebei North University, Zhangjiakou, Hebei 075000, China,Corresponding author at: State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China
| | - Huiyang Li
- Tianjin Key Laboratory of Biomaterial Research, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin 300192, China
| | - Jinping Lan
- College of Lab Medicine, Hebei North University, Zhangjiakou, Hebei 075000, China
| | - Xiuqing Hao
- The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei 075000, China
| | - Huiying Liu
- The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei 075000, China
| | - Xiaoman Wang
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China,Corresponding authors
| | - Yong Huang
- College of Lab Medicine, Hebei North University, Zhangjiakou, Hebei 075000, China,Corresponding authors
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16
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Harigua-Souiai E, Heinhane MM, Abdelkrim YZ, Souiai O, Abdeljaoued-Tej I, Guizani I. Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules. Front Genet 2021; 12:744170. [PMID: 34912370 PMCID: PMC8667578 DOI: 10.3389/fgene.2021.744170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/30/2021] [Indexed: 12/26/2022] Open
Abstract
Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.
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Affiliation(s)
- Emna Harigua-Souiai
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Mohamed Mahmoud Heinhane
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Yosser Zina Abdelkrim
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Oussama Souiai
- Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS)-LR20IPT09, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Ines Abdeljaoued-Tej
- Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS)-LR20IPT09, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Engineering School of Statistics and Information Analysis, University of Carthage, Ariana, Tunisia
| | - Ikram Guizani
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
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17
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Yin Y, Hu H, Yang Z, Xu H, Wu J. RealVS: Toward Enhancing the Precision of Top Hits in Ligand-Based Virtual Screening of Drug Leads from Large Compound Databases. J Chem Inf Model 2021; 61:4924-4939. [PMID: 34619030 DOI: 10.1021/acs.jcim.1c01021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Accurate modeling of compound bioactivities is essential for the virtual screening of drug leads. In real-world scenarios, pharmacists tend to choose from the top-k hit compounds ranked by predicted bioactivities from a large database with interest to continue wet experiments for drug discovery. Significant improvement of the precision of the top hits in ligand-based virtual screening of drug leads is more valuable than conventional schemes for accurately predicting the bioactivities of all compounds from a large database. Here, we proposed a new method, RealVS, to significantly improve the top hits' precision and learn interpretable key substructures associated with compound bioactivities. The features of RealVS involve the following points. (1) Abundant transferable information from the source domain was introduced for alleviating the insufficiency of inactive ligands associated with drug targets. (2) The adversarial domain alignment was adopted to fit the distribution of generated features of compounds from the training data set and that from the screening database for greater model generalization ability. (3) A novel objective function was proposed to simultaneously optimize the classification loss, regression loss, and adversarial loss, where most inactive ligands tend to be screened out before activity regression prediction. (4) Graph attention networks were adopted for learning key substructures associated with ligand bioactivities for better model interpretability. The results on a large number of benchmark data sets show that our method has significantly improved the precision of top hits under various k values in ligand-based virtual screening of drug leads from large compound databases, which is of great value in real-world scenarios. The web server of RealVS is freely available at noveldelta.com/RealVS for academic purposes, where virtual screening of hits from large compound databases is accessible.
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Affiliation(s)
- Yueming Yin
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Haifeng Hu
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Zhen Yang
- National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Huajian Xu
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Jiansheng Wu
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
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18
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Liu Q, Wan J, Wang G. A survey on computational methods in discovering protein inhibitors of SARS-CoV-2. Brief Bioinform 2021; 23:6384382. [PMID: 34623382 PMCID: PMC8524468 DOI: 10.1093/bib/bbab416] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/25/2021] [Accepted: 09/12/2021] [Indexed: 12/13/2022] Open
Abstract
The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [
1–
5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.
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Affiliation(s)
- Qiaoming Liu
- Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang 150001, China
| | - Jun Wan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.,Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Guohua Wang
- Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang 150001, China.,Information and Computer Engineering College, Northeast Forestry University, Harbin, Heilongjiang 150001, China
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19
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Pinto GP, Hendrikse NM, Stourac J, Damborsky J, Bednar D. Virtual screening of potential anticancer drugs based on microbial products. Semin Cancer Biol 2021; 86:1207-1217. [PMID: 34298109 DOI: 10.1016/j.semcancer.2021.07.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 07/14/2021] [Accepted: 07/18/2021] [Indexed: 01/20/2023]
Abstract
The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNPOnco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.
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Affiliation(s)
- Gaspar P Pinto
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, Brno, 625 00, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, Brno, 656 91, Czech Republic
| | - Natalie M Hendrikse
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, Brno, 625 00, Czech Republic
| | - Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, Brno, 625 00, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, Brno, 656 91, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, Brno, 625 00, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, Brno, 656 91, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, Brno, 625 00, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, Brno, 656 91, Czech Republic.
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20
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Anand U, Jakhmola S, Indari O, Jha HC, Chen ZS, Tripathi V, Pérez de la Lastra JM. Potential Therapeutic Targets and Vaccine Development for SARS-CoV-2/COVID-19 Pandemic Management: A Review on the Recent Update. Front Immunol 2021; 12:658519. [PMID: 34276652 PMCID: PMC8278575 DOI: 10.3389/fimmu.2021.658519] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 06/07/2021] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a highly pathogenic novel virus that has caused a massive pandemic called coronavirus disease 2019 (COVID-19) worldwide. Wuhan, a city in China became the epicenter of the outbreak of COVID-19 in December 2019. The disease was declared a pandemic globally by the World Health Organization (WHO) on 11 March 2020. SARS-CoV-2 is a beta CoV of the Coronaviridae family which usually causes respiratory symptoms that resemble common cold. Multiple countries have experienced multiple waves of the disease and scientific experts are consistently working to find answers to several unresolved questions, with the aim to find the most suitable ways to contain the virus. Furthermore, potential therapeutic strategies and vaccine development for COVID-19 management are also considered. Currently, substantial efforts have been made to develop successful and safe treatments and SARS-CoV-2 vaccines. Some vaccines, such as inactivated vaccines, nucleic acid-based, and vector-based vaccines, have entered phase 3 clinical trials. Additionally, diverse small molecule drugs, peptides and antibodies are being developed to treat COVID-19. We present here an overview of the virus interaction with the host and environment and anti-CoV therapeutic strategies; including vaccines and other methodologies, designed for prophylaxis and treatment of SARS-CoV-2 infection with the hope that this integrative analysis could help develop novel therapeutic approaches against COVID-19.
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Affiliation(s)
- Uttpal Anand
- Department of Life Sciences, National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Shweta Jakhmola
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, India
| | - Omkar Indari
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, India
| | - Hem Chandra Jha
- Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, India
| | - Zhe-Sheng Chen
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John’s University, Queens, NY, United States
| | - Vijay Tripathi
- Department of Molecular and Cellular Engineering, Jacob Institute of Biotechnology and Bioengineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, India
| | - José M. Pérez de la Lastra
- Instituto de Productos Naturales y Agrobiología (IPNA), Consejo Superior de Investigaciones científicas (CSIS), Santa Cruz de Tenerife, Spain
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21
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Wang S, Sun Q, Xu Y, Pei J, Lai L. A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2. Brief Bioinform 2021; 22:6291517. [PMID: 34081143 PMCID: PMC8195169 DOI: 10.1093/bib/bbab211] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 04/29/2021] [Accepted: 05/11/2021] [Indexed: 12/27/2022] Open
Abstract
The COVID-19 pandemic calls for rapid development of effective treatments. Although various drug repurpose approaches have been used to screen the FDA-approved drugs and drug candidates in clinical phases against SARS-CoV-2, the coronavirus that causes this disease, no magic bullets have been found until now. In this study, we used directed message passing neural network to first build a broad-spectrum anti-beta-coronavirus compound prediction model, which gave satisfactory predictions on newly reported active compounds against SARS-CoV-2. Then, we applied transfer learning to fine-tune the model with the recently reported anti-SARS-CoV-2 compounds and derived a SARS-CoV-2 specific prediction model COVIDVS-3. We used COVIDVS-3 to screen a large compound library with 4.9 million drug-like molecules from ZINC15 database and recommended a list of potential anti-SARS-CoV-2 compounds for further experimental testing. As a proof-of-concept, we experimentally tested seven high-scored compounds that also demonstrated good binding strength in docking studies against the 3C-like protease of SARS-CoV-2 and found one novel compound that can inhibit the enzyme. Our model is highly efficient and can be used to screen large compound databases with millions or more compounds to accelerate the drug discovery process for the treatment of COVID-19.
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Affiliation(s)
- Shiwei Wang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, PR China
| | - Qi Sun
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, PR China
| | - Youjun Xu
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, PR China
| | - Jianfeng Pei
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, PR China
| | - Luhua Lai
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, PR China
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22
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Haroon M, Akhtar T, Khalid M, Ali S, Zahra S, Ul Haq I, Alhujaily M, C H de B Dias M, Cristina Lima Leite A, Muhammad S. Synthesis, antioxidant, antimicrobial and antiviral docking studies of ethyl 2-(2-(arylidene)hydrazinyl)thiazole-4-carboxylates. ACTA ACUST UNITED AC 2021; 76:467-480. [PMID: 33901389 DOI: 10.1515/znc-2021-0042] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 04/09/2021] [Indexed: 11/15/2022]
Abstract
A series of ethyl 2-(2-(arylidene)hydrazinyl)thiazole-4-carboxylates (2a-r) was synthesized in two steps from thiosemicarbazones (1a-r), which were cyclized with ethyl bromopyruvate to ethyl 2-(2-(arylidene)hydrazinyl)thiazole-4-carboxylates (2a-r). The structures of compounds (2a-r) were established by FT-IR, 1H- and 13C-NMR. The structure of compound 2a was confirmed by HRMS. The compounds (2a-r) were then evaluated for their antimicrobial and antioxidant assays. The antioxidant studies revealed, ethyl 2-(2-(4-hydroxy-3-methoxybenzylidene)hydrazinyl)thiazole-4-carboxylate (2g) and ethyl 2-(2-(1-phenylethylidene)hydrazinyl)thiazole-4-carboxylate (2h) as promising antioxidant agents with %FRSA: 84.46 ± 0.13 and 74.50 ± 0.37, TAC: 269.08 ± 0.92 and 269.11 ± 0.61 and TRP: 272.34 ± 0.87 and 231.11 ± 0.67 μg AAE/mg dry weight of compound. Beside bioactivities, density functional theory (DFT) methods were used to study the electronic structure and properties of synthesized compounds (2a-m). The potential of synthesized compounds for possible antiviral targets is also predicted through molecular docking methods. The compounds 2e and 2h showed good binding affinities and inhibition constants to be considered as therapeutic target for Mpro protein of SARS-CoV-2 (COVID-19). The present in-depth analysis of synthesized compounds will put them under the spot light for practical applications as antioxidants and the modification in structural motif may open the way for COVID-19 drug.
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Affiliation(s)
- Muhammad Haroon
- Department of Chemistry, Mirpur University of Science and Technology (MUST), 10250Mirpur, AJK, Pakistan
| | - Tashfeen Akhtar
- Department of Chemistry, Mirpur University of Science and Technology (MUST), 10250Mirpur, AJK, Pakistan
| | - Muhammad Khalid
- Department of Chemistry, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan, 64200, Pakistan
| | - Shehbaz Ali
- Department of Biosciences and Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Punjab, Pakistan
| | - Saniya Zahra
- Department of Pharmacy, Quaid-i-Azam University, Islamabad, Pakistan
| | - Ihsan Ul Haq
- Department of Pharmacy, Quaid-i-Azam University, Islamabad, Pakistan
| | - Muhanad Alhujaily
- Department of Clinical Laboratory, College of Applied Medicine, University of Bisha, Bisha, 61922, P.O. Box 551Saudi Arabia
| | - Mabilly C H de B Dias
- Departamento de Ciências Farmacêuticas, Centro de Ciências da Saúde, Universidade Federal de Pernambuco, 50740-520, Recife, PE, Brazil
| | - Ana Cristina Lima Leite
- Departamento de Ciências Farmacêuticas, Centro de Ciências da Saúde, Universidade Federal de Pernambuco, 50740-520, Recife, PE, Brazil
| | - Shabbir Muhammad
- Department of Physics, College of Science, King Khalid University, P.O. Box 9004, Abha61413, Saudi Arabia
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23
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Zaki MEA, Al-Hussain SA, Masand VH, Akasapu S, Bajaj SO, El-Sayed NNE, Ghosh A, Lewaa I. Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis. Pharmaceuticals (Basel) 2021; 14:357. [PMID: 33924395 PMCID: PMC8070011 DOI: 10.3390/ph14040357] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 04/05/2021] [Accepted: 04/05/2021] [Indexed: 12/16/2022] Open
Abstract
Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure-Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole-indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.
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Affiliation(s)
- Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia;
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia;
| | - Vijay H. Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra 444 602, India
| | | | | | | | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, Assam 781014, India;
| | - Israa Lewaa
- Department of Business Administration, Faculty of Business Administration, Economics and Political Science, British University in Egypt, Cairo 11837, Egypt;
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