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Ohue M, Kojima Y, Kosugi T. Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties. Molecules 2023; 28:5652. [PMID: 37570623 PMCID: PMC10420264 DOI: 10.3390/molecules28155652] [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: 06/21/2023] [Revised: 07/06/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Protein-protein interactions (PPIs) are associated with various diseases; hence, they are important targets in drug discovery. However, the physicochemical empirical properties of PPI-targeted drugs are distinct from those of conventional small molecule oral pharmaceuticals, which adhere to the "rule of five (RO5)". Therefore, developing PPI-targeted drugs using conventional methods, such as molecular generation models, is challenging. In this study, we propose a molecular generation model based on deep reinforcement learning that is specialized for the production of PPI inhibitors. By introducing a scoring function that can represent the properties of PPI inhibitors, we successfully generated potential PPI inhibitor compounds. These newly constructed virtual compounds possess the desired properties for PPI inhibitors, and they show similarity to commercially available PPI libraries. The virtual compounds are freely available as a virtual library.
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Affiliation(s)
- Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Kanagawa 226-8501, Japan (T.K.)
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2
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Ibrahim AH, Karabulut OC, Karpuzcu BA, Türk E, Süzek BE. A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction. PLoS One 2023; 18:e0285168. [PMID: 37130110 PMCID: PMC10153705 DOI: 10.1371/journal.pone.0285168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 04/17/2023] [Indexed: 05/03/2023] Open
Abstract
Prediction of virus-host protein-protein interactions (PPI) is a broad research area where various machine-learning-based classifiers are developed. Transforming biological data into machine-usable features is a preliminary step in constructing these virus-host PPI prediction tools. In this study, we have adopted a virus-host PPI dataset and a reduced amino acids alphabet to create tripeptide features and introduced a correlation coefficient-based feature selection. We applied feature selection across several correlation coefficient metrics and statistically tested their relevance in a structural context. We compared the performance of feature-selection models against that of the baseline virus-host PPI prediction models created using different classification algorithms without the feature selection. We also tested the performance of these baseline models against the previously available tools to ensure their predictive power is acceptable. Here, the Pearson coefficient provides the best performance with respect to the baseline model as measured by AUPR; a drop of 0.003 in AUPR while achieving a 73.3% (from 686 to 183) reduction in the number of tripeptides features for random forest. The results suggest our correlation coefficient-based feature selection approach, while decreasing the computation time and space complexity, has a limited impact on the prediction performance of virus-host PPI prediction tools.
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Affiliation(s)
- Ahmed Hassan Ibrahim
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Onur Can Karabulut
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Betül Asiye Karpuzcu
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Erdem Türk
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Barış Ethem Süzek
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
- Georgetown University Medical Center, Biochemistry and Molecular & Cellular Biology, Washington DC, United States of America
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Singh S, Chauhan P, Sharma V, Rao A, Kumbhar BV, Prajapati VK. Identification of multi-targeting natural antiviral peptides to impede SARS-CoV-2 infection. Struct Chem 2022; 34:1-16. [PMID: 36570051 PMCID: PMC9759041 DOI: 10.1007/s11224-022-02113-9] [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: 04/11/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2 and its variants cause serious health concerns throughout the world. The alarming increase in the daily number of cases has become a nightmare in many low-income countries; although some vaccines are available, their high cost and low vaccine production make them unreachable to ordinary people in developing countries. Other treatment strategies are required for novel therapeutic options. The peptide-based drug is one of the alternatives with low toxicity, more specificity, and ease of synthesis. Herein, we have applied structure-based virtual screening to identify potential peptides targeting the critical proteins of SARS-CoV-2. Non-toxic natural antiviral peptides were selected from the enormous number of peptides. Comparative modeling was applied to prepare a 3D structure of selected peptides. 3D models of the peptides were docked using the ClusPro docking server to determine their binding affinity and peptide-protein interaction. The high-scoring peptides were docked with other crucial proteins to analyze multiple targeting peptides. The two best peptides were subjected to MD simulations to validate the structure stability and evaluated RMSD, RMSF, Rg, SASA, and H-bonding from the trajectory analysis of 100 ns. The proposed lead peptides can be used as a broad-spectrum drug and potentially develop as a therapeutic to combat SARS-CoV-2, positively impacting the current pandemic. Supplementary Information The online version contains supplementary material available at 10.1007/s11224-022-02113-9.
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Affiliation(s)
- Satyendra Singh
- Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan 305817 India
| | - Priya Chauhan
- Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan 305817 India
| | - Vinita Sharma
- Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan 305817 India
| | - Abhishek Rao
- Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan 305817 India
| | - Bajarang Vasant Kumbhar
- Department of Biological Sciences, Sunandan Divatia School of Science, NMIMS University (Deemed), Vile Parle, Mumbai, India
| | - Vijay Kumar Prajapati
- Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan 305817 India
- Department of Biochemistry, School of Biological Sciences, Central University of Punjab, Bathinda, Punjab India
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Debnath SK, Debnath M, Srivastava R, Omri A. Drugs repurposing for SARS-CoV-2: new insight of COVID-19 druggability. Expert Rev Anti Infect Ther 2022; 20:1187-1204. [PMID: 35615888 DOI: 10.1080/14787210.2022.2082944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
INTRODUCTION The ongoing epidemic of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) creates a massive panic worldwide due to the absence of effective medicines. Developing a new drug or vaccine is time-consuming to pass safety and efficacy testing. Therefore, repurposing drugs have been introduced to treat COVID-19 until effective drugs are developed. AREA COVERED A detailed search of repurposing drugs against SARS-CoV-2 was carried out using the PubMed database, focusing on articles published 2020 years onward. A different class of drugs has been described in this article to target hosts and viruses. Based on the previous pandemic experience of SARS-CoV and MERS, several antiviral and antimalarial drugs are discussed here. This review covers the failure of some repurposed drugs that showed promising activity in the earlier CoV-pandemic but were found ineffective against SARS-CoV-2. All these discussions demand a successful drug development strategy for screening and identifying an effective drug for better management of COVID-19. The drug development strategies described here will serve a new scope of research for academicians and researchers. EXPERT OPINION Repurposed drugs have been used since COVID-19 to eradicate disease propagation. Drugs found effective for MERS and SARS may not be effective against SARS-CoV-2. Drug libraries and artificial intelligence are helpful tools to screen and identify different molecules targeting viruses or hosts.
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Affiliation(s)
- Sujit Kumar Debnath
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Monalisha Debnath
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Rohit Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Abdelwahab Omri
- Department of Chemistry and Biochemistry, The Novel Drug & Vaccine Delivery Systems Facility, Laurentian University, Sudbury, Canada
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Perišić O, Wriggers W. Mechanism for the Unfolding of the TOP7 Protein in Steered Molecular Dynamics Simulations as Revealed by Mutual Information Analysis. Front Mol Biosci 2021; 8:696609. [PMID: 34660691 PMCID: PMC8516001 DOI: 10.3389/fmolb.2021.696609] [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: 04/17/2021] [Accepted: 08/30/2021] [Indexed: 12/03/2022] Open
Abstract
We employed mutual information (MI) analysis to detect motions affecting the mechanical resistance of the human-engineered protein Top7. The results are based on the MI analysis of pair contact correlations measured in steered molecular dynamics (SMD) trajectories and their statistical dependence on global unfolding. This study is the first application of the MI analysis to SMD forced unfolding, and we furnish specific SMD recommendations for the utility of parameters and options in the TimeScapes package. The MI analysis provided a global overview of the effect of perturbation on the stability of the protein. We also employed a more conventional trajectory analysis for a detailed description of the mechanical resistance of Top7. Specifically, we investigated 1) the hydropathy of the interactions of structural segments, 2) the H2O concentration near residues relevant for unfolding, and 3) the changing hydrogen bonding patterns and main chain dihedral angles. The results show that the application of MI in the study of protein mechanical resistance can be useful for the engineering of more resistant mutants when combined with conventional analysis. We propose a novel mutation design based on the hydropathy of residues that would stabilize the unfolding region by mimicking its more stable symmetry mate. The proposed design process does not involve the introduction of covalent crosslinks, so it has the potential to preserve the conformational space and unfolding pathway of the protein.
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Affiliation(s)
| | - Willy Wriggers
- Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA, United States
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Habibi M, Taheri G. Topological network based drug repurposing for coronavirus 2019. PLoS One 2021; 16:e0255270. [PMID: 34324563 PMCID: PMC8320924 DOI: 10.1371/journal.pone.0255270] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/13/2021] [Indexed: 12/14/2022] Open
Abstract
The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become the current health concern and threat to the entire world. Thus, the world needs the fast recognition of appropriate drugs to restrict the spread of this disease. The global effort started to identify the best drug compounds to treat COVID-19, but going through a series of clinical trials and our lack of information about the details of the virus's performance has slowed down the time to reach this goal. In this work, we try to select the subset of human proteins as candidate sets that can bind to approved drugs. Our method is based on the information on human-virus protein interaction and their effect on the biological processes of the host cells. We also define some informative topological and statistical features for proteins in the protein-protein interaction network. We evaluate our selected sets with two groups of drugs. The first group contains the experimental unapproved treatments for COVID-19, and we show that from 17 drugs in this group, 15 drugs are approved by our selected sets. The second group contains the external clinical trials for COVID-19, and we show that 85% of drugs in this group, target at least one protein of our selected sets. We also study COVID-19 associated protein sets and identify proteins that are essential to disease pathology. For this analysis, we use DAVID tools to show and compare disease-associated genes that are contributed between the COVID-19 comorbidities. Our results for shared genes show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases. In the last part of this work, we recommend 56 potential effective drugs for further research and investigation for COVID-19 treatment. Materials and implementations are available at: https://github.com/MahnazHabibi/Drug-repurposing.
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Affiliation(s)
- Mahnaz Habibi
- Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran
- * E-mail:
| | - Golnaz Taheri
- Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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Bojarska J, New R, Borowiecki P, Remko M, Breza M, Madura ID, Fruziński A, Pietrzak A, Wolf WM. The First Insight Into the Supramolecular System of D,L-α-Difluoromethylornithine: A New Antiviral Perspective. Front Chem 2021; 9:679776. [PMID: 34055746 PMCID: PMC8155678 DOI: 10.3389/fchem.2021.679776] [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: 03/12/2021] [Accepted: 04/26/2021] [Indexed: 12/28/2022] Open
Abstract
Targeting the polyamine biosynthetic pathway by inhibiting ornithine decarboxylase (ODC) is a powerful approach in the fight against diverse viruses, including SARS-CoV-2. Difluoromethylornithine (DFMO, eflornithine) is the best-known inhibitor of ODC and a broad-spectrum, unique therapeutical agent. Nevertheless, its pharmacokinetic profile is not perfect, especially when large doses are required in antiviral treatment. This article presents a holistic study focusing on the molecular and supramolecular structure of DFMO and the design of its analogues toward the development of safer and more effective formulations. In this context, we provide the first deep insight into the supramolecular system of DFMO supplemented by a comprehensive, qualitative and quantitative survey of non-covalent interactions via Hirshfeld surface, molecular electrostatic potential, enrichment ratio and energy frameworks analysis visualizing 3-D topology of interactions in order to understand the differences in the cooperativity of interactions involved in the formation of either basic or large synthons (Long-range Synthon Aufbau Modules, LSAM) at the subsequent levels of well-organized supramolecular self-assembly, in comparison with the ornithine structure. In the light of the drug discovery, supramolecular studies of amino acids, essential constituents of proteins, are of prime importance. In brief, the same amino-carboxy synthons are observed in the bio-system containing DFMO. DFT calculations revealed that the biological environment changes the molecular structure of DFMO only slightly. The ADMET profile of structural modifications of DFMO and optimization of its analogue as a new promising drug via molecular docking are discussed in detail.
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Affiliation(s)
- Joanna Bojarska
- Chemistry Department, Institute of Ecological and Inorganic Chemistry, Technical University of Lodz, Lodz, Poland
| | - Roger New
- Faculty of Science & Technology, Middlesex University, London, United Kingdom
| | - Paweł Borowiecki
- Faculty of Chemistry, Department of Drugs Technology and Biotechnology, Laboratory of Biocatalysis and Biotransformation, Warsaw University of Technology, Warsaw, Poland
| | | | - Martin Breza
- Department of Physical Chemistry, Slovak Technical University, Bratislava, Slovakia
| | - Izabela D. Madura
- Faculty of Chemistry, Warsaw University of Technology, Warsaw, Poland
| | - Andrzej Fruziński
- Chemistry Department, Institute of Ecological and Inorganic Chemistry, Technical University of Lodz, Lodz, Poland
| | - Anna Pietrzak
- Chemistry Department, Institute of Ecological and Inorganic Chemistry, Technical University of Lodz, Lodz, Poland
| | - Wojciech M. Wolf
- Chemistry Department, Institute of Ecological and Inorganic Chemistry, Technical University of Lodz, Lodz, Poland
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Chang CK, Lin SM, Satange R, Lin SC, Sun SC, Wu HY, Kehn-Hall K, Hou MH. Targeting protein-protein interaction interfaces in COVID-19 drug discovery. Comput Struct Biotechnol J 2021; 19:2246-2255. [PMID: 33936565 PMCID: PMC8064971 DOI: 10.1016/j.csbj.2021.04.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/30/2021] [Accepted: 04/01/2021] [Indexed: 02/06/2023] Open
Abstract
To date, the COVID-19 pandemic has claimed over 1 million human lives, infected another 50 million individuals and wreaked havoc on the global economy. The crisis has spurred the ongoing development of drugs targeting its etiological agent, the SARS-CoV-2. Targeting relevant protein-protein interaction interfaces (PPIIs) is a viable paradigm for the design of antiviral drugs and enriches the targetable chemical space by providing alternative targets for drug discovery. In this review, we will provide a comprehensive overview of the theory, methods and applications of PPII-targeted drug development towards COVID-19 based on recent literature. We will also highlight novel developments, such as the successful use of non-native protein-protein interactions as targets for antiviral drug screening. We hope that this review may serve as an entry point for those interested in applying PPIIs towards COVID-19 drug discovery and speed up drug development against the pandemic.
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Affiliation(s)
- Chung-Ke Chang
- Taiwan Biobank, Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Shan-Meng Lin
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan
| | - Roshan Satange
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan.,Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Taichung 402, Taiwan
| | - Shih-Chao Lin
- Bachelor Degree Program in Marine Biotechnology, National Taiwan Ocean University, Keelung 20224, Taiwan
| | - Sin-Cih Sun
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan
| | - Hung-Yi Wu
- Institute of Veterinary Pathobiology, College of Veterinary Medicine, National Chung Hsing University, Taichung 40227, Taiwan
| | - Kylene Kehn-Hall
- Department of Biomedical Sciences & Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Virginia 24061, United States
| | - Ming-Hon Hou
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan.,Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Taichung 402, Taiwan
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Vicenti I, Zazzi M, Saladini F. SARS-CoV-2 RNA-dependent RNA polymerase as a therapeutic target for COVID-19. Expert Opin Ther Pat 2021; 31:325-337. [PMID: 33475441 PMCID: PMC7938656 DOI: 10.1080/13543776.2021.1880568] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 01/20/2021] [Indexed: 02/08/2023]
Abstract
Introduction: The current SARS-CoV-2 pandemic urgently demands for both prevention and treatment strategies. RNA-dependent RNA-polymerase (RdRp), which has no counterpart in human cells, is an excellent target for drug development. Given the time-consuming process of drug development, repurposing drugs approved for other indications or at least successfully tested in terms of safety and tolerability, is an attractive strategy to rapidly provide an effective medication for severe COVID-19 cases.Areas covered: The currently available data and upcominSg studies on RdRp which can be repurposed to halt SARS-CoV-2 replication, are reviewed.Expert opinion: Drug repurposing and design of novel compounds are proceeding in parallel to provide a quick response and new specific drugs, respectively. Notably, the proofreading SARS-CoV-2 exonuclease activity could limit the potential for drugs designed as immediate chain terminators and favor the development of compounds acting through delayed termination. While vaccination is awaited to curb the SARS-CoV-2 epidemic, even partially effective drugs from repurposing strategies can be of help to treat severe cases of disease. Considering the high conservation of RdRp among coronaviruses, an improved knowledge of its activity in vitro can provide useful information for drug development or drug repurposing to combat SARS-CoV-2 as well as future pandemics.
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Affiliation(s)
- Ilaria Vicenti
- Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Maurizio Zazzi
- Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Francesco Saladini
- Department of Medical Biotechnologies, University of Siena, Siena, Italy
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