1
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Lefin N, Herrera-Belén L, Farias JG, Beltrán JF. Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides. Mol Divers 2024; 28:2365-2374. [PMID: 37626205 DOI: 10.1007/s11030-023-10718-3] [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: 05/02/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023]
Abstract
Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.
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Affiliation(s)
- Nicolás Lefin
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Lisandra Herrera-Belén
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad Santo Tomás, Temuco, Chile
| | - Jorge G Farias
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Jorge F Beltrán
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile.
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2
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Song F, Sun H, Ma X, Wang W, Luan M, Zhai H, Su G, Liu Y. QSAR and molecular docking studies on designing potent inhibitors of SARS-CoVs main protease. Front Pharmacol 2023; 14:1185004. [PMID: 37266150 PMCID: PMC10230167 DOI: 10.3389/fphar.2023.1185004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 04/18/2023] [Indexed: 06/03/2023] Open
Abstract
Background: Severe acute respiratory syndrome coronavirus (SARS-CoVs) have emerged as a global health threat, which had caused a high rate of mortality. There is an urgent need to find effective drugs against these viruses. Objective: This study aims to predict the activity of unsymmetrical aromatic disulfides by constructing a QSAR model, and to design new compounds according to the structural and physicochemical attributes responsible for higher activity towards SARS-CoVs main protease. Methods: All molecules were constructed in ChemOffice software and molecular descriptors were calculated by CODESSA software. A regression-based linear heuristic method was established by changing descriptors datasets and calculating predicted IC50 values of compounds. Then, some new compounds were designed according to molecular descriptors from the heuristic method model. The compounds with predicted values smaller than a set point were constantly screened out. Finally, the properties analysis and molecular docking were conducted to further understand the structure-activity relationships of these finalized compounds. Results: The heuristic method explored the various descriptors responsible for bioactivity and gained the best linear model with R2 0.87. The success of the model fully passed the testing set validation, proving that the model has both high statistical significance and excellent predictive ability. A total of 5 compounds with ideal predicted IC50 were found from the 96 newly designed derivatives and their properties analyze was carried out. Molecular docking experiments were conducted for the optimal compound 31a, which has the best compound activity with good target protein binding capability. Conclusion: The heuristic method was quite reliable for predicting IC50 values of unsymmetrical aromatic disulfides. The present research provides meaningful guidance for further exploration of the highly active inhibitors for SARS-CoVs.
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Affiliation(s)
- Fucheng Song
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
| | - Haoyang Sun
- Department of Traditional Chinese Medicine, Songshan Hospital of Qingdao University, Qingdao, China
| | - Xiaofang Ma
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
| | - Wei Wang
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
| | | | - Honglin Zhai
- Department of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, China
| | - Guanmin Su
- Shandong Provincial Center for Disease Control and Prevention, Jinan, China
| | - Yantao Liu
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
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3
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Ivanova YO, Voronina AI, Skvortsov VS. [The prediction of SARS-CoV-2 main protease inhibition with filtering by position of ligand]. BIOMEDITSINSKAIA KHIMIIA 2022; 68:444-458. [PMID: 36573412 DOI: 10.18097/pbmc20226806444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The paper analyzes a set of equations that adequately predict the IC50 value for SARS-CoV-2 main protease inhibitors. The training set was obtained using filtering by criteria independent of prediction of target value. It included 76 compounds, and the test set included nine compounds. We used the values of energy contributions obtained in the calculation of the change of the free energy of complex by MMGBSA method and a number of characteristics of the physical and chemical properties of the inhibitors as independent variables. It is sufficient to use only seven independent variables without loss of prediction quality (Q² = 0.79; R²prediction = 0.89). The maximum error in this case does not exceed 0.92 lg(IC50) units with a full range of observed values from 1.26 to 4.95.
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Affiliation(s)
- Ya O Ivanova
- Institute of Biomedical Chemistry, Moscow, Russia
| | - A I Voronina
- Institute of Biomedical Chemistry, Moscow, Russia
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4
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Speck-Planche A, Kleandrova VV. Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles. ACS OMEGA 2022; 7:32119-32130. [PMID: 36120024 PMCID: PMC9476185 DOI: 10.1021/acsomega.2c03363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the danger associated with respiratory viruses continues to be evidenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the virus responsible for the current COVID-19 pandemic, other viruses such as SARS-CoV-1, the influenza A and B viruses (IAV and IBV, respectively), and the respiratory syncytial virus (RSV) can lead to globally spread viral diseases. Also, from a biological point of view, most of these viruses can cause an organ-damaging hyperinflammatory response known as the cytokine storm (CS). Computational approaches constitute an essential component of modern drug development campaigns, and therefore, they have the potential to accelerate the discovery of chemicals able to simultaneously inhibit multiple molecular and nonmolecular targets. We report here the first multicondition model based on quantitative structure-activity relationships and an artificial neural network (mtc-QSAR-ANN) for the virtual design and prediction of molecules with dual pan-antiviral and anti-CS profiles. Our mtc-QSAR-ANN model exhibited an accuracy higher than 80%. By interpreting the different descriptors present in the mtc-QSAR-ANN model, we could retrieve several molecular fragments whose assembly led to new molecules with drug-like properties and predicted pan-antiviral and anti-CS activities.
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Affiliation(s)
- Alejandro Speck-Planche
- Grupo
de Química Computacional y Teórica (QCT-USFQ), Departamento
de Ingeniería Química, Universidad
San Francisco de Quito, Diego de Robles y vía Interoceánica, Quito 170901, Ecuador
| | - Valeria V. Kleandrova
- Laboratory
of Fundamental and Applied Research of Quality and Technology of Food
Production, Moscow State University of Food
Production, Volokolamskoe
shosse 11, 125080, Moscow, Russian Federation
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5
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A Review on Herbal Secondary Metabolites Against COVID-19 Focusing on the Genetic Variants of SARS-CoV-2. Jundishapur J Nat Pharm Prod 2022. [DOI: 10.5812/jjnpp-129618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Context: An outbreak of the new coronavirus disease 2019 (COVID-19) was reported in Wuhan, China, in December 2019, subsequently affecting countries worldwide and causing a pandemic. Although several vaccines, such as mRNA vaccines, inactivated vaccines, and adenovirus vaccines, have been licensed in several countries, the danger of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants persists. To date, Alpha (B.1.1.7), Beta (B.1.351, B.1.351.2, B.1.351.3), Delta (B.1.617.2, AY.1, AY.2, AY. 3), Gamma (P.1, P.1.1, P.1.2), and Iota (B.1 .526) circulating in the United States, Kappa (B.1.617.1) in India, Lambda (C.37) in Peru and Mu (B.1.621) in Colombia are considered the variants of concern and interest. Evidence Acquisition: Data were collected through the end of August 2021 by searching PubMed, Scopus, and Google Scholar databases. There were findings from in silico, in vitro cell-based, and non-cell-based investigations. Results: The potential and safety profile of herbal medicines need clarification to scientifically support future recommendations regarding the benefits and risks of their use. Conclusions: Current research results on natural products against SARS-CoV-2 and variants are discussed, and their specific molecular targets and possible mechanisms of action are summarized.
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6
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Synthesis and Biological Activity of Unsymmetrical Monoterpenylhetaryl Disulfides. Molecules 2022; 27:molecules27165101. [PMID: 36014334 PMCID: PMC9416111 DOI: 10.3390/molecules27165101] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 07/28/2022] [Accepted: 08/05/2022] [Indexed: 11/24/2022] Open
Abstract
New unsymmetrical monoterpenylhetaryl disulfides based on heterocyclic disulfides and monoterpene thiols were synthesized for the first time in 48–88% yields. Hydrolysis of disulfides with fragments of methyl esters of 2-mercaptonicotinic acid was carried out in 73–95% yields. The obtained compounds were evaluated for antioxidant, antibacterial, antifungal activity, cytotoxicity and mutagenicity.
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Gao K, Wang R, Chen J, Cheng L, Frishcosy J, Huzumi Y, Qiu Y, Schluckbier T, Wei X, Wei GW. Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. Chem Rev 2022; 122:11287-11368. [PMID: 35594413 PMCID: PMC9159519 DOI: 10.1021/acs.chemrev.1c00965] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.
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Affiliation(s)
- Kaifu Gao
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jiahui Chen
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Limei Cheng
- Clinical
Pharmacology and Pharmacometrics, Bristol
Myers Squibb, Princeton, New Jersey 08536, United States
| | - Jaclyn Frishcosy
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuta Huzumi
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuchi Qiu
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Tom Schluckbier
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Xiaoqi Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
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8
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Costa AS, Martins JPA, de Melo EB. SMILES-based 2D-QSAR and similarity search for identification of potential new scaffolds for development of SARS-CoV-2 MPRO inhibitors. Struct Chem 2022; 33:1691-1706. [PMID: 35811781 PMCID: PMC9257568 DOI: 10.1007/s11224-022-02008-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 06/30/2022] [Indexed: 11/26/2022]
Abstract
COVID-19, whose etiological agent is the SARS-CoV-2 virus, has caused over 537.5 million cases and killed over 6.3 million people since its discovery in 2019. Despite the recent development of the first drugs indicated for treating people already infected, the great need to develop new anti-SARS-CoV-2 drugs still exists, mainly due to the possible emergence of new variants of this virus and resistant strains of the current variants. Thus, this work presents the results of QSAR and similarity search studies based only on 2D structures from a set of 32 bicycloproline derivatives, aiming to quickly, reproducibly, and reliably identify potentially useful compounds as scaffolds of new major protease inhibitors (Mpro) of the virus. The obtained QSAR model is based only on topological molecular descriptors. The model has good internal and external statistics, is robust, and does not present a chance correlation. This model was used as one of the tools to support the virtual screening stage carried out in the SwissADME web tool. Five molecules, from an initial set of 2695 molecules, proved to be the most promising, as they were within the model’s applicability domain and linearity range, with low potential to cause carcinogenic, teratogenic, and reproductive toxicity effects and promising pharmacokinetic properties. These five compounds were then selected as the most competent to generate, in future studies, new anti-SARS-CoV-2 agents with drug-likeness properties suitable for use in therapy.
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Affiliation(s)
- Adriana Santos Costa
- Theoretical Medicinal and Environmental Chemistry Laboratory (LQMAT), Department of Pharmacy, Western Paraná State University (UNIOESTE), 2069 Universitária St, Cascavel, Paraná, 85819-110 Brazil
| | - João Paulo Ataide Martins
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais (UFMG), 6627 Antônio Carlos Avenue, Belo Horizonte, Minas Gerais, 31270-901 Brazil
| | - Eduardo Borges de Melo
- Theoretical Medicinal and Environmental Chemistry Laboratory (LQMAT), Department of Pharmacy, Western Paraná State University (UNIOESTE), 2069 Universitária St, Cascavel, Paraná, 85819-110 Brazil
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9
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Azimi A, Ahmadi S, Kumar A, Qomi M, Almasirad A. SMILES-Based QSAR and Molecular Docking Study of Oseltamivir Derivatives as Influenza Inhibitors. Polycycl Aromat Compd 2022. [DOI: 10.1080/10406638.2022.2067194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Atena Azimi
- Faculty of Pharmacy, Tehran Medical Sciences, Department of Medicinal Chemistry, Islamic Azad University, Tehran, Iran
| | - Shahin Ahmadi
- Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Department of Chemistry, Islamic Azad University, Tehran, Iran
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Mahnaz Qomi
- Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Department of Chemistry, Islamic Azad University, Tehran, Iran
- Active Pharmaceutical Ingredients Research (APIRC), Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Ali Almasirad
- Faculty of Pharmacy, Tehran Medical Sciences, Department of Medicinal Chemistry, Islamic Azad University, Tehran, Iran
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10
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Mozafari Z, Chamjangali MA, Arashi M, Goudarzi N. Suggestion of active 3-chymotrypsin like protease (3CL Pro) inhibitors as potential anti-SARS-CoV-2 agents using predictive QSAR model based on the combination of ALASSO with an ANN model. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:863-888. [PMID: 34634208 DOI: 10.1080/1062936x.2021.1975167] [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: 07/17/2021] [Accepted: 08/28/2021] [Indexed: 06/13/2023]
Abstract
The novel severe acute respiratory syndrome coronavirus (SARS CoV-2) was introduced as an epidemic in 2019 and had millions of deaths worldwide. Given the importance of this disease, the recommendation and design of new active compounds are crucial. 3-chymotrypsin-like protease (3 CLpro) inhibitors have been identified as potent compounds for treating SARS-CoV-2 disease. So, the design of new 3 CLpro inhibitors was proposed using a quantitative structure-activity relationship (QSAR) study. In this context, a powerful adaptive least absolute shrinkage and selection operator (ALASSO) penalized variable selection method with inherent advantages coupled with a nonlinear artificial neural network (ANN) modelling method were used to provide a QSAR model with high interpretability and predictability. After evaluating the accuracy and validity of the developed ALASSO-ANN model, new compounds were proposed using effective descriptors, and the biological activity of the new compounds was predicted. Ligand-receptor (LR) interactions were also performed to confirm the interaction strength of the compounds using molecular docking (MD) study. The pharmacokinetics properties and calculated Lipinski's rule of five were applied to all proposed compounds. Due to the ease of synthesis of these suggested new compounds, it is expected that they have acceptable pharmacological properties.
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Affiliation(s)
- Z Mozafari
- Department of Chemistry, Shahrood University of Technology, Shahrood, Iran
| | - M Arab Chamjangali
- Department of Chemistry, Shahrood University of Technology, Shahrood, Iran
| | - M Arashi
- Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
| | - N Goudarzi
- Department of Chemistry, Shahrood University of Technology, Shahrood, Iran
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11
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Toropova AP, Toropov AA, Benfenati E. Semi-correlations as a tool to model for skin sensitization. Food Chem Toxicol 2021; 157:112580. [PMID: 34560179 DOI: 10.1016/j.fct.2021.112580] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/20/2021] [Indexed: 01/10/2023]
Abstract
Semi-correlation specifically assesses the correlation between a binary variable and a continuous variable. Semi-correlations were applied to develop binary models for various endpoints. We applied the semi-correlation to develop models of two kinds of skin sensitization one related to animals (local lymph node assay LLNA) and one to human beings (direct peptide reactivity assay DPRA and/or human cell line activation test h-CLAT). The models refer to binary classification for a two-level strategy: the first level (analysis of all compounds) is used in the format "sensitizer or non-sensitizer", and the second level (only sensitizers) is a further classification in the format "strong or weak sensitizer". The ranges of statistical characteristics of the models depend on the endpoint, LLNA or DPRA/h-CLAT: for the first level, sensitivity: 0.69-0.88, specificity: 0.75-0.89, accuracy: 0.77-0.87, Matthew's correlation coefficient (MCC): 0.54-0.57 and for the second level, sensitivity: 0.70-1.0, specificity: 0.78-0.83, accuracy: 0.77-0.87, MCC: 0.54-0.76. Thus, the described approach can be applied to building up models of the skin sensitization potency.
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Affiliation(s)
- Alla P Toropova
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.
| | - Andrey A Toropov
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - Emilio Benfenati
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
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12
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Amin SA, Banerjee S, Singh S, Qureshi IA, Gayen S, Jha T. First structure-activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery. Mol Divers 2021; 25:1827-1838. [PMID: 33400085 PMCID: PMC7782049 DOI: 10.1007/s11030-020-10166-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 11/28/2020] [Indexed: 11/10/2022]
Abstract
Main protease (Mpro) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) intervenes in the replication and transcription processes of the virus. Hence, it is a lucrative target for anti-viral drug development. In this study, molecular modeling analyses were performed on the structure activity data of recently reported diverse SARS-CoV-2 Mpro inhibitors to understand the structural requirements for higher inhibitory activity. The classification-based quantitative structure-activity relationship (QSAR) models were generated between SARS-CoV-2 Mpro inhibitory activities and different descriptors. Identification of structural fingerprints to increase or decrease in the inhibitory activity was mapped for possible inclusion/exclusion of these fingerprints in the lead optimization process. Challenges in ADME properties of protease inhibitors were also discussed to overcome the problems of oral bioavailability. Further, depending on the modeling results, we have proposed novel as well as potent SARS-CoV-2 Mpro inhibitors.
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Affiliation(s)
- Sk Abdul Amin
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Suvankar Banerjee
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Samayaditya Singh
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, 500046, Telangana, India
| | - Insaf Ahmed Qureshi
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, 500046, Telangana, India
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, 470003, MP, India.
| | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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13
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Toropova AP, Toropov AA, Benfenati E. The self-organizing vector of atom-pairs proportions: use to develop models for melting points. Struct Chem 2021. [DOI: 10.1007/s11224-021-01778-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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14
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Toropova AP, Toropov AA, Roncaglioni A, Benfenati E. The index of ideality of correlation improves the predictive potential of models of the antioxidant activity of tripeptides from frog skin (Litoria rubella). Comput Biol Med 2021; 133:104370. [PMID: 33838612 DOI: 10.1016/j.compbiomed.2021.104370] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 03/29/2021] [Accepted: 03/29/2021] [Indexed: 12/20/2022]
Abstract
It is usually held that good-quality models for the biological activity of peptides must take into account their 3D architecture and descriptors of quantum mechanics. However, the present study shows that it is possible to build up models without these complex calculations. The structure of tripeptides represented by sequences of one-symbol abbreviations of the corresponding amino acids serves to build up quantitative structure-activity relationships for the antioxidant activity of tripeptides from frog skin. The statistical quality of the best model for the validation set is n = 27, r2 = 0.93, RMSE = 0.15.
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Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - Alessandra Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
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15
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Toropov AA, Toropova AP, Roncaglioni A, Benfenati E. The system of self-consistent semi-correlations as one of the tools of cheminformatics for designing antiviral drugs. NEW J CHEM 2021. [DOI: 10.1039/d1nj03394h] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The development of antiviral agents against SARS-CoV-2 is necessary.
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Affiliation(s)
- Andrey A. Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alla P. Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alessandra Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
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Przybyłek M. Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening. Molecules 2020; 25:E5942. [PMID: 33333961 PMCID: PMC7765417 DOI: 10.3390/molecules25245942] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/12/2020] [Accepted: 12/14/2020] [Indexed: 12/14/2022] Open
Abstract
Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models' accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs.
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Affiliation(s)
- Maciej Przybyłek
- Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, Poland
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