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Jawarkar RD, Bakal RL, Zaki MEA, Al-Hussain S, Ghosh A, Gandhi A, Mukerjee N, Samad A, Masand VH, Lewaa I. QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CL pro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches. ARAB J CHEM 2022; 15:103499. [PMID: 34909066 PMCID: PMC8524701 DOI: 10.1016/j.arabjc.2021.103499] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 10/10/2021] [Indexed: 12/26/2022] Open
Abstract
Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved PEC50 value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.
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Key Words
- 3CLpro, 3C like Protease
- FDA, Food and Drug Administration
- GA-MLR
- GA-MLR, Genetic Algorithm Multilinear Regression
- HCoV SARS 3CLpro
- HCoV-HKU1, Human coronavirus HKU1
- HCoV-NL63, Human coronavirus NL63
- HCoVs, human coronaviruses
- Lead
- MD, Molecular Docking
- MDS, molecular dynamic simulation
- MERS, Middle East Respiratory Syndrome
- MMGBSA calculations
- MMGBSA, Molecular Mechanics Generalized Born and Surface Area
- Molecular docking and MD simulation
- OECD, Organization for Economic Corporation and Development
- QSAR based virtual screening
- QSAR, Quantitative Structure Activity Relationship
- RNA, Ribo-nucleic acid
- SARS, severe acute respiratory sign
- VS, Virtual Screening
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Affiliation(s)
- R D Jawarkar
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati, Maharashtra, 444603, India
| | - Ravindrakumar L Bakal
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati, Maharashtra, 444603, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Al-Imam Mohammad Ibn Saud Islamic university, Riyadh 13318, Saudi Arabia
| | - Sami Al-Hussain
- Department of Chemistry, Faculty of Science, Al-Imam Mohammad Ibn Saud Islamic university, Riyadh 13318, Saudi Arabia
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, Assam 781014, India
| | - Ajaykumar Gandhi
- Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra 431 004, India
| | - Nobendu Mukerjee
- Department of Microbiology; Ramakrishna Mission Vivekananda Centenary College, Akhil Mukherjee Rd, Chowdhary Para, Rahara, Khardaha, Kolkata, West Bengal 700118, India
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Vijay H Masand
- Department of Chemistry, Vidyabharti Mahavidyalaya, Camp Road, Amravati Maharashtra, India
| | - Israa Lewaa
- Department of Business Administration, Faculty of Business Administration, Economics & Political Science, The British University in Egypt (BUE), Cairo, Egypt
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Lans I, Palacio-Rodríguez K, Cavasotto CN, Cossio P. Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles. J Comput Aided Mol Des 2020; 34:1063-1077. [PMID: 32656619 PMCID: PMC7449997 DOI: 10.1007/s10822-020-00329-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/27/2020] [Indexed: 01/27/2023]
Abstract
Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.
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Affiliation(s)
- Isaias Lans
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Karen Palacio-Rodríguez
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Pilar, Buenos Aires, Argentina
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
| | - Pilar Cossio
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia.
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438, Frankfurt am Main, Germany.
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Karasev D, Sobolev B, Lagunin A, Filimonov D, Poroikov V. Prediction of Protein-Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences. Int J Mol Sci 2019; 21:ijms21010024. [PMID: 31861473 PMCID: PMC6981593 DOI: 10.3390/ijms21010024] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/13/2019] [Accepted: 12/16/2019] [Indexed: 12/14/2022] Open
Abstract
The affinity of different drug-like ligands to multiple protein targets reflects general chemical–biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein–ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands’ specificity well coincides with the phylogenic taxonomy of the proteins. Methods using multiple alignment require an accurate match of functionally significant residues. Such conditions may not be met in the case of diverged protein families. To overcome these limitations, we propose an approach based on the analysis of local sequence similarity within the set of analyzed proteins. The positional scores, calculated by sequence fragment comparisons, are used as input data for the Bayesian classifier. Our approach provides a prediction accuracy comparable or exceeding those of other methods. It was demonstrated on the popular Gold Standard test sets, presenting different sequence heterogeneity and varying from the group, including different protein families to the more specific groups. A reasonable prediction accuracy was also found for protein kinases, displaying weak relationships between sequence phylogeny and inhibitor specificity. Thus, our method can be applied to the broad area of protein–ligand interactions.
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Affiliation(s)
- Dmitry Karasev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; (B.S.); (A.L.); (D.F.); (V.P.)
- Correspondence:
| | - Boris Sobolev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; (B.S.); (A.L.); (D.F.); (V.P.)
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; (B.S.); (A.L.); (D.F.); (V.P.)
- Department of Bioinformatics, Russian National Research Medical University, Moscow 117997, Russia
| | - Dmitry Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; (B.S.); (A.L.); (D.F.); (V.P.)
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; (B.S.); (A.L.); (D.F.); (V.P.)
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Liu Y. Incorporation of absorption and metabolism into liver toxicity prediction for phytochemicals: A tiered in silico QSAR approach. Food Chem Toxicol 2018; 118:409-415. [DOI: 10.1016/j.fct.2018.05.039] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/05/2018] [Accepted: 05/16/2018] [Indexed: 02/06/2023]
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Hevener KE. Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation. Methods Mol Biol 2018; 1800:275-285. [PMID: 29934898 DOI: 10.1007/978-1-4939-7899-1_13] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process, is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This chapter focuses on the application of computational molecular filters, applied either prescreening or postscreening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns.
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Affiliation(s)
- Kirk E Hevener
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, USA.
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