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Gu Z, Yan Y, Liu H, Wu D, Yao H, Lin K, Li X. Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network. J Chem Inf Model 2024; 64:3047-3058. [PMID: 38520328 DOI: 10.1021/acs.jcim.3c01900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2024]
Abstract
Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale because covalent docking requires determination of the covalent reaction type of the compound. Here, we propose to use deep learning of a lateral interactions spiking neural network to construct a covalent lead compound screening model to quickly screen covalent lead compounds. We used the 3CL protease (3CL Pro) of SARS-CoV-2 as the screen target and constructed two classification models based on LISNN to predict the covalent binding and inhibitory activity of compounds. The two classification models were trained on the covalent complex data set targeting cysteine (Cys) and the compound inhibitory activity data set targeting 3CL Pro, respected, with good prediction accuracy (ACC > 0.9). We then screened the screening compound library with 6 covalent binding screening models and 12 inhibitory activity screening models. We tested the inhibitory activity of the 32 compounds, and the best compound inhibited SARS-CoV-2 3CL Pro with an IC50 value of 369.5 nM. Further assay implied that dithiothreitol can affect the inhibitory activity of the compound to 3CL Pro, indicating that the compound may covalently bind 3CL Pro. The selectivity test showed that the compound had good target selectivity to 3CL Pro over cathepsin L. These correlation assays can prove the rationality of the covalent lead compound screening model. Finally, covalent docking was performed to demonstrate the binding conformation of the compound with 3CL Pro. The source code can be obtained from the GitHub repository (https://github.com/guzh970630/Screen_Covalent_Compound_by_LISNN).
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Affiliation(s)
- Zhihao Gu
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yong Yan
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Hanwen Liu
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Di Wu
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Hequan Yao
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Kejiang Lin
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Xuanyi Li
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
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Chagaleti BK, Saravanan V, Vellapandian C, Kathiravan MK. Exploring cyclin-dependent kinase inhibitors: a comprehensive study in search of CDK-6 inhibitors using a pharmacophore modelling and dynamics approach. RSC Adv 2023; 13:33770-33785. [PMID: 38019988 PMCID: PMC10655667 DOI: 10.1039/d3ra05672d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Cancer prevalence and resistance issues in cancer treatment are a significant public health concern globally. Among the existing strategies in cancer therapy, targeting cyclin-dependent kinases (CDKs), especially CDK-6 is found to be one of the most promising targets, as this enzyme plays a pivotal role in cell cycle stages and cell proliferation. Cell proliferation is the characteristic feature of cancer giving rise to solid tumours. Our research focuses on creating novel compounds, specifically, pyrazolopyrimidine fused azetidinones, using a groundbreaking molecular hybridization approach to target CDK-6. Through computational investigations, ligand-based pharmacophore modelling, pharmacokinetic studies (ADMET), molecular docking, and dynamics simulations, we identified 18 promising compounds. The pharmacophore model featured one aromatic hydrophobic centre (F1: Aro/Hyd) and two H-bond acceptors (F2 and F3: Acc). Molecular docking results showed favourable binding energies (-6.5 to -8.0 kcal mol-1) and effective hydrogen bonds and hydrophobic interactions. The designed compounds demonstrated good ADMET profiles. Specifically, B6 and B18 showed low energy conformation (-7.8 kcal and -7.6 kcal), providing insights into target inhibition compared to the standard drug Palbociclib. Extensive molecular dynamics simulations confirmed the stability of these derivatives. Throughout the 100 ns simulation, the ligand-protein complexes maintained structural stability, with acceptable RMSD values. These compounds hold promise as potential leads in cancer therapy.
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Affiliation(s)
- Bharath Kumar Chagaleti
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology Kattankulathur-603203 India
| | - Venkatesan Saravanan
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology Kattankulathur-603203 India
| | - Chitra Vellapandian
- Department of Pharmacology, SRM College of Pharmacy SRMIST, Kattankulathur Chennai Tamil Nadu - 603 203 India
| | - Muthu K Kathiravan
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology Kattankulathur-603203 India
- Dr A. P. J. Abdul Kalam Research Lab, Department of Pharmaceutical Chemistry, SRM College of Pharmacy SRMIST, Kattankulathur Chennai Tamil Nadu - 603 203 India
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Samuel JG, Malgija B, Ebenezer C, Solomon RV. Insight into designing of 2-pyridone derivatives for COVID-19 drug discovery - A computational study. Struct Chem 2022; 34:1-20. [PMID: 36320317 PMCID: PMC9607770 DOI: 10.1007/s11224-022-02076-x] [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: 06/01/2022] [Accepted: 10/04/2022] [Indexed: 11/27/2022]
Abstract
Presently, the prime global focus is on SARS-CoV-2, as no fully established, licensed medicine has been found thus far, in spite of the existence of various reports and administration of partially proven certain class of natural products. However, in case of natural products, the extraction and purification limit their application. This situation drives researchers to explore synthetically viable drugs. In the present investigation, twenty-three 2-pyridone synthetic derivatives (P1-P23) have been theoretically tested for their suitability as potential inhibitors for COVID-19 main protease through DFT, molecular docking, and molecular dynamics simulations. DFT calculations offer insights into structure-property relationships, while ADMET studies indicate the pharmacological characteristics of these molecules. Molecular docking studies ascertain the nature and mode of interactions of these entities with COVID-19 main protease. Furthermore, covalent docking has been carried out to verify the feasibility of the formation of a covalent bond with the active site. The top protein-inhibitor complexes, such as P18, P11, and P12, were identified based on their glide score. These molecules, along with the covalent docked complexes, namely P18 and P16, were selected and subjected to molecular dynamics simulations. The 100 ns simulation process exhibited that the covalent docked ones, due to their stable form could serve as lead compounds against SARS-CoV-2. Hence, this study affirms the potential candidature of 2-pyridone-based inhibitors.
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Affiliation(s)
- Joseph George Samuel
- Department of Chemistry, Madras Christian College (Autonomous), University of Madras), Chennai, 600 059 India
| | - Beutline Malgija
- MCC-MRF Innovation Park, Madras Christian College, Chennai, 600 059 India
| | - Cheriyan Ebenezer
- Department of Chemistry, Madras Christian College (Autonomous), University of Madras), Chennai, 600 059 India
| | - Rajadurai Vijay Solomon
- Department of Chemistry, Madras Christian College (Autonomous), University of Madras), Chennai, 600 059 India
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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: 13] [Impact Index Per Article: 6.5] [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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Investigating novel thiazolyl-indazole derivatives as scaffolds for SARS-CoV-2 MPro inhibitors. EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY REPORTS 2022. [PMID: 37519829 PMCID: PMC8828376 DOI: 10.1016/j.ejmcr.2022.100034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
COVID-19 is a global pandemic caused by infection with the SARS-CoV-2 virus. Remdesivir, a SARS-CoV-2 RNA polymerase inhibitor, is the only drug to have received widespread approval for treatment of COVID-19. The SARS-CoV-2 main protease enzyme (MPro), essential for viral replication and transcription, remains an active target in the search for new treatments. In this study, the ability of novel thiazolyl-indazole derivatives to inhibit MPro is evaluated. These compounds were synthesized via the heterocyclization of phenacyl bromide with (R)-carvone, (R)-pulegone and (R)-menthone thiosemicarbazones. The binding affinity and binding interactions of each compound were evaluated through Schrödinger Glide docking, AMBER molecular dynamics simulations, and MM-GBSA free energy estimation, and these results were compared with similar calculations of MPro binding various 5-mer substrates (VKLQA, VKLQS, VKLQG) and a previously identified MPro tight-binder X77. From these simulations, we can see that binding is driven by residue specific interactions such as π-stacking with His41, and S/π interactions with Met49 and Met165. The compounds were also experimentally evaluated in a MPro biochemical assay and the most potent compound containing a phenylthiazole moiety inhibited protease activity with an IC50 of 92.9 μM. This suggests that the phenylthiazole scaffold is a promising candidate for the development of future MPro inhibitors.
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Mohamed NM, Eltelbany RFA. Synthetic Coumarin Derivatives as SARS‐CoV‐2 Major Protease Inhibitors: Design, Synthesis, Bioevaluation and Molecular Docking. ChemistrySelect 2021. [DOI: 10.1002/slct.202103658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Nada M. Mohamed
- Pharmaceutical Chemistry Department Faculty of Pharmacy Modern University for Technology and Information (MTI) Cairo 11585 Egypt
| | - Rania F. A. Eltelbany
- Biochemistry Department Faculty of Pharmacy Modern University for Technology and Information (MTI) Cairo 11585 Egypt
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Gupta SS, Kumar A, Shankar R, Sharma U. In silico approach for identifying natural lead molecules against SARS-COV-2. J Mol Graph Model 2021; 106:107916. [PMID: 33892297 PMCID: PMC8042570 DOI: 10.1016/j.jmgm.2021.107916] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/24/2021] [Accepted: 03/29/2021] [Indexed: 11/30/2022]
Abstract
The life challenging COVID-19 disease caused by the SARS-CoV-2 virus has greatly impacted smooth survival worldwide since its discovery in December 2019. Currently, it is one of the major threats to humanity. Moreover, any specific drug or vaccine unavailability against COVID-19 forces to discover a new drug on an urgent basis. Viral cycle inhibition could be one possible way to prevent the further genesis of this viral disease, which can be contributed by drug repurposing techniques or screening of small bioactive natural molecules against already validated targets of COVID-19. The main protease (Mpro) responsible for producing functional proteins from polyprotein is an important key step for SARS-CoV-2 virion replication. Natural product or herbal based formulations are an important platform for potential therapeutics and lead compounds in the drug discovery process. Therefore, here we have screened >53,500 bioactive natural molecules from six different natural product databases against Mpro (PDB ID: 6LU7) of COVID-19 through computational study. Further, the top three molecules were subjected to pharmacokinetics evaluation, which is an important factor that reduces the drug failure rate. Moreover, the top three screened molecules (C00014803, C00006660, ANLT0001) were further validated by a molecular dynamics study under a condition similar to the physiological one. Relative binding energy analysis of three lead molecules indicated that C00014803 possess highest binding affinity among all three hits. These extensive studies can be a significant foundation for developing a therapeutic agent against COVID-19 through vet lab studies.
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Affiliation(s)
- Shiv Shankar Gupta
- Chemical Technology Division, CSIR-IHBT, Palampur, HP, 176 061, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Ashwani Kumar
- Biotechnology Division, CSIR-IHBT, Palampur, HP, 176 061, India
| | - Ravi Shankar
- Biotechnology Division, CSIR-IHBT, Palampur, HP, 176 061, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
| | - Upendra Sharma
- Chemical Technology Division, CSIR-IHBT, Palampur, HP, 176 061, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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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: 13] [Impact Index Per Article: 4.3] [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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