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Boutalaka M, El Bahi S, Alaqarbeh M, El Alaouy MA, Koubi Y, Khatabi KE, Maghat H, Bouachrine M, Lakhlifi T. Computational investigation of imidazo[2,1-b]oxazole derivatives as potential mutant BRAF kinase inhibitors: 3D-QSAR, molecular docking, molecular dynamics simulation, and ADMETox studies. J Biomol Struct Dyn 2024; 42:5268-5287. [PMID: 37424193 DOI: 10.1080/07391102.2023.2233629] [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: 03/01/2023] [Accepted: 06/09/2023] [Indexed: 07/11/2023]
Abstract
BRAF inhibitors are known to be an effective therapeutic target for treating melanoma and other types of cancer. Using 3D-QSAR, molecular docking, and MD simulations, this study evaluated various imidazo[2,1-b]oxazole derivatives that function as mutant BRAF kinase inhibitors. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were used to create the 3D-QSAR models. CoMSIA/SEHA model has solid predictive power across several models (Q2 = 0.578; R2 = 0.828; R2pred = 0.74) and is the best model according to the numerous field models generated. The created model's predictive power was evaluated through external validation using a test set. CoMSIA/SEHA contour maps collect information that can be used to identify critical regions with solid anticancer activity. We developed four inhibitors with high predicted activity due to these observations. ADMET prediction was used to assess the toxicity of the proposed imidazo[2,1-b]oxazole compounds. The predictive molecules (T1-T4) demonstrated good ADMET properties, excluding the toxic active compounds 11r from the database. Molecular docking was also used to determine the patterns and modes of interactions between imidazo[2,1-b]oxazole ligands and receptors, which revealed that the proposed imidazo[2,1-b]oxazole scaffold was stable in the receptor's active site (PDB code: 4G9C). The suggested compounds (T1-T4) were subjected to molecular dynamics simulations lasting 100 ns to determine their binding free energies. The results showed that T2 had a more favorable binding free energy (-149.552 kJ/mol) than T1 (-112.556 kJ/mol), T3 (-115.503 kJ/mol), and T4 (-102.553 kJ/mol). The results suggest that the imidazo[2,1-b]oxazole compounds investigated in this study have potential as inhibitors of BRAF kinase and could be further developed as anticancer drugs. Highlights22 imidazo[2,1-b]oxazole compounds were subjected to research on three-dimensional quantitative conformational relationships.Using contour maps from 3D-QSAR models as a guide was used to figure out the areas and strategies for structural optimization.Combined molecular docking, molecular dynamics simulations, and binding free energy calculations to verify the inhibitor activity of the proposed 22 imidazo[2,1-b]oxazole compounds.Four potential B-RAF Kinase inhibitors were discovered, providing theoretical clues for developing a highly anticancer agent.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Meryem Boutalaka
- Department of Chemistry, Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University of Moulay Ismail, Meknes, Morocco
| | - Salma El Bahi
- Department of Chemistry, Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University of Moulay Ismail, Meknes, Morocco
| | | | - Moulay Ahfid El Alaouy
- Department of Chemistry, Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University of Moulay Ismail, Meknes, Morocco
| | - Yassine Koubi
- Department of Chemistry, Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University of Moulay Ismail, Meknes, Morocco
| | - Khalil El Khatabi
- Department of Chemistry, Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University of Moulay Ismail, Meknes, Morocco
| | - Hamid Maghat
- Department of Chemistry, Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University of Moulay Ismail, Meknes, Morocco
| | - Mohammed Bouachrine
- Department of Chemistry, Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University of Moulay Ismail, Meknes, Morocco
- EST Khenifra, Sultan Moulay Slimane University, Beni Mellal, Morocco
| | - Tahar Lakhlifi
- Department of Chemistry, Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University of Moulay Ismail, Meknes, Morocco
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Liang J, Zheng Y, Tong X, Yang N, Dai S. In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning. Molecules 2022; 28:208. [PMID: 36615401 PMCID: PMC9821958 DOI: 10.3390/molecules28010208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/17/2022] [Accepted: 12/23/2022] [Indexed: 12/28/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19, is spreading rapidly and has caused hundreds of millions of infections and millions of deaths worldwide. Due to the lack of specific vaccines and effective treatments for COVID-19, there is an urgent need to identify effective drugs. Traditional Chinese medicine (TCM) is a valuable resource for identifying novel anti-SARS-CoV-2 drugs based on the important contribution of TCM and its potential benefits in COVID-19 treatment. Herein, we aimed to discover novel anti-SARS-CoV-2 compounds and medicinal plants from TCM by establishing a prediction method of anti-SARS-CoV-2 activity using machine learning methods. We first constructed a benchmark dataset from anti-SARS-CoV-2 bioactivity data collected from the ChEMBL database. Then, we established random forest (RF) and support vector machine (SVM) models that both achieved satisfactory predictive performance with AUC values of 0.90. By using this method, a total of 1011 active anti-SARS-CoV-2 compounds were predicted from the TCMSP database. Among these compounds, six compounds with highly potent activity were confirmed in the anti-SARS-CoV-2 experiments. The molecular fingerprint similarity analysis revealed that only 24 of the 1011 compounds have high similarity to the FDA-approved antiviral drugs, indicating that most of the compounds were structurally novel. Based on the predicted anti-SARS-CoV-2 compounds, we identified 74 anti-SARS-CoV-2 medicinal plants through enrichment analysis. The 74 plants are widely distributed in 68 genera and 43 families, 14 of which belong to antipyretic detoxicate plants. In summary, this study provided several medicinal plants with potential anti-SARS-CoV-2 activity, which offer an attractive starting point and a broader scope to mine for potentially novel anti-SARS-CoV-2 drugs.
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Affiliation(s)
- Jihao Liang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Yang Zheng
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Xin Tong
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Naixue Yang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Shaoxing Dai
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
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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: 31] [Impact Index Per Article: 15.5] [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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