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Ajmal A, Alkhatabi HA, Alreemi RM, Alamri MA, Khalid A, Abdalla AN, Alotaibi BS, Wadood A. Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target. BMC Chem 2024; 18:57. [PMID: 38528576 DOI: 10.1186/s13065-024-01152-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/26/2024] [Indexed: 03/27/2024] Open
Abstract
Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).
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Affiliation(s)
- Amar Ajmal
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, 23200, Pakistan
| | - Hind A Alkhatabi
- Department of Biochemistry, College of Science, University of Jeddah, Jeddah, 21959, Saudi Arabia
| | - Roaa M Alreemi
- Department of Biochemistry, College of Science, University of Jeddah, Jeddah, 21959, Saudi Arabia
| | - Mubarak A Alamri
- Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Asaad Khalid
- Substance Abuse and Toxicology Research Center, Jazan University, P.O. Box: 114, Jazan, 45142, Saudi Arabia.
| | - Ashraf N Abdalla
- Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
| | - Bader S Alotaibi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra Univesity, Al- Quwayiyah, Riyadh, Saudi Arabia
| | - Abdul Wadood
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, 23200, Pakistan.
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Shafiq M, Sherwani ZA, Mushtaq M, Nur-E-Alam M, Ahmad A, Ul-Haq Z. A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors. Mol Divers 2024:10.1007/s11030-023-10799-0. [PMID: 38305819 DOI: 10.1007/s11030-023-10799-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 12/22/2023] [Indexed: 02/03/2024]
Abstract
Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for further research, the significant structural conservation among PI3Ks makes it extremely difficult to develop new isoform-selective PI3Kα inhibitors. Embracing this challenge, we herein designed a hybrid protocol by integrating machine learning (ML) with in silico drug-designing strategies. A deep learning classification model was developed and trained on the physicochemical descriptors data of known PI3Kα inhibitors and used as a screening filter for a database of small molecules. This approach led us to the prediction of 662 compounds showcasing appropriate features to be considered as PI3Kα inhibitors. Subsequently, a multiphase molecular docking was applied to further characterize the predicted hits in terms of their binding affinities and binding modes in the targeted cavity of the PI3Kα. As a result, a total of 12 compounds were identified whereas the best poses highlighted the efficiency of these ligands in maintaining interactions with the crucial residues of the protein to be targeted for the inhibition of associated activity. Notably, potential activity of compound 12 in counteracting PI3Kα function was found in a previous in vitro study. Following the drug-likeness and pharmacokinetic characterizations, six compounds (compounds 1, 2, 3, 6, 7, and 11) with suitable ADME-T profiles and promising bioavailability were selected. The mechanistic studies in dynamic mode further endorsed the potential of identified hits in blocking the ATP-binding site of the receptor with higher binding affinities than the native inhibitor, alpelisib (BYL-719), particularly the compounds 1, 2, and 11. These outcomes support the reliability of the developed classification model and the devised computational strategy for identifying new isoform-selective drug candidates for PI3Kα inhibition.
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Affiliation(s)
- Muhammad Shafiq
- H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Zaid Anis Sherwani
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Mamona Mushtaq
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Mohammad Nur-E-Alam
- Department of Pharmacognosy, College of Pharmacy, King Saud University, P.O. Box. 2457, Riyadh, 11451, Kingdom of Saudi Arabia
| | - Aftab Ahmad
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, 92618, USA
| | - Zaheer Ul-Haq
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.
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Manochkumar J, Cherukuri AK, Kumar RS, Almansour AI, Ramamoorthy S, Efferth T. A critical review of machine-learning for "multi-omics" marine metabolite datasets. Comput Biol Med 2023; 165:107425. [PMID: 37696182 DOI: 10.1016/j.compbiomed.2023.107425] [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: 05/30/2023] [Revised: 07/12/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.
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Affiliation(s)
- Janani Manochkumar
- School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, India
| | - Aswani Kumar Cherukuri
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
| | - Raju Suresh Kumar
- Department of Chemistry, College of Science, King Saud University, P. O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Abdulrahman I Almansour
- Department of Chemistry, College of Science, King Saud University, P. O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Siva Ramamoorthy
- School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, India.
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Mainz, Germany.
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