{"title":"Exploring drug repositioning possibilities of kinase inhibitors via molecular simulation**","authors":"Qing‐Xin Wang, Jiao Cai, Zi‐Jun Chen, Jia‐Chuan Liu, Jing‐Jing Wang, Hai Zhou, Qing‐Qing Li, Zi‐Xuan Wang, Yi‐Bo Wang, Zhen‐Jiang Tong, Jin Yang, Tian‐Hua Wei, Meng‐Yuan Zhang, Yun Zhou, Wei‐Chen Dai, Ning Ding, Xue‐Jiao Leng, Xiao‐Ying Yin, Shan‐Liang Sun, Yan‐Cheng Yu, Nian‐Guang Li, Zhi‐Hao Shi","doi":"10.1002/minf.202300336","DOIUrl":null,"url":null,"abstract":"Kinases, a class of enzymes controlling various substrates phosphorylation, are pivotal in both physiological and pathological processes. Although their conserved ATP binding pockets pose challenges for achieving selectivity, this feature offers opportunities for drug repositioning of kinase inhibitors (KIs). This study presents a cost‐effective in silico prediction of KIs drug repositioning via analyzing cross‐docking results. We established the KIs database (278 unique KIs, 1834 bioactivity data points) and kinases database (357 kinase structures categorized by the DFG motif) for carrying out cross‐docking. Comparative analysis of the docking scores and reported experimental bioactivity revealed that the Atypical, TK, and TKL superfamilies are suitable for drug repositioning. Among these kinase superfamilies, Olverematinib, Lapatinib, and Abemaciclib displayed enzymatic activity in our focused AKT‐PI3K‐mTOR pathway with IC<jats:sub>50</jats:sub> values of 3.3, 3.2 and 5.8 μM. Further cell assays showed IC<jats:sub>50</jats:sub> values of 0.2, 1.2 and 0.6 μM in tumor cells. The consistent result between prediction and validation demonstrated that repositioning KIs via <jats:italic>in silico</jats:italic> method is feasible.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"27 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/minf.202300336","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
Abstract
Kinases, a class of enzymes controlling various substrates phosphorylation, are pivotal in both physiological and pathological processes. Although their conserved ATP binding pockets pose challenges for achieving selectivity, this feature offers opportunities for drug repositioning of kinase inhibitors (KIs). This study presents a cost‐effective in silico prediction of KIs drug repositioning via analyzing cross‐docking results. We established the KIs database (278 unique KIs, 1834 bioactivity data points) and kinases database (357 kinase structures categorized by the DFG motif) for carrying out cross‐docking. Comparative analysis of the docking scores and reported experimental bioactivity revealed that the Atypical, TK, and TKL superfamilies are suitable for drug repositioning. Among these kinase superfamilies, Olverematinib, Lapatinib, and Abemaciclib displayed enzymatic activity in our focused AKT‐PI3K‐mTOR pathway with IC50 values of 3.3, 3.2 and 5.8 μM. Further cell assays showed IC50 values of 0.2, 1.2 and 0.6 μM in tumor cells. The consistent result between prediction and validation demonstrated that repositioning KIs via in silico method is feasible.
期刊介绍:
Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010.
Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation.
The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.