Computational insights into allosteric inhibition of focal adhesion kinase: A combined pharmacophore modeling and molecular dynamics approach

IF 2.7 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Vikas Kumar , Pooja Singh , Shraddha Parate , Rajender Singh , Hyeon-Su Ro , Kyoung Seob Song , Keun Woo Lee , Yeong-Min Park
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Abstract

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase that modulates integrin and growth factor signaling pathways and is implicated in cancer cell migration, proliferation, and survival. Over the past decade various, FAK kinase, FERM, and FAT domain inhibitors have been reported and a few kinase domain inhibitors are under clinical consideration. However, few of them were identified as multikinase inhibitors. In kinase drug design selectivity is always a point of concern, to improve selectivity allosteric inhibitor development is the best choice. The current research utilized a pharmacophore modeling (PM) approach to identify novel allosteric inhibitors of FAK. The all-available allosteric inhibitor bound 3D structures with PDB ids 4EBV, 4EBW, and 4I4F were utilized for the pharmacophore modeling. The validated PM models were utilized to map a database of 770,550 compounds prepared from ZINC, EXIMED, SPECS, ASINEX, and InterBioScreen, aiming to identify potential allosteric inhibitors. The obtained compounds from screening step were forwarded to molecular docking (MD) for the prediction of binding orientation inside the allosteric site and the results were evaluated with the known FAK allosteric inhibitor (REF). Finally, 14 FAK-inhibitor complexes were selected from the docking study and were studied under molecular dynamics simulations (MDS) for 500 ns. The complexes were ranked according to binding free energy (BFE) and those demonstrated higher affinity for allosteric site of FAK than REF inhibitors were selected. The selected complexes were further analyzed for intermolecular interactions and finally, three potential allosteric inhibitor candidates for the inhibition of FAK protein were identified. We believe that identified scaffolds may help in drug development against FAK as an anticancer agent.

Abstract Image

通过计算深入了解病灶粘附激酶的异位抑制作用:药效学建模与分子动力学相结合的方法
病灶粘附激酶(FAK)是一种非受体酪氨酸激酶,可调节整合素和生长因子信号通路,与癌细胞的迁移、增殖和存活有关。在过去十年中,各种 FAK 激酶、FERM 和 FAT 结构域抑制剂已被报道,一些激酶结构域抑制剂正在临床试验中。然而,其中只有少数被确定为多激酶抑制剂。在激酶药物设计中,选择性始终是一个关注点,为了提高选择性,开发异构抑制剂是最好的选择。目前的研究采用药效学建模(PM)方法来确定新型 FAK 异构抑制剂。药效学建模利用了 PDB ids 为 4EBV、4EBW 和 4I4F 的所有可用的异构抑制剂结合三维结构。经过验证的 PM 模型被用于映射从 ZINC、EXIMED、SPECS、ASINEX 和 InterBioScreen 中获得的 770,550 个化合物的数据库,目的是找出潜在的异位抑制剂。筛选步骤中获得的化合物被转入分子对接(MD),以预测在异构位点内的结合方向,并将结果与已知的 FAK 异构抑制剂(REF)进行评估。最后,从对接研究中选出了 14 个 FAK 抑制剂复合物,并在分子动力学模拟(MDS)中进行了 500 ns 的研究。根据结合自由能(BFE)对复合物进行了排序,选出了与 FAK 的异构位点亲和力高于 REF 抑制剂的复合物。我们进一步分析了所选复合物的分子间相互作用,最后确定了三种潜在的异位抑制剂候选物,用于抑制 FAK 蛋白。我们相信,所发现的支架可能有助于开发针对 FAK 的抗癌药物。
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来源期刊
Journal of molecular graphics & modelling
Journal of molecular graphics & modelling 生物-计算机:跨学科应用
CiteScore
5.50
自引率
6.90%
发文量
216
审稿时长
35 days
期刊介绍: The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design. As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.
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