Exploring Inhibition Mechanisms in Wildtype and T315I BCR-ABL1: An In Silico Approach Integrating Virtual Screening, MD Simulations, and MM-GBSA Analysis

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY Journal of Computational Chemistry Pub Date : 2024-12-05 DOI:10.1002/jcc.27545
Ozlen Balta, Ercument Yilmaz, Gizem Tatar Yilmaz
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Abstract

The BCR-ABL tyrosine kinase which is responsible for the pathogenesis of chronic myeloid leukemia (CML), has emerged as a promising therapeutic target. To address this issue, we employed a comprehensive computational approach integrating virtual screening, molecular dynamics (MD) simulations, and MM-GBSA (Molecular Mechanics/Generalized Born Surface Area) analysis to identify potential inhibitors and elucidate their binding mechanisms. Initially, virtual screening was conducted on 994 compounds from the ZINC database and, these compounds were docked against wildtype and T315I mutant ABL1 for the Type I and Type II ABL1 kinase inhibition mechanisms. In our molecular docking analysis for Type I inhibition, compound 911 demonstrated notable affinity towards the wildtype ABL1, with a binding energy of −14.91 kcal/mol, while compound 972 showed significant binding affinity towards the mutant ABL1, with a binding energy of −14.27 kcal/mol. In the Type II inhibition mechanism, the compounds with the highest binding affinity were compound 261 in wildtype ABL1 with −17.05 kcal/mol binding energy and compound 966 to the mutant ABL1 with a binding energy of −16.29 kcal/mol. Furthermore, analyses of MD simulations and MM/GBSA binding free energy (ΔG) were performed for target proteins with compounds, that exhibited the most favorable binding affinities with target proteins. The selected hit compounds showed ΔG scores ranging from −118.09 to −74.85 kJ/mol in both wildtype and mutant ABL1. Considering all in silico studies performed, it can be inferred that the identified molecules hold promise as potential candidates for drug design aimed at targeting CML.

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探索野生型和T315I BCR-ABL1的抑制机制:一种集成虚拟筛选、MD模拟和MM-GBSA分析的计算机方法
BCR-ABL酪氨酸激酶与慢性髓性白血病(CML)的发病机制有关,已成为一个有希望的治疗靶点。为了解决这个问题,我们采用了综合计算方法,结合虚拟筛选、分子动力学(MD)模拟和MM-GBSA(分子力学/广义出生表面积)分析来识别潜在的抑制剂并阐明它们的结合机制。首先,对来自ZINC数据库的994种化合物进行了虚拟筛选,并将这些化合物与野生型和T315I突变型ABL1对接,以研究I型和II型ABL1激酶抑制机制。在I型抑制的分子对接分析中,化合物911对野生型ABL1表现出显著的亲和力,结合能为- 14.91 kcal/mol,而化合物972对突变型ABL1表现出显著的亲和力,结合能为- 14.27 kcal/mol。在II型抑制机制中,对野生型ABL1的结合能最高的化合物为化合物261 (- 17.05 kcal/mol),对突变型ABL1的结合能最高的化合物为化合物966 (- 16.29 kcal/mol)。此外,通过MD模拟和MM/GBSA结合自由能(ΔG)对目标蛋白与化合物进行了分析,发现与目标蛋白的结合亲和力最强。所选择的命中化合物在野生型和突变型ABL1中的ΔG得分范围为- 118.09至- 74.85 kJ/mol。考虑到所进行的所有计算机研究,可以推断所鉴定的分子有望成为针对CML的药物设计的潜在候选者。
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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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