用于准确预测 p38 MAPK II 型抑制剂解离率的蛋白质配体相互作用指纹图谱。

IF 1.4 4区 生物学 Q4 CELL BIOLOGY
Duo Zhang, Shuheng Huang, Hu Mei, MuliadiYeremia Kevin, Tingting Shi, Linxin Chen
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引用次数: 13

摘要

结合/解结合动力学是决定药物效力的关键因素。然而,在药物开发的早期阶段,预测动力学特性仍面临许多挑战。在这项工作中,应用位置约束分子动力学模拟结合能量分解,提取了20种p38丝裂原活化蛋白激酶(p38 MAPK)II型抑制剂在解结合途径上的蛋白质配体相互作用(PLI)指纹。结果表明,三个关键位置的静电和/或范德华相互作用指纹可用于准确预测 p38 MAPK II 型抑制剂的解离率常数 (koff)。本文提出的策略不仅可以提供预测 p38 MAPK II 型抑制剂解离率的有效方法,还可以提供焓驱动解结合过程的原子级机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Protein-ligand interaction fingerprints for accurate prediction of dissociation rates of p38 MAPK Type II inhibitors.

Binding/unbinding kinetics are key determinants of drug potencies. However, there are still a lot of challenges in predicting kinetic properties during early-stage drug development. In this work, position-restrained molecular dynamics simulations combined with energy decomposition were applied to extract protein-ligand interaction (PLI) fingerprints along the unbinding pathway of 20 p38 mitogen-activated protein kinase (p38 MAPK) Type II inhibitors. The results showed that the electrostatic and/or van der Waals interaction fingerprints at three key positions can be used for accurate prediction of the dissociation rate constants (koff) of p38 MAPK Type II inhibitors. The strategy proposed in this paper can provide not only an efficient method of predicting the dissociation rates of the p38 MAPK Type II inhibitors, but also the atom-level mechanism of enthalpy-driven unbinding process.

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来源期刊
Integrative Biology
Integrative Biology 生物-细胞生物学
CiteScore
4.90
自引率
0.00%
发文量
15
审稿时长
1 months
期刊介绍: Integrative Biology publishes original biological research based on innovative experimental and theoretical methodologies that answer biological questions. The journal is multi- and inter-disciplinary, calling upon expertise and technologies from the physical sciences, engineering, computation, imaging, and mathematics to address critical questions in biological systems. Research using experimental or computational quantitative technologies to characterise biological systems at the molecular, cellular, tissue and population levels is welcomed. Of particular interest are submissions contributing to quantitative understanding of how component properties at one level in the dimensional scale (nano to micro) determine system behaviour at a higher level of complexity. Studies of synthetic systems, whether used to elucidate fundamental principles of biological function or as the basis for novel applications are also of interest.
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