建模肽-蛋白复合物:对接,模拟和机器学习。

Q3 Biochemistry, Genetics and Molecular Biology QRB Discovery Pub Date : 2022-01-01 DOI:10.1017/qrd.2022.14
Arup Mondal, Liwei Chang, Alberto Perez
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引用次数: 5

摘要

肽介导了高达40%的蛋白质相互作用,它们的高特异性和在小分子无法结合的地方结合的能力使它们成为潜在的候选药物。然而,预测肽-蛋白复合物仍然比蛋白质-蛋白质或蛋白质-小分子相互作用更具挑战性,部分原因是肽具有高灵活性。在这篇综述中,我们着眼于对接,分子模拟和机器学习的进展,以解决与肽相关的问题,如预测结构,结合亲和力甚至动力学。我们特别专注于解释分子模拟中使用的对接程序和力场的数量,因此潜在用户可以有一个有根据的猜测,为什么选择一个建模工具或另一个来解决他们的科学问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Modelling peptide-protein complexes: docking, simulations and machine learning.

Peptides mediate up to 40% of protein interactions, their high specificity and ability to bind in places where small molecules cannot make them potential drug candidates. However, predicting peptide-protein complexes remains more challenging than protein-protein or protein-small molecule interactions, in part due to the high flexibility peptides have. In this review, we look at the advances in docking, molecular simulations and machine learning to tackle problems related to peptides such as predicting structures, binding affinities or even kinetics. We specifically focus on explaining the number of docking programmes and force fields used in molecular simulations, so a prospective user can have an educated guess as to why choose one modelling tool or another to address their scientific questions.

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来源期刊
QRB Discovery
QRB Discovery Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
3.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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