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模拟肽 - 蛋白质复合物:对接、模拟与机器学习。

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

作者信息

Mondal Arup, Chang Liwei, Perez Alberto

机构信息

Department of Chemistry, University of Florida, Gainesville, FL 32611, USA.

Quantum Theory project, University of Florida, Gainesville, FL 32611, USA.

出版信息

QRB Discov. 2022 Sep 19;3:e17. doi: 10.1017/qrd.2022.14. eCollection 2022.

Abstract

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.

摘要

肽介导高达40%的蛋白质相互作用,其高特异性以及在小分子无法结合的部位进行结合的能力使其成为潜在的药物候选物。然而,预测肽-蛋白质复合物仍然比预测蛋白质-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度的灵活性。在这篇综述中,我们探讨了对接、分子模拟和机器学习方面的进展,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。我们特别着重于解释分子模拟中使用的对接程序和力场的数量,以便潜在用户能够明智地猜测为何选择一种建模工具而非另一种来解决他们的科学问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d34/10392694/c3331fe90bb6/S263328922200014X_figAb.jpg

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