Muhammedkutty Fidha Nazreen Kunnath, MacAinsh Matthew, Zhou Huan-Xiang
Department of Chemistry and Department of Physics, University of Illinois Chicago, Chicago, IL, 60607, USA.
Department of Chemistry and Department of Physics, University of Illinois Chicago, Chicago, IL, 60607, USA; Department of Physics, University of Illinois Chicago, Chicago, IL, 60607, USA.
Curr Opin Struct Biol. 2025 Jun;92:103029. doi: 10.1016/j.sbi.2025.103029. Epub 2025 Mar 10.
Recent years have seen remarkable gains in the accuracy of atomistic molecular dynamics (MD) simulations of intrinsically disordered proteins (IDPs) and expansion in the types of calculated properties that can be directly compared with experimental measurements. These advances occurred due to the use of IDP-tested force fields and the porting of MD simulations to GPUs and other computational technologies. All-atom MD simulations are now explaining the sequence-dependent dynamics of IDPs; elucidating the mechanisms of their binding to other proteins, nucleic acids, and membranes; revealing the modes of drug action on them; and characterizing their phase separation. Artificial intelligence (AI) and machine learning (ML) are further expanding the reach of atomistic MD simulations.
近年来,内在无序蛋白质(IDP)的原子分子动力学(MD)模拟准确性有了显著提高,可直接与实验测量相比较的计算属性类型也有所扩展。这些进展得益于使用经过IDP测试的力场,以及将MD模拟移植到GPU和其他计算技术上。全原子MD模拟现在正在解释IDP的序列依赖性动力学;阐明它们与其他蛋白质、核酸和膜结合的机制;揭示药物对它们的作用模式;以及表征它们的相分离。人工智能(AI)和机器学习(ML)正在进一步扩展原子MD模拟的范围。