Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics
Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics
复制标题
通过人工智能辅助分子动力学发现蛋白质构象灵活性
DOI：
10.1021/acs.jpcb.0c03985
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Tiwary, Pratyush
中科院分区：
文献类型：
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作者：
Smith, Zachary;Ravindra, Pavan;Wang, Yihang;Cooley, Rory;Tiwary, Pratyush
蛋白质的样品有多种不同于其晶体结构的构象。这些结构，它们的倾向，以及它们之间的移动路径包含了大量关于蛋白质功能的信息，这些信息从纯粹的结构角度来看是隐藏的。分子动力学模拟可以揭示这些替代构象，但往往在一个令人望而却步的高计算成本。在这里，我们应用我们最近的统计力学和人工智能为基础的分子动力学框架，以增强蛋白质环的采样。我们通过对经典试件蛋白T4溶菌酶的三个突变体的研究来验证该方法。我们能够根据它们激发态的稳定性对它们进行正确的排序。通过分析反应坐标，我们还获得了至关重要的洞察力，为什么这些特定的扰动序列空间导致构象灵活性的巨大变化。因此，我们的框架允许一个准确的比较环构象群体与最小的先验人类偏见，并应直接适用于生物学，化学等一系列大分子。
Proteins sample a variety of conformations distinct from their crystal structure. These structures, their propensities, and the pathways for moving between them contain an enormous amount of information about protein function that is hidden from a purely structural perspective. Molecular dynamics simulations can uncover these alternative conformations but often at a prohibitively high computational cost. Here we apply our recent statistical mechanics and artificial intelligence-based molecular dynamics framework for enhanced sampling of protein loops. We exemplify the approach through the study of three mutants of the classical test-piece protein T4 lysozyme. We are able to correctly rank these according to the stability of their excited state. By analyzing reaction coordinates, we also obtain crucial insight into why these specific perturbations in sequence space lead to tremendous variations in conformational flexibility. Our framework thus allows an accurate comparison of loop conformation populations with minimal prior human bias and should be directly applicable to a range of macromolecules in biology, chemistry, and beyond.