Department of Medicine , University of Massachusetts Medical School , Worcester , Massachusetts 01605 , United States.
Program in Bioinformatics and Integrative Biology , University of Massachusetts Medical School , Worcester , Massachusetts 01605 , United States.
J Chem Theory Comput. 2020 Feb 11;16(2):1284-1299. doi: 10.1021/acs.jctc.9b00781. Epub 2020 Jan 16.
Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few closely related systems has guided our understanding of the mechanisms by which changes such as ligand binding or mutation can alter the function. The general problem of detecting and interpreting such mechanisms from simulations of many related systems, however, remains a challenge. This problem is addressed here by applying supervised and unsupervised machine learning techniques to a variety of thermodynamic observables extracted from molecular dynamics simulations of different systems. As an important test case, these methods are applied to understand the evasion by human immunodeficiency virus type-1 (HIV-1) protease of darunavir, a potent inhibitor to which resistance can develop via the simultaneous mutation of multiple amino acids. Complex mutational patterns have been observed among resistant strains, presenting a challenge to developing a mechanistic picture of resistance in the protease. In order to dissect these patterns and gain mechanistic insight into the role of specific mutations, molecular dynamics simulations were carried out on a collection of HIV-1 protease variants, chosen to include highly resistant strains and susceptible controls, in complex with darunavir. Using a machine learning approach that takes advantage of the hierarchical nature in the relationships among the sequence, structure, and function, an integrative analysis of these trajectories reveals key details of the resistance mechanism, including changes in the protein structure, hydrogen bonding, and protein-ligand contacts.
在过去几十年中，使用分子动力学或蒙特卡罗技术对生物分子进行的原子级模拟为它们的功能提供了详细的见解。比较几个密切相关系统的此类模拟结果，有助于我们理解配体结合或突变等变化如何改变功能的机制。然而，从许多相关系统的模拟中检测和解释这种机制的一般问题仍然是一个挑战。通过将监督和无监督机器学习技术应用于从不同系统的分子动力学模拟中提取的各种热力学观测值，可以解决这个问题。作为一个重要的测试案例，这些方法被应用于理解人类免疫缺陷病毒 1 型 (HIV-1) 蛋白酶对达芦那韦的逃避，达芦那韦是一种有效的抑制剂，通过多个氨基酸的同时突变可以产生耐药性。在耐药菌株中观察到复杂的突变模式，这给蛋白酶耐药性的机制研究带来了挑战。为了解剖这些模式并深入了解特定突变的作用，对达芦那韦与一系列 HIV-1 蛋白酶变体的复合物进行了分子动力学模拟，这些变体包括高度耐药的菌株和易感对照。利用一种机器学习方法，该方法利用序列、结构和功能之间的层次关系，对这些轨迹进行综合分析，揭示了耐药机制的关键细节，包括蛋白质结构、氢键和蛋白质-配体接触的变化。
