Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.
Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.
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DOI：
10.1021/acs.jctc.9b00781
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发表时间：
2020-02-11
影响因子：
5.5
通讯作者：
Schiffer CA
中科院分区：
文献类型：
--
作者：
Whitfield TW;Ragland DA;Zeldovich KB;Schiffer CA
在过去的几十年里，生物分子的原子模拟，无论是使用分子动力学还是蒙特卡罗技术，都提供了对其功能的详细见解。对几个密切相关系统的模拟结果进行比较，有助于我们理解配体结合或突变等变化可以改变功能的机制。然而，从许多相关系统的模拟中检测和解释这些机制的一般问题仍然是一个挑战。本文通过将有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来解决这个问题。作为一个重要的测试案例，这些方法被应用于了解人类免疫缺陷病毒1型（HIV-1）蛋白酶对darunavir的逃避，darunavir是一种有效的抑制剂，可以通过多个氨基酸的同时突变而产生耐药性。在耐药菌株中观察到复杂的突变模式，这对开发蛋白酶耐药的机制图片提出了挑战。为了剖析这些模式并深入了解特定突变的作用机制，对HIV-1蛋白酶变体进行了分子动力学模拟，其中包括高度耐药菌株和易感对照，并与darunavir联合使用。利用机器学习方法，利用序列、结构和功能之间关系的层次性，对这些轨迹进行综合分析，揭示了抗性机制的关键细节，包括蛋白质结构、氢键和蛋白质-配体接触的变化。
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.
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影响因子：
5.5
作者：
Cai, Yufeng;Schiffer, Celia A.
通讯作者：
Schiffer, Celia A.
影响因子：
2.9
作者：
Clemente, JC;Hemrajani, R;Dunn, BA
通讯作者：
Dunn, BA
影响因子：
15
作者：
Altman, Michael D.;Ali, Akbar;Tidor, Bruce
通讯作者：
Tidor, Bruce
DOI：
10.1073/pnas.97.18.10101
发表时间：
2000-08-29
影响因子：
11.1
作者：
Alter, O;Brown, PO;Botstein, D
通讯作者：
Botstein, D
影响因子：
8.6
作者：
Behler, Joerg;Parrinello, Michele
通讯作者：
Parrinello, Michele