A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs
A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs
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DOI：
10.3390/molecules24112097
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发表时间：
2019-06-01
期刊：
影响因子：
4.6
通讯作者：
Weinstein, Harel
中科院分区：
文献类型：
--
作者：
Plante, Ambrose;Shore, Derek M.;Weinstein, Harel
G蛋白偶联受体（GPCR）在许多细胞信号传导机制中起关键作用，并且必须以配体特异性方式在多种偶联可能性中进行选择，以便在不同的细胞环境中执行无数功能。从分子动力学（MD）模拟中已经了解了很多关于配体-GPCR复合物的分子机制。然而，为了探索GPCR对不同配体的响应中的配体特异性差异，如理解配体偏差和功能选择性所需的，需要从所需的大规模模拟中创建非常大量的数据。这就成为一个大数据问题，用于对累积轨迹进行高维分析。在这里，我们描述了一种新的机器学习（ML）方法来解决这个问题，该方法基于将MD模拟轨迹中编码的GPCR功能相关的配体特异性差异的分析转换为最先进的深度学习对象识别技术可识别的表示。我们说明了这种方法，应用它来识别的药理学分类的配体结合5-HT 2A和D2亚型的A类GPCR的血清素和多巴胺的家庭。ML为基础的方法被证明执行的分类任务具有很高的准确性，我们确定的GPCR结构和功能的背景下的分类的分子决定因素。本研究建立了一个框架，用于对为了解配体特异性GPCR活性而收集的MD大数据进行有效的计算分析。
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity.