An integrated toolkit combining computational systems biology techniques with molecular dynamics simulations to delineate functionality of GPCRs

一个集成的工具包，将计算系统生物学技术与分子动力学模拟相结合，以描述 GPCR 的功能

• 批准号: 10659236
• 负责人: Andrei Rodin
• 金额: $ 37.4万
• 依托单位: BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-05 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659236
• 关键词: Adopted; Agonist; Algorithmic Software; Algorithms; Bar Codes; Base Sequence; Bayesian Modeling; Bayesian Network; Binding Sites; Biological Assay; Cell membrane; Cells; Communication; Communities; Complex; Computer software; Coupling; Data; Data Analyses; Data Reporting; Drug Targeting; Elements; Environment; Event; Explosion; Family; Fluorescence Resonance Energy Transfer; Frequencies; G-Protein-Coupled Receptors; GTP-Binding Proteins; Glean; Goals; Grant; Human; Knowledge; Label; Ligand Binding; Ligands; Maps; Mediating; Methods; Mining; Molecular Conformation; Motion; Nature; Neighborhoods; Physiological Processes; Principal Component Analysis; Property; Protein Dynamics; Protein Sequence Analysis; Proteins; Receptor Activation; Resolution; Series; Site; Structure; System; Systems Biology; Techniques; Testing; Time; Transducers; Validation; conformational conversion; design; drug discovery; experimental study; high dimensionality; innovation; insight; machine learning method; molecular dynamics; molecular scale; novel strategies; pharmacologic; predictive test; protein complex; public health relevance; receptor; sensor; spatiotemporal; stem; structural determinants; three dimensional structure; tool

Project Summary / Abstract In the last decade there has been an explosion of resolved high-resolution structures of G-protein coupled receptors (GPCRs) and their complexes with several G-proteins, collectively known as transducer proteins. GPCRs are dynamic proteins, and exist in multiple functional conformational states. Comparisons of three-dimensional structures of the inactive and active states of GPCRs have led to identification of residue pair distances that show distinct changes upon activation. Such residue pairs are known as “activation microswitches”. Molecular Dynamics (MD) simulations is an attractive tool for identifying (i) the residue pairs that are critical to GPCR activation, (ii) residue pairs involved in allosteric communication from the ligand binding site to the G protein coupling site, and (iii) residue pairs in the GPCR:G protein interfaces that contribute to their coupling strength and selectivity. While our ability to generate long time scale dynamics trajectories have increased exponentially, the results of MD simulations have largely been analyzed using prior knowledge of the GPCRs. There is a critical need for adopting the unbiased, data-driven, systems biology tools to analyze long time scale MD trajectories data to mine knowledge on the residue motions that provide information on allosteric communication network in GPCRs. Our overarching goal in this grant is to apply Bayesian Network (BN) modeling, an interpretable machine learning methodology, to the MD simulation trajectories data on GPCR:G protein complexes in order to identify the residues in various GPCR structural regions that contribute to ligand selectivity. Network-centered approaches have not been used, so far, to analyze high-dimensional residue pairs MD simulation data. BN modeling, in particular, has attractive properties (interpretability, probabilistic nature of the data representation, statistical validation, tools for topology comparison and analysis) that presently deployed secondary MD simulation data analysis methods, such as principal component analysis (PCA) of residue pairs, lack. We propose to use BN modeling with large scale MD trajectories (multiple short and multiple long trajectories) of inactive state GPCRs and fully active state GPCR:G protein complexes to (i) identify the activation microswitches, the residue pairs that show large scale conformational changes upon activation, and (ii) outline the residue network involved in the allosteric communication from the agonist binding site to the G-protein coupling sites. We will also (iii) identify the residues in the GPCR:G protein interface that contribute to selectivity in coupling to specific family of G proteins (Gs, Gi and Gq). In aim 2, we will (iv) dissect time-correlated events using BN series and dynamic BN (DBN) models to delineate and scrutinize the residue networks that lead to large scale transitions. Importantly, the deliverables will include an unprecedented “toolkit” (algorithms + software) incorporating system biology tools for analyzing MD simulation trajectories data that are generalizable to any protein complexes.

项目概要/摘要 在过去的十年中，G 蛋白偶联的高分辨率结构的解析出现了爆炸性的增长。 受体 (GPCR) 及其与多种 G 蛋白的复合物，统称为转导蛋白。 是动态蛋白质，以多种功能构象状态存在。 GPCR 的非活性和活性状态的结构已导致残基对距离的识别，显示 激活后的明显变化被称为“激活微开关”。 (MD) 模拟是一种有吸引力的工具，可用于识别 (i) 对 GPCR 激活至关重要的残基对，(ii) 参与从配体结合位点到 G 蛋白偶联位点的变构通讯的残基对，以及 (iii) GPCR：G 蛋白界面中的残基对有助于其偶联强度和选择性。 虽然我们生成长时间尺度动态轨迹的能力呈指数级增长，但结果 MD 模拟很大程度上是利用 GPCR 的先验知识进行分析的，因此迫切需要。 采用公正的、数据驱动的系统生物学工具来分析长期尺度的MD轨迹数据以进行挖掘 关于残基运动的知识提供了 GPCR 中变构通信网络的信息。 这笔赠款的总体目标是应用贝叶斯网络（BN）建模，这是一种可解释的机器学习 方法，对 GPCR:G 蛋白复合物的 MD 模拟轨迹数据进行鉴定，以识别残基 有助于配体选择性的各种 GPCR 结构区域尚未得到证实。 迄今为止，用于分析高维残基对 MD 模拟数据尤其具有吸引力。 属性（可解释性、数据表示的概率性质、统计验证、拓扑工具 比较和分析），目前采用了二次MD模拟数据分析方法，例如主 残基对的成分分析（PCA），缺乏。 我们建议使用具有大规模 MD 轨迹（多个短轨迹和多个长轨迹）的 BN 建模 非活性状态 GPCR 和完全活性状态 GPCR：G 蛋白复合物，以 (i) 识别激活微开关， 激活后显示大规模构象变化的残基对，以及（ii）概述残基网络 参与从激动剂结合位点到 G 蛋白偶联位点的变构通讯。 鉴定 GPCR：G 蛋白界面中有助于选择性偶联特定 G 家族的残基 在目标 2 中，我们将 (iv) 使用 BN 系列和动态 BN (DBN) 剖析时间相关事件。 模型来描绘和审查导致大规模转变的残留网络。 重要的是，交付成果将包括一个前所未有的“工具包”（算法+软件），其中包含 用于分析可推广到任何蛋白质复合物的 MD 模拟轨迹数据的系统生物学工具。