Markov State Model approaches for folding, binding and design

用于折叠、装订和设计的马尔可夫状态模型方法

• 批准号: 9923709
• 负责人: Vincent Voelz
• 金额: $ 29.56万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9923709
• 关键词: Address; Affinity; Algorithmic Software; Algorithms; Antibiotic Resistance; Area; Behavior; Binding; Binding Proteins; Biological Assay; Biology; Caliber; Collaborations; Computer Hardware; Computer software; Consumption; Custom; Data; Drug Design; Entropy; Free Energy; Goals; Grain; Health; Human; Libraries; Ligand Binding; Ligands; MDM2 gene; Malignant Neoplasms; Measures; Medicine; Methods; Microbial Biofilms; Modeling; Molecular; Molecular Conformation; Mutation; N-terminal; Pathway interactions; Peptides; Play; Point Mutation; Population; Property; Protein Dynamics; Protein Engineering; Proteins; Role; Route; Sampling; Scheme; Series; Site; Source; System; TP53 gene; Technology; Testing; Thermodynamics; Time; Uncertainty; Work; Yeasts; analytical tool; base; cluster computing; computational pipelines; computational platform; design; drug discovery; experimental study; improved; in silico; peptidomimetics; preservation; protein protein interaction; receptor; simulation; small molecule; tool

Project Summary Understanding the conformational dynamics of proteins and their binding partners are crucial to predicting and designing their function. As computer hardware and software becomes ever more efficient, simulation- based methods will play increasingly important roles in molecular design. Markov State Models (MSMs), which describe conformational dynamics as a network of transitions between metastable states, can be used as simulation-based platform for the prediction and design of multiple sequences—for small perturbations that preserve state definitions, mutational effects can be inferred by estimating changes in transition rates—but new methods must be developed to do this efficiently. We will address this challenge by developing new methods to efficiently sample MSMs for multiple sequences, and then apply this technology to predict and design binding affinities and rates of peptidomimetics, an area that will have widespread benefits to human health. Our first specific aim is to develop two analytic tools facilitating the efficient estimation of MSMs for multiple sequences: (1) surprisal-based adaptive sampling, which uses a relative entropy metric for two or more MSMs to prioritize sampling of states that most efficiently decrease the uncertainty in the models, and (2) maximum- caliber approaches for inferring changes in MSM transition rates directly from changes in state populations. These methods will be tested against changes in stabilities and folding rates measured for a corpus of well- studied mini-proteins with available trajectory data. Our second specific aim is to apply this technology to predict binding affinities, pathways and rates for peptidomimetic ligands of MDM2, a well-studied protein-peptide binding system and important cancer target. We will build MSMs of apo-MDM2 to explore the role of the N-terminal lid region in ligand binding, and the utility of MSM-derived receptor ensembles for computational drug design. We will then construct an MSM of p53 binding to MDM2, and use it as a starting point for building multi-ensemble MSMs of ligand binding for series of related small-molecules, peptides, and spiroligomer peptidomimetics, with the goal of achieving efficient estimates of affinities as well as binding on- and off-rates. Our third specific aim, a collaboration with the David Baker lab, is to use MSM methods to screen and improve de novo designed protein binders of LapG, a new route to disperse bacterial biofilms, a major source of antibiotic resistance. Toward this end, we screen the binding properties of about 100 top-ranked designs and choose around a dozen for expression, purification and assaying for binding. If successful, we will have avoided the need for time-consuming yeast display experiments, moving a step closer to a self-contained computational pipeline for generating custom protein binding interfaces, a potentially transformative tool in biology and medicine. We will evaluate methods for “in silico affinity maturation” against experimental data for a site-saturated library of all possible single-point mutations measured for our top-binding candidate.

项目概要 了解蛋白质及其结合伙伴的构象动力学对于预测至关重要 并设计它们的功能。随着计算机硬件和软件变得越来越高效，模拟 基于此的方法将在分子设计中发挥越来越重要的作用。马尔可夫状态模型 (MSM)，其中 将构象动力学描述为亚稳态之间的转变网络，可以用作 基于仿真的平台，用于预测和设计多个序列——适用于小扰动 保留状态定义，突变效应可以通过估计转换率的变化来推断——但是新的 必须开发方法来有效地做到这一点。我们将通过开发新方法来应对这一挑战 有效地对多个序列的 MSM 进行采样，然后应用该技术来预测和设计结合 肽模拟物的亲和力和比率，这一领域将对人类健康产生广泛的益处。 我们的第一个具体目标是开发两种分析工具，以促进对多种物质的 MSM 进行有效估计 序列：（1）基于意外的自适应采样，它对两个或多个 MSM 使用相对熵度量 优先对最有效降低模型不确定性的状态进行采样，以及（2）最大- 直接从州人口变化推断 MSM 转变率变化的口径方法。 这些方法将针对稳定性和折叠率的变化进行测试，这些变化是针对良好的语料库测量的。 利用可用的轨迹数据研究了微型蛋白质。 我们的第二个具体目标是应用这项技术来预测结合亲和力、途径和速率 MDM2 的拟肽配体，一种经过充分研究的蛋白质-肽结合系统和重要的癌症靶标。 我们将构建 apo-MDM2 的 MSM，以探索 N 端盖区域在配体结合中的作用，以及 MSM 衍生受体整体在计算药物设计中的实用性。然后我们将构建一个 MSM p53 与 MDM2 结合，并将其用作构建配体结合的多整体 MSM 的起点 系列相关的小分子、肽和螺寡聚肽模拟物，其目标是实现 有效估计亲和力以及具有约束力的结合率和解离率。 我们的第三个具体目标是与 David Baker 实验室合作，使用 MSM 方法来筛选和 改进从头设计的 LapG 蛋白结合剂，这是分散细菌生物膜的主要来源的新途径 抗生素耐药性。为此，我们筛选了约 100 个排名靠前的设计的装订属性 并选择大约十几个进行表达、纯化和结合分析。如果成功的话，我们将有 避免了耗时的酵母展示实验，向独立的方向迈进了一步 用于生成定制蛋白质结合界面的计算管道，这是一种潜在的变革工具 生物学和医学。我们将根据实验数据评估“计算机亲和力成熟”方法 为我们的顶部结合候选者测量的所有可能的单点突变的位点饱和库。