Markov State Model approaches for folding, binding and design

项目摘要 了解蛋白质及其结合伙伴的构象动力学对于预测 并设计了它们的功能。随着计算机硬件和软件变得越来越高效，模拟- 基于分子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)，它 将构象动力学描述为亚稳态之间的跃迁网络，可用作 基于模拟的平台，用于预测和设计多个序列-用于 保留状态定义，可以通过估计转换率的变化来推断突变效应-但这是新的 必须开发有效的方法来做到这一点。我们将通过开发新的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绑定 多肽类药物的亲和力和比率，这一领域将对人类健康产生广泛的好处。 我们的第一个具体目标是开发两个分析工具，促进对多个MSM的有效估计 序列：(1)基于惊喜的自适应采样，对两个或多个MSM使用相对熵度量 对最有效地减少模型中不确定性的状态抽样进行优先排序，以及(2)最大限度地- 直接从州人口变化推断男男性接触者转化率变化的口径方法。 这些方法将与稳定性和折叠率的变化进行测试，这些变化是在一组井的语料库中测量的。 利用现有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是一种研究广泛的蛋白质-多肽结合系统，也是重要的癌症靶点。 我们将构建apo-MDM2的MSM，以探索N-末端LID区域在配体结合中的作用，以及 MSM衍生受体系综在计算机药物设计中的应用。然后我们将构建一个MSM P53与MDM2结合，并以其为起点构建多个MSM的配体结合 一系列相关的小分子、多肽和螺环激元多肽类药物，目的是实现 高效的亲和力估计，以及具有约束力的上下限利率。 我们的第三个具体目标是与大卫·贝克实验室合作，使用MSM方法筛选和 改进LapG的重新设计的蛋白质结合剂，LapG是分散细菌生物膜的新途径，LapG是主要来源 抗生素耐药性。为此，我们筛选了大约100个排名靠前的设计的绑定属性 并选择大约12个进行表达、纯化和分析结合。如果成功，我们将拥有 避免了耗时的酵母展示实验，向自给自足的 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性工具 生物和医学。我们将根据实验数据对“电子亲和成熟”的方法进行评估。 一个包含所有可能的单点突变的位点饱和文库，为我们的最佳结合候选者测量。

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.