Markov State Model approaches for folding, binding and design

项目摘要 了解蛋白质及其结合伴侣的构象动力学对于预测和 设计其功能。分子模拟适用于这一任务，但对于配体仍然具有挑战性 解离在非常慢的时间尺度上发生的结合系统。我们正在开发新的马尔可夫状态 模型（MSM）方法，它描述了构象动力学之间的转换网络， 亚稳态，来应对这一挑战。多尺度马尔可夫模型（MEMM）提供了一个强大的框架 用于从短轨迹的集合中建立长时间尺度上的变分最优动力学模型 在偏置热力学集合中采样，以预测配体结合亲和力、速率和机制。 在冠状病毒大流行期间，我们的团队使用分布式计算平台Folding@home（FAH） 利用扩展系综（EE）进行SARS-CoV-2主要蛋白酶抑制剂的虚拟筛选 模拟，其中多个炼金术中间体可以在一个单一的模拟采样，以估计 束缚自由能这启发我们将联合收割机EE和MSM方法结合起来， FAH将在虚拟筛选和分子设计方面取得根本性进展，具体目标如下： 我们的第一个目标是改进EE方法计算配体结合自由能。联同 衬衫实验室，我们寻求理解和改善融合问题，并探索和统一相关的 接近。我们将研究如何以及EE估计的自由能突变可以与MEMM使用 来预测蛋白质折叠稳定性和速率的变化。最后，我们将与Karanicolas实验室合作， 确定FAH上EE计算的ABFE可与先进机器一起使用的程度 学习分类器，从基于结构的虚拟筛选研究中发现活性和有效的抑制剂。 我们的第二个目标是开发一种组合元数据模型（metaD）+ MEMM方法来建模绑定 反应.我们将开发和测试两种不同的策略，其中使用meta D来推导负电位 的平均力沿着结合反应坐标，可以用作偏置电位，用于构建多- 配体结合的整体马尔可夫模型（MEMM）。我们将在玩具装订系统中测试这些方法， L99 A溶菌酶的小配体。最后，我们将应用meta D + MEM来预测亲和力、速率和 大环内酯类天然产物carolacton与FolD及其已知耐药突变体结合的机制。 我们的第三个目标是研究溶液态预组织在多大程度上决定了结合亲和力， 以及基于仿真的建模是否可以将这种思想定量地用于计算设计。用于 语料库的105个环肽与公布的亲和力，EE+MSM方法将测试的有效性，两步 构象选择模型本文的研究结果对循环流化床的设计、试验和优化具有指导意义 肽结合剂破坏肿瘤抑制因子PTEN的二聚化，与荣盛合作 王实验室在坦普尔，寻找新的诊断/治疗癌症转移。

Project Summary Understanding the conformational dynamics of proteins and their binding partners is crucial to predicting and designing their function. Molecular simulations are suitable for this task, but remain challenging for ligand binding systems where dissociation occurs on very slow time scales. We are developing new Markov state model (MSM) approaches, which describe conformational dynamics as a network of transitions between metastable states, to address this challenge. Multi-scale Markov models (MEMMs) offer a robust framework for building variationally optimal models of dynamics on long time scales, from ensembles of short trajectories sampled in biased thermodynamic ensembles, to predict ligand binding affinities, rates and mechanisms. During the coronavirus pandemic, our group used the distributed computing platform Folding@home (FAH) to perform virtual screening of SARS-CoV-2 main protease inhibitors by utilizing expanded-ensemble (EE) simulations, in which multiple alchemical intermediates can be sampled in a single simulation, to estimate binding free energies. This has inspired us to combine EE and MSM methods that can leverage the power of FAH to make fundamental advances in virtual screening and molecular design, in three specific aims: Our first aim is to improve EE methods for computing ligand binding free energies. In collaboration with the Shirts Lab, we seek to understand and ameliorate convergence issues, and explore and unify related approaches. We will investigate how well EE estimates of free energies of mutations can be used with MEMMs to predict changes in protein folding stability and rates. Finally, we will work with the Karanicolas Lab to determine the extent to which EE-calculated ABFEs on FAH can be used alongside advanced machine learning classifiers to discover both active and potent inhibitors from structure-based virtual screening studies. Our second aim is to develop a combined metadynamics (metaD) + MEMM approach for modeling binding reactions. We will develop and test two different strategies in which metaD is used to derive negative potentials of mean force along binding reaction coordinates that can be used as bias potentials for constructing multi- ensemble Markov models (MEMMs) of ligand binding. We will test these methods in toy binding systems, and small ligands of L99A lysozyme. Finally, we will apply metaD+MEMMs to predict affinities, rates and mechanisms of the macrolide natural product carolacton binding to FolD and its known drug-resistant mutants. Our third aim is to examine the extent to which solution-state preorganization determines binding affinity, and whether simulation-based modeling can use this idea quantitatively for computational design. For a corpus of 105 cyclic peptides with published affinities, EE+MSM approaches will test the validity of a two-step conformational selection model. The results of this work will guide the design, testing and optimization of cyclic peptide binders to disrupt dimerization of the tumor suppressor PTEN, a collaboration with the Rongsheng Wang Lab at Temple, to find new diagnostics/therapeutics for cancer metastasis.