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和 设计他们的功能。分子模拟适合于这项任务，但对配体来说仍然具有挑战性。 解离发生在非常慢的时间尺度上的结合系统。我们正在开发新的马尔可夫状态 模型(MSM)方法，它将构象动力学描述为 亚稳态，以应对这一挑战。多尺度马尔可夫模型(MEMM)提供了一个健壮的框架 用于从短轨迹集合建立长时间尺度上的变分最优动力学模型 在偏向热力学系综中采样，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，我们团队使用分布式计算平台Folding@Home(FAH) 利用扩展集合(EE)进行SARS-CoV-2主要蛋白酶抑制剂的虚拟筛选 模拟，其中可以在一次模拟中对多个炼金术中间体进行采样，以估计 结合自由能。这激发了我们将EE和MSM方法结合起来，这些方法可以利用 FAH将在虚拟筛选和分子设计方面取得根本性进展，具体目标有三： 我们的第一个目标是改进计算配体结合自由能的EE方法。与 衬衫实验室，我们寻求了解和改善融合问题，并探索和统一相关问题 接近了。我们将研究突变自由能的EE估计在多大程度上适用于MEMM 以预测蛋白质折叠稳定性和速率的变化。最后，我们将与卡拉尼科拉斯实验室合作 确定FAH上的EE计算的ABFE可以在多大程度上与先进机器一起使用 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用于建模绑定的组合元动力学(MetaD)+MEMM方法 反应。我们将开发和测试两种不同的策略，在这些策略中使用MetaD来推导负电位 沿结合反应坐标的平均作用力，可用作构建多个 配基结合的系综马尔可夫模型。我们将在玩具捆绑系统中测试这些方法，以及 L99A溶菌酶的小配体。最后，我们将应用MetaD+MEMMS来预测亲和力、比率和 大环内酯天然产物Carolacton与Fold的结合机制及其已知的耐药突变体。 我们的第三个目标是检查解决方案状态前组织决定绑定亲和力的程度， 以及基于仿真的建模是否可以将这一思想定量地用于计算设计。为. 由105个具有已公布亲和力的环肽组成的语料库，EE+MSM方法将测试两步法的有效性 构象选择模型。本文的工作结果将对循环汽车的设计、试验和优化具有指导意义。 与荣盛的合作，破坏肿瘤抑制基因PTEN的二聚化的多肽结合物 为癌症转移寻找新的诊断/治疗方法。

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.