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

基本信息

批准号：
10708149
负责人：
Vincent Voelz
金额：
$ 36.83万
依托单位：
TEMPLE UNIV OF THE COMMONWEALTH
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-05-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10708149
关键词：
Address Affinity Binding COVID-19 screening Collaborations Colorado Computer Hardware Computing Methodologies Coupled Coupling Cyclic Peptides Data Dimerization Dissociation Drug resistance Fox Chase Cancer Center Free Energy Funding Health Home Human Joints Ligand Binding Ligands Macrolides Malignant Neoplasms Methods Modeling Molecular Molecular Conformation Muramidase Mutation Natural Products Neoplasm Metastasis PTEN gene Pathway interactions Peptides Physics Population Protease Inhibitor Protein Dynamics Proteins Publishing Reaction Research Role Sampling Series Structure System Testing Therapeutic Thermodynamics Time Toy Tumor Suppressor Proteins Universities Variant Work cluster computing computational platform coronavirus disease coronavirus pandemic cost crowdsourcing design drug discovery improved inhibitor machine learning classifier markov model models and simulation mutant novel diagnostics open data protein folding receptor restraint simulation tool virtual screening

项目摘要

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.

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和预测至关重要。设计它们的功能。分子模拟适合这项任务，但对于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，将构象动力学描述为之间的转变网络亚稳态，以应对这一挑战。多尺度马尔可夫模型 (MEMM) 提供了强大的框架用于从短轨迹集合中构建长时间尺度上动态的变分最优模型在有偏差的热力学整体中采样，以预测配体结合亲和力、速率和机制。疫情期间，我们组使用了分布式计算平台Folding@home（FAH）利用扩展集合 (EE) 对 SARS-CoV-2 主要蛋白酶抑制剂进行虚拟筛选模拟，其中可以在单个模拟中对多个炼金中间体进行采样，以估计结合自由能。这启发我们将 EE 和 MSM 方法结合起来，利用 FAH 将在虚拟筛选和分子设计方面取得根本性进展，以实现三个具体目标：我们的首要目标是改进计算配体结合自由能的 EE 方法。与合作衬衫实验室，我们寻求理解和改善融合问题，并探索和统一相关的接近。我们将研究突变自由能的 EE 估计与 MEMM 的使用效果如何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 可以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动力学 (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metaD来导出负电位沿结合反应坐标的平均力，可用作构建多点的偏置电位配体结合的整体马尔可夫模型（MEMM）。我们将在玩具绑定系统中测试这些方法，并且 L99A 溶菌酶的小配体。最后，我们将应用 metaD+MEMM 来预测亲和力、比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检查溶液状态预组织决定结合亲和力的程度，以及基于仿真的建模是否可以定量地使用这个想法进行计算设计。对于一个具有已公布的亲和力的 105 个环肽的语料库，EE+MSM 方法将测试两步法的有效性构象选择模型。这项工作的结果将指导循环系统的设计、测试和优化。与荣盛合作，利用肽结合物破坏肿瘤抑制因子 PTEN 的二聚化天普大学的王实验室致力于寻找癌症转移的新诊断/治疗方法。

项目成果

期刊论文数量（21）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Expanded Ensemble Methods Can be Used to Accurately Predict Protein-Ligand Relative Binding Free Energies.

扩展集成方法可用于准确预测蛋白质-配体相对结合自由能。

DOI：
发表时间：
2021-10-12
期刊：
影响因子：
0
作者：
Zhang, Si;Hahn, David F;Shirts, Michael R;Voelz, Vincent A
通讯作者：
Voelz, Vincent A

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors.

有效的非共价 SARS-CoV-2 主要蛋白酶抑制剂的开放科学发现。

DOI：
发表时间：
2023-11-10
期刊：
影响因子：
0
作者：
Boby, Melissa L;Fearon, Daren;Ferla, Matteo;Filep, Mihajlo;Koekemoer, Lizbé;Robinson, Matthew C;COVID Moonshot Consortium‡;Chodera, John D;Lee, Alpha A;London, Nir;von Delft, Annette;von Delft, Frank;Achdout, Hagit;Aimon, Anthony;Alonzi, Dom
通讯作者：
Alonzi, Dom

Fluorinated Aromatic Monomers as Building Blocks To Control α-Peptoid Conformation and Structure.

氟化芳香族单体作为控制α-类肽构象和结构的构建模块。