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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：
10446465
负责人：
Vincent Voelz
金额：
$ 39.76万
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/10446465
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 Lead 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 Publishing Reaction Research Role Sampling Series Structure System Testing Therapeutic Thermodynamics Time Toy Tumor Suppressor Proteins Universities Variant Work base cluster computing computational platform coronavirus disease cost crowdsourcing design drug discovery improved inhibitor machine learning classifier markov model models and simulation mutant novel diagnostics open data pandemic coronavirus protein folding receptor 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.

项目总结