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Revealing the Ligand Binding Landscape with Advanced Molecular Simulation Methods

利用先进的分子模拟方法揭示配体结合景观

基本信息

批准号：
10166872
负责人：
Alexander Dickson
金额：
$ 33.49万
依托单位：
MICHIGAN STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-20 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10166872
关键词：
Affect Affinity Algorithms Binding Biophysics Cells Chemicals Cloning Control Groups Coupled Development Diffusion Docking Drug Design Drug Kinetics Drug Modelings Drug Targeting Epoxide hydrolase Equilibrium Event Free Energy Freedom G-Protein-Coupled Receptors Human body Ingestion Intake Kinetics Knowledge Laboratories Lead Ligand Binding Ligands Metabolism Methods Modeling Molecular Molecular Conformation Motion Organism Outcome Pathway interactions Pharmaceutical Preparations Pharmacologic Substance Process Property Protein Biosynthesis Protein Dynamics Proteins Running Sampling Screening procedure Series Structural Models System Techniques Technology Testing Therapeutic Time Tissues Uncertainty Work base cell growth computational pipelines design diabetic patient driving force drug action drug discovery drug efficacy druggable target empowered experimental study fitness improved inhibitor/antagonist insight molecular dynamics novel painful neuropathy pharmacophore protein degradation residence screening simulation success time use tool virtual screening

项目摘要

Project Summary Human bodies are living systems that are constantly in flux. Pharmaceutical drugs take action within this non-equilibrium context: after a drug is ingested it is absorbed, distributed to tissues, bound (both on-target and off-target), released, metabolized and eliminated. Each of these processes occurs with a rate, and the efficacy of a drug is a largely function of these rates. In contrast, the dominant paradigm in drug discovery has been the optimization of affinity, which alone is insufficient to determine the rates of binding (kon) and unbinding (koff). Though the binding affinity is the ratio of the koff and kon, and longer residence times can lead to higher binding affinity, these are not well-correlated, as kon values can vary from diffusion-limited (109 M-1 s-1) down to <104 M-1 s-1 for protein targets with slow degrees of freedom, such G-protein-coupled receptors. Prediction of affinity is easier than kinetics as it is a state function, which depends only on the endpoints of the binding path. Binding kinetics are dependent on the molecular details encoded in the ligand binding transition state – the highest point in free energy along the binding pathway. Molecular dynamics simulation can be used to study these transition states in atomic detail, but only recently – empowered by advances in hardware and new algorithms for simulation – has it become capable of simulating unbiased ligand binding and release events, which can be coupled to long timescale protein motions. As such, little is known about the ligand binding transition state for a given protein target, and how it changes from ligand to ligand. Empowered by the WExplore enhanced sampling method (developed by the PI), the Dickson laboratory will use molecular dynamics simulation to reveal the landscape of protein-ligand conformations. WExplore can generate extremely rare ligand release pathway ensembles (events occurring only once in ~1000 seconds) without the use of biasing forces, which is a dramatic improvement upon current technology. Importantly, this will enable analysis of the ligand binding transition states for a series of ligands on two protein drug targets (soluble epoxide hydrolase (sEH), and Translocator protein 18kDA (TSPO)). This will mark the first study of the robustness of ligand binding transition states, which is a key quantity for kinetics-based drug design. Further, this work will build a method to encode properties of the transition state into screening tools that can, for the first time, screen ligands according to kinetics in a high-throughput manner. These methods will then be applied to identify new long residence time inhibitors for both sEH and TSPO, two systems where residence time has been shown to be important for drug efficacy.

项目总结