EnzyDock-based Multistate and Multiscale Tools for Covalent Drug Design

基于 EnzyDock 的多状态和多尺度共价药物设计工具

• 批准号: 10575904
• 负责人: Kwangho Nam
• 金额: $ 19.93万
• 依托单位: UNIVERSITY OF TEXAS ARLINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10575904
• 关键词: Address; Affinity; Algorithms; Benchmarking; Binding; Binding Sites; Biological; Calibration; Chemical Models; Chemicals; Complex; Consensus; Crystallography; Cysteine; Data; Databases; Development; Docking; Drug Design; Evaluation; Event; Family; Future; Goals; Intervention; Kinetics; Knowledge; Ligand Binding; Ligands; Link; Medicine; Methods; Modeling; Molecular; Molecular Conformation; Pharmaceutical Preparations; Phase; Process; Program Development; Property; Proteins; Quantum Mechanics; Reaction; Research; Site; Solvents; Structure; System; Technology; Temperature; Testing; Thermodynamics; Toxic effect; Training; Translating; chemical bond; chemical reaction; cheminformatics; computer studies; cost effective; covalent bond; density; design; drug discovery; enzyme mechanism; improved; in silico; inhibitor; interest; molecular mechanics; novel; off-target site; programs; screening; success; theories; tool

PROJECT SUMMARY/ABSTRACT In recent years, FDA has approved a growing number of drugs that are covalently linked to target biological molecules. To expand the development of covalent inhibitors, technologies more specific to the discovery of such inhibitors are needed. It is also necessary to address concerns regarding off-site reactivity and toxicity associated with covalent drugs. The particular focus of this proposal is to develop multiscale in silico covalent docking approaches by integrating robust quantum mechanical and molecular mechanical (QM/MM) potentials with the EnzyDock docking platform, thus enabling explicit modeling of multi-step chemical events and their energetic contributions during the search for docked poses. Current docking approaches lack the ability to perform covalent bond formation in a manner consistent with an inhibitor’s pre-covalent binding mode, as well as with the reaction transition state and covalently bonded mode. This wanting ability not only hampers the fundamental understanding of warhead-target reactivity, but also poses a technical barrier for advancing in silico docking strategies. Indeed, many existing docking programs offer the capacity to perform covalent docking but in an ad hoc fashion, as covalent docking was not considered from the design phase of the program development. With the goal to overcome this technical challenge, two specific aims are: AIM 1 is to develop a multiscale QM/MM/EnzyDock covalent docking method. In this development, EnzyDock will serve as the primary docking platform and robust semiempirical QM/MM potentials will be developed, calibrated for each specific warhead- target reaction type and combined with EnzyDock. In addition, we will develop and implement the generalized Born (GB) solvation model with the QM/MM potential framework to improve the energetics of QM/MM-docked poses. AIM 2 will apply the QM/MM/EnzyDock approach developed in AIM 1 to establish effective workflow for in silico screening of large covalent inhibitor databases. Specifically, two workflows will be explored: The first workflow is based on docking with a predefined covalent attachment site, which is employed in most covalent docking programs. The second workflow entails a dynamic approach to covalent docking, in which covalent attachment sites on the ligand are searched and determined on the fly during docking using cheminformatics analysis and spatial proximity with target residues in the binding pocket. In this research, the study will be limited to the warheads that react only with cysteine residues, while additional target residues, reaction types and warheads will be considered in future research to construct a more comprehensive warhead-target reaction database. Thus, the two workflows will be tested and benchmarked against known structures and kinetic/thermodynamic data of drug-Cys covalent systems. We expect that the methods developed in this project will make the in silico covalent inhibitor discovery more powerful and help understand electrophilic-target reactivity for use in warhead design and selection.

项目总结/文摘