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.

项目概要/摘要 近年来，FDA 批准了越来越多与靶标生物共价连接的药物 为了扩大共价抑制剂的开发，需要更具体的技术来发现 还需要解决有关场外反应性和毒性的问题。 该提案的特别重点是开发多尺度计算机共价药物。 通过集成强大的量子力学和分子力学 (QM/MM) 潜力的对接方法 与 EnzyDock 对接平台一起，从而能够对多步骤化学事件及其结果进行显式建模 当前的对接方法缺乏在搜索对接姿势期间的能量贡献。 以与抑制剂的预共价结合模式一致的方式进行共价键形成，以及 与反应过渡态和共价键模式一样，这种想要的能力不仅阻碍了反应。 对弹头目标反应性的基本了解，但也对计算机模拟的推进构成了技术障碍 事实上，许多现有的对接程序提供了进行共价对接的能力，但是 以一种临时的方式，因为从程序开发的设计阶段就没有考虑共价对接。 为了克服这一技术挑战，有两个具体目标： AIM 1 是开发一种多尺度 QM/MM/EnzyDock 共价对接方法在此开发中，EnzyDock 将作为主要对接。 将开发平台和强大的半经验 QM/MM 潜力，并针对每个特定弹头进行校准 目标反应类型并与EnzyDock结合此外，我们将开发并实现通用化。 具有 QM/MM 势框架的 Born (GB) 溶剂化模型可改善 QM/MM 对接的能量学 AIM 2 将应用 AIM 1 中开发的 QM/MM/EnzyDock 方法来建立有效的工作流程。 具体来说，将探索两个工作流程：第一个。 工作流程基于与预定义的共价连接位点的对接，该位点在大多数共价连接中都采用 第二个工作流程需要动态的共价对接方法，其中共价对接。 使用化学信息学在对接过程中动态搜索和确定配体上的附着位点 在本研究中，研究将受到限制。 只与半胱氨酸残基反应的弹头，而其他目标残基、反应类型和 未来的研究将考虑弹头，以构建更全面的弹头-目标反应 因此，这两个工作流程将根据已知的结构和基准进行测试和基准测试。 我们期望本项目中开发的方法能够获得药物-Cys 共价系统的动力学/热力学数据。 将使计算机共价抑制剂的发现更加强大，并有助于理解亲电靶标 用于弹头设计和选择的反应性。