EnzyDock-based Multistate and Multiscale Tools for Covalent Drug Design

项目概要/摘要 近年来，FDA批准了越来越多的与靶生物共价连接的药物， 分子。为了扩大共价抑制剂的发展，技术更具体的发现， 需要这样的抑制剂。还必须解决有关场外反应性和毒性的问题 与共价药物有关。该提案的重点是开发多尺度的计算机共价键， 通过整合稳健的量子力学和分子力学（QM/MM）势的对接方法 与EnzyDock对接平台，从而实现多步化学事件及其 在寻找对接姿势的过程中做出了巨大的贡献。目前的对接方法缺乏以下能力： 以与抑制剂的前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合模式一样。这种欲望不仅阻碍了 对弹头-目标反应性的基本理解，但也构成了计算机技术进步的技术障碍 对接策略事实上，许多现有的对接程序提供了进行共价对接的能力， 以特别的方式，因为共价对接在程序开发的设计阶段没有被考虑。 为了克服这一技术挑战，有两个具体目标：AIM 1是开发一个多尺度的 QM/MM/EnzyDock共价对接方法。在这项开发中，酶码头将作为主要的对接， 将开发平台和可靠的半经验QM/MM势，并针对每一种特定的弹头进行校准， 靶向反应类型并与EnzyDock结合。此外，我们还将制定和实施 玻恩（GB）溶剂化模型与QM/MM势框架，以改善QM/MM对接的能量 摆姿势。AIM 2将应用AIM 1中开发的QM/MM/EnzyDock方法建立有效的工作流程， 大的共价抑制剂数据库的计算机筛选。具体而言，将探讨两个工作流程： 工作流程基于与预定义的共价连接位点对接，这在大多数共价连接中被采用。 对接程序。第二个工作流程需要共价对接的动态方法，其中共价对接是一个动态过程。 在对接过程中，使用化学信息学在运行中搜索和确定配体上的连接位点 分析和与结合口袋中的靶残基的空间接近性。在本研究中，研究将是有限的 弹头只与半胱氨酸残基反应，而其他目标残基，反应类型和 在未来的研究中，将考虑弹头，以构建一个更全面的弹头-目标反作用系统 数据库因此，这两个工作流程将根据已知结构进行测试和基准测试， 药物-Cys共价系统的动力学/热力学数据。我们希望在这个项目中开发的方法 将使计算机共价抑制剂的发现更加强大，并有助于了解亲电靶点 用于弹头设计和选择。

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.