Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation.
Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation.
复制标题
使用通过配体竞争饱和进行位点识别的集成共价药物设计工作流程。
DOI：
10.1021/acs.jctc.3c00232
复制
发表时间：
2023
影响因子：
5.5
通讯作者：
MacKerellJr,AlexanderD
中科院分区：
文献类型：
--
作者：
Yu,Wenbo;Weber,DavidJ;MacKerellJr,AlexanderD
共价药物设计是药物发现的重要组成部分。传统药物以可逆平衡方式与其靶标相互作用，而不可逆共价药物通过与靶残基形成共价键来延长药物与靶标相互作用的持续时间，从而可能提供更有效的治疗方法。为了促进此类配体的设计，可以使用计算方法来帮助识别共价结合靶蛋白上的反应性亲核残基（通常是半胱氨酸），测试各种弹头基团的潜在反应性，并预测对结合的非共价贡献，这可以促进对结合特异性很重要的药物-靶点相互作用。为了进一步帮助共价药物设计，我们扩展了基于显式溶剂全原子分子模拟（SILCS：通过配体竞争饱和进行位点识别）的官能团作图方法，该方法本质上考虑了蛋白质灵活性、官能团和蛋白质去溶剂化以及官能团-蛋白质相互作用。通过使用 SILCS-Monte Carlo (SILCS-MC) 对接代表性弹头碎片库，可以正确识别正在测试的蛋白质的反应性半胱氨酸。此外，还训练了一个机器学习模型，以使用 SILCS-MC 的指标以及实验模型复合弹头反应数据来量化各种弹头组对蛋白质的有效性。还针对多种蛋白质测试了使用 SILCS 配体网格自由能 (LGFE) 排名对具有类似弹头的共价分子结合剂进行排名的能力。基于这些工具，开发了一个基于 SILCS 的集成工作流程，名为 SILCS-Covalent，它可以定性和定量地为共价药物发现提供信息。
Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug–target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug–target interactions that are important for binding specificity. To further aid covalent drug design, we extended a functional group mapping approach based on explicit solvent all-atom molecular simulations (SILCS: site identification by ligand competitive saturation) that intrinsically considers protein flexibility, functional group, and protein desolvation along with functional group–protein interactions. Through docking of a library of representative warhead fragments using SILCS-Monte Carlo (SILCS-MC), reactive cysteines can be correctly identified for proteins being tested. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for proteins using metrics from SILCS-MC as well as experimental model compound warhead reactivity data. The ability to rank covalent molecular binders with similar warheads using SILCS ligand grid free energy (LGFE) ranking was also tested for several proteins. Based on these tools, an integrated SILCS-based workflow was developed, named SILCS-Covalent, which can both qualitatively and quantitatively inform covalent drug discovery.