Computational modulator design and machine learning to target protein-protein interactions

针对蛋白质-蛋白质相互作用的计算调节器设计和机器学习

• 批准号: 10152659
• 负责人: Yingkai Zhang
• 金额: $ 55.68万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10152659
• 关键词: Affinity; Area; Autoimmune Diseases; Binding; Binding Proteins; Biological; Computing Methodologies; Development; Diabetes Mellitus; Disease; Docking; Drug Targeting; Gene Expression Regulation; Goals; Length; Ligands; Machine Learning; Malignant Neoplasms; Methods; Modeling; Nature; Neurodegenerative Disorders; Pathway interactions; Performance; Process; Proteins; Research; Signal Transduction; Specificity; Structure; Surface; Tertiary Protein Structure; Therapeutic; Therapeutic Uses; Toxic effect; Work; computerized tools; design; drug development; drug discovery; improved; inhibitor/antagonist; learning strategy; mimetics; molecular dynamics; molecular modeling; multitask; novel; programs; protein protein interaction; screening; small molecule; success; therapeutic target; tool

Abstract The overall goal of my research program is to develop and apply computational tools to facilitate the rational design of modulators of important cellular pathways for therapeutic use. Protein-protein interactions (PPIs) are central factors in cellular signaling and gene regulation networks. Their misregulation is associated with a variety of diseases, including cancer, neurodegenerative disease, autoimmune disease, and diabetes. Inevitably, many PPIs are biologically compelling targets for drug discovery. But despite a few notable successes, most PPIs have not been successfully targeted and remain undruggable. The fundamental challenge derives from their intrinsic structural features: the binding surfaces of many PPIs are generally large in area, flat, and dynamic. PPIs are often transient and involve multivalent contacts. Currently, one most promising PPI inhibitor discovery strategy is to use miniature protein domain mimetics (PDMs) to reproduce the key interface contacts utilized by nature. PDMs are advantageous as medium-sized molecules with high surface complementarity and a broader set of contact points than typical small molecules, but are still limited because—by definition—only a portion of the total PPI binding energy is captured in the interaction. The binding affinity of the synthetic domains is often lower than the cognate full-length proteins. On the other hand, targeted covalent inhibition is an orthogonal therapeutic approach fit to overcome the fundamental binding limitations at PPIs, but has a well-known drawback: the high reactivity of typical covalent warheads leads to nonspecific inhibition, and toxicity. Here we aim to develop computational methods for a new design strategy that will leverage the strengths of these two methods—PDMs and covalent inhibition—while simultaneously mitigating their respective limitations. The focus of the effort is to rationally discover potent inhibitors that will non-covalently recognize and then covalently target protein-protein binding interfaces with exquisite specificity. Furthermore, our development of robust scoring functions by integrating multitask machine learning and molecular modeling would significantly accelerate the rational drug discovery process. The planned work builds on our recent advances in three state-of-the-art computational approaches: AlphaSpace for fragment- centric topographical mapping of PPI interfaces; ab initio QM/MM molecular dynamics for modeling covalent inhibition; and a novel delta-machine learning strategy to simultaneously improve scoring, docking and screening performance of a protein-ligand scoring function. Our design efforts will result in highly specific and potent modulators of a variety of therapeutically important but previously undruggable PPI interfaces, providing new leads for drug development.

抽象的 我的研究计划的总体目标是开发和应用计算工具来促进理性 设计用于治疗用途的重要细胞途径的调节剂。蛋白质-蛋白质相互作用（PPI）是 细胞信号传导和基因调控网络的核心因素。他们的监管不当与 多种疾病，包括癌症、神经退行性疾病、自身免疫性疾病和糖尿病。 许多 PPI 不可避免地成为药物发现的生物学上引人注目的目标。但尽管有一些值得注意的 尽管取得了成功，但大多数 PPI 尚未成功靶向并且仍然无法成药。基本的 挑战源自其内在的结构特征：许多质子泵抑制剂的结合表面通常很大 面积、平面和动态。 PPI 通常是暂时的并且涉及多价接触。目前，最 有前景的 PPI 抑制剂发现策略是使用微型蛋白结构域模拟物 (PDM) 来重现 大自然利用的关键接口触点。 PDM 作为中等大小的分子具有较高的优势 表面互补性和比典型小分子更广泛的接触点，但仍然有限 因为根据定义，只有总 PPI 结合能的一部分在相互作用中被捕获。这 合成结构域的结合亲和力通常低于同源全长蛋白质。另一方面， 靶向共价抑制是一种正交治疗方法，适合克服基本结合 PPI 的局限性，但有一个众所周知的缺点：典型共价弹头的高反应性导致 非特异性抑制和毒性。在这里，我们的目标是开发新设计策略的计算方法 这将利用这两种方法（PDM 和共价抑制）的优势，同时 减轻各自的限制。努力的重点是合理地发现有效的抑制剂 非共价识别然后以精确的特异性共价靶向蛋白质-蛋白质结合界面。 此外，我们通过集成多任务机器学习和开发强大的评分功能 分子建模将显着加速合理的药物发现过程。计划的工作 建立在我们最近在三种最先进的计算方法方面取得的进展之上：用于片段的 AlphaSpace PPI 界面的中心地形图；用于共价建模的从头开始 QM/MM 分子动力学 抑制;以及一种新颖的增量机器学习策略，可同时提高评分、对接和 蛋白质配体评分函数的筛选性能。我们的设计工作将带来高度具体和 多种治疗上重要但以前不可成药的 PPI 界面的有效调节剂，提供 药物开发的新线索。