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: 10623409
• 负责人: Yingkai Zhang
• 金额: $ 58.89万
• 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 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10623409
• 关键词: Affinity; Area; Binding; Biological; Biological Process; Biology; Biophysics; Cell Death Induction; Chemicals; Collaborations; Disease; Docking; Drug Design; Drug Targeting; EP300 gene; Goals; Graph; Hydrogen Bonding; Lead; Learning; Length; Life; Ligands; MDM2 gene; Machine Learning; Medical; Methods; Molecular; Nature; Pathway interactions; Property; Proteins; Regulation; Research; Signal Pathway; Signal Transduction; Surface; Tertiary Protein Structure; Testing; Therapeutic Intervention; Therapeutic Uses; Virus; alpha helix; chemical reaction; computerized tools; deep learning model; design; drug development; drug discovery; frontier; improved; inhibitor; innovation; insight; laboratory experiment; machine learning model; mimetics; molecular modeling; novel; programs; protein function; protein protein interaction; rational design; small molecule; success; therapeutic development; therapeutic target; tool; unnatural amino acids; virtual screening

Abstract The overall goal of my research program is to develop and apply state-of-the-art machine learning and molecular modeling 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 biological networks, and their mis-regulations lead to diseases states. Thus PPIs are biologically compelling targets for drug discovery. Despite a few notable successes, most PPIs have not been successfully targeted and remain challenging for therapeutic intervention. 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. One of the most promising PPI inhibitor discovery strategies 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. In last five years, we have significantly advanced a pocket-guided rational design approach based on AlphaSpace to tackle this challenge. We have successfully optimized a PDM to target the KIX domain of the p300/CBP coactivator by introducing non-natural amino acids to improve pocket-fragment binding; rationally designed a novel NEMO coiled coil mimic that disrupts virus-induced NF-κB signaling and induces cell death; and successfully targeted a new binding pocket on MDM2 and MDMX with a potent dual inhibitor by elaborating hydrogen-bond stabilized alpha-helix mimetics. Meanwhile, we have developed state-of-the-art scoring functions for protein-ligand docking as well as virtual screening, advanced deep learning models to predict molecular properties and chemical reactions, and established strong and fruitful collaborations with several outstanding experimental labs in chemical biology and biophysics to discover new modulators of biomolecular interactions. These significant advances set the stage for us to further push the frontier of integrating machine learning and molecular modeling for rational drug design. Our focus in the next few years will be to establish a robust pocket-guided design platform based on AlphaSpace and machine learning for PPI orthosteric inhibitor optimization, provide physical/chemical insights and develop novel computational strategies for allosteric modulator discovery, and explore chemical space with deep sequence/graph/geometric representation learning for multi-objective molecular design. Our modulator design efforts in close collaborations with our experimental colleagues will not only rigorously test predictive power of our developed methods in real life applications, but also result in highly specific and potent modulators towards several important but challenging therapeutic targets, providing new leads for drug development.

摘要 我的研究计划的总体目标是开发和应用最先进的机器学习和 有助于合理设计重要细胞通路调节器的分子建模工具 治疗用途。蛋白质相互作用(PPI)是细胞信号和生物过程中的核心因素 网络，以及他们的不当监管导致了疾病状态。因此，PPI是生物上令人信服的目标 药物发现。尽管取得了一些显著的成功，但大多数PPI没有成功地确定目标，仍然是 对治疗干预具有挑战性。根本的挑战来自于它们的内在结构 特点：许多PPI的结合面一般面积大、平坦、动态。PPI通常是 瞬变的，涉及多价接触。最有前景的PPI抑制剂发现策略之一是 使用微型蛋白质结构域模拟(PDMS)来复制自然利用的关键界面接触。 PDM作为中等大小的分子具有很高的表面互补性和更广泛的 接触点比典型的小分子更多，但仍然有限，因为-根据定义-只有一部分 总的PPI结合能在相互作用中被捕获。合成域的结合亲和力通常是 低于同源全长蛋白质。在过去的五年里，我们已经显著地推进了口袋引导 基于AlphaSpace的理性设计方法来应对这一挑战。我们已经成功地优化了 PDM通过引入非天然氨基酸来针对p300/CBP共激活子的KIX结构域 口袋片段结合；合理设计了一种新型的NEMO螺旋线圈模拟物，可破坏病毒诱导的NF-κB 并成功地以MDM2和MDMX上的一个新的结合口袋为靶点 通过阐述氢键稳定的α-螺旋模拟物，有效的双重抑制剂。与此同时，我们有 开发了最先进的蛋白质-配体对接评分功能以及虚拟筛选功能，高级 用于预测分子性质和化学反应的深度学习模型，并建立了强大的 与化学生物学和生物物理学领域的几个优秀实验实验室进行了卓有成效的合作，以发现 生物分子相互作用的新调节器。这些重大进展为我们进一步推动 将机器学习和分子建模结合起来进行合理药物设计的前沿。我们将重点放在 未来几年将建立一个基于AlphaSpace和MACHINE的强大的口袋导引设计平台 学习PPI正构体抑制剂优化，提供物理/化学见解并开发新的 发现变构调节剂的计算策略，并深入探索化学空间 用于多目标分子设计的序列/图形/几何表示学习。我们的调制器设计 与我们的实验同事密切合作的努力不仅将严格测试 我们开发的方法在现实生活中的应用，但也导致高度特异性和强大的调节器，以 几个重要但具有挑战性的治疗靶点，为药物开发提供了新的线索。