Machine learning of biomolecular interactions and the human signaling networks they comprise

生物分子相互作用及其组成的人类信号网络的机器学习

• 批准号: 10714785
• 负责人: Mohammed Nazar AlQuraishi
• 金额: $ 41.13万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-22 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714785
• 关键词: Address; Affinity; Alleles; Amino Acid Sequence; Biological; Cells; Chemicals; Collaborations; Computer Models; Disease; Genetic Diseases; Genome; Human; Human Biology; Individual; Information Networks; Language; Learning; Ligand Binding; Ligands; Machine Learning; Maps; Modeling; Mutation; National Heart, Lung, and Blood Institute; Pathogenicity; Pathway interactions; Persons; Population; Property; Protein Conformation; Proteins; Proteome; Research; Signal Transduction; Structure; Systems Biology; Techniques; Testing; Trans-Omics for Precision Medicine; Variant; biobank; computerized tools; deep learning; exome; genetic analysis; genomic data; human disease; information processing; learning strategy; machine learning model; mathematical learning; predictive modeling; programs; protein protein interaction; protein structure prediction; synergism; trait

My lab will use machine learning to build physically-grounded models of biomolecules and their interactions and apply these models at proteome (genome) scale to address basic questions in the systems biology of human signaling. On the modeling front, our efforts will focus on building computational models of protein-ligand interactions, with a specific emphasis on post-translationally modified ligands that cells widely employ in signaling networks. I hypothesize that a step change in accuracy and generality of protein-ligand interaction models is possible using deep learning advances in protein structure prediction and protein representation learning. My lab has been at the forefront of these advances, having developed the first end-to-end differentiable model of protein structure prediction (RGN); the first protein language model (UniRep), a key technique for learning mathematical representations that capture chemical, structural, and evolutionary properties of proteins; and one of the first deep learning methods for protein-protein interactions (HSM). We will leverage our expertise in these domains to predict protein-ligand interactions based on both sequence and structure information. We will further develop specialized models for predicting protein structures and alternate protein conformations for the purpose of predicting protein-ligand interaction, using these predictions as inputs for our protein-ligand interaction models. On the biological front, we will employ these machine-learned models to assemble person-specific signaling networks to understand how normal allelic variation is manifested at the level of signaling networks, and how these networks are perturbed in human diseases. To study general variation in signaling networks, we will use exome sequences (UK Biobank and NHLBI TOPMed) to build individualized networks that map person-specific protein sequences to protein-ligand affinities. We will quantify how network topology varies among individuals and populations and test whether disease-associated traits correlate with topology. We will also compare networks of healthy and disease-afflicted persons to identify topological differences that predispose individuals to genetic diseases. Ultimately, I expect machine-learned models to be sufficiently predictive of ligand binding that mechanistic understanding of pathway rewiring by mutations is possible. While my focus will be computational, I expect to carry out close collaborations—with the Fordyce Lab (Stanford) to experimentally characterize and validate protein-ligand interactions and the Shen Lab (Columbia) to perform statistical genetic analyses—to exploit synergies at the interface of computation and experimentation.

我的实验室将使用机器学习来构建生物分子及其相互作用的物理模型 在蛋白质组（基因组）规模上应用这些模型来解决人类系统生物学中的基本问题 发信号。在建模方面，我们的工作重点是建立蛋白质配体的计算模型 相互作用，特别强调细胞广泛用于信号传导的翻译后修饰配体 网络。我假设蛋白质-配体相互作用模型的准确性和通用性的阶跃变化是 利用蛋白质结构预测和蛋白质表示学习方面的深度学习进展成为可能。我的 实验室一直处于这些进步的最前沿，开发了第一个端到端可微分模型 蛋白质结构预测（RGN）；第一个蛋白质语言模型（UniRep），学习的关键技术 捕获蛋白质的化学、结构和进化特性的数学表示；和一个 第一个用于蛋白质-蛋白质相互作用（HSM）的深度学习方法。我们将利用我们在这些方面的专业知识 基于序列和结构信息预测蛋白质-配体相互作用的结构域。我们将进一步 为此目的开发专门的模型来预测蛋白质结构和替代蛋白质构象 预测蛋白质-配体相互作用，使用这些预测作为我们的蛋白质-配体相互作用模型的输入。 在生物学方面，我们将利用这些机器学习模型来组装特定于人的信号 网络来了解正常等位基因变异如何在信号网络水平上表现出来，以及如何 这些网络在人类疾病中受到干扰。为了研究信号网络的一般变化，我们将使用 外显子组序列（英国生物银行和 NHLBI TOPMed）用于构建映射个人特异性的个性化网络 蛋白质序列与蛋白质配体亲和力。我们将量化网络拓扑在个体之间的差异 和人群并测试疾病相关特征是否与拓扑相关。我们也会比较 健康人和患病者的网络，以识别导致个体易感的拓扑差异 到遗传病。最终，我希望机器学习模型能够充分预测配体结合 通过突变对通路重新布线的机械理解是可能的。虽然我的重点是 在计算方面，我希望与福代斯实验室（斯坦福大学）进行密切合作，进行实验 表征并验证蛋白质-配体相互作用，并由 Shen Lab（哥伦比亚）进行统计遗传学 分析——利用计算和实验界面的协同作用。