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物理地面的生物分子及其相互作用的模型 在蛋白质组（基因组）量表上应用这些模型以解决人类系统生物学中的基本问题 信号。在建模方面，我们的努力将集中于构建蛋白质配体的计算模型 相互作用，特别强调细胞在信号传导中广泛采用的翻译后修饰的配体 网络。我假设蛋白质 - 配体相互作用模型的准确性和一般性的步骤改变是 使用蛋白质结构预测和蛋白质表示学习中的深度学习进展。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的可区分模型 蛋白质结构预测（RGN）；第一种蛋白质语言模型（UNIREP），这是一种学习的关键技术 捕获蛋白质的化学，结构和进化特性的数学表示；一个 蛋白质 - 蛋白质相互作用（HSM）的第一个深度学习方法。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 - 配体相互作用的域。我们将进一步 开发了用于预测蛋白质结构和替代蛋白质构象的专业模型 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来预测蛋白质 - 配体相互作用。 在生物学方面，我们将采用这些机器学习模型来组装特定于人的信号传导 网络了解如何在信号网络级别表现出正常的等位基因变化，以及如何表现 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差异，我们将使用 外显子序列（英国生物银行和NHLBI顶部）建立个性化网络，以绘制特定于人的特定网络 蛋白质 - 配体亲和力的蛋白质序列。我们将量化网络拓扑在个体之间的变化 人群并测试与疾病相关的性状是否与拓扑相关。我们还将比较 健康和疾病的人的网络，以识别偏爱个体的拓扑差异 遗传疾病。最终，我预计机器学习的模型将充分预测配体结合 对通过突变重新布线的通路的机械理解是可能的。虽然我的重点是 我希望在计算机上进行密切的合作 - 在Fordyce Lab（Stanford）实验 表征和验证蛋白质 - 配体相互作用和沉实验（哥​​伦比亚）以执行统计通用 分析 - 在计算和实验的界面上利用协同作用。