Molecular modeling and machine learning for protein structures and interactions

蛋白质结构和相互作用的分子建模和机器学习

• 批准号: 10191763
• 负责人: Philip Bradley
• 金额: $ 13.13万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10191763
• 关键词: Adaptive Immune System; Algorithms; Antigens; Autoimmune Diseases; Binding; Binding Sites; Bioinformatics; Biology; Caliber; Catalytic Domain; Cell Therapy; Communicable Diseases; Complement; Complex; Computational algorithm; Data; Diagnostic; Goals; Health; Human; Immune system; Laboratories; Machine Learning; Malignant Neoplasms; Molecular; Molecular Machines; Organism; Peptide/MHC Complex; Process; Protein Engineering; Proteins; Research; Signal Transduction; Specificity; Structural Models; T-Cell Receptor; T-cell receptor repertoire; Tandem Repeat Sequences; Techniques; Time; Work; design; insight; lens; molecular modeling; prediction algorithm; protein folding; protein structure; rapid growth; scaffold; simulation; structural biology; tool

PROJECT SUMMARY / ABSTRACT Structural biology provides a powerful lens through which to view living systems. With advances in algorithms and computing, molecular simulations have begun to complement traditional experimental approaches as tools for discovery. At the same time, data-intensive machine learning approaches are becoming increasingly important in biology, fueled by the rapid growth in high-throughput experimentation. Research in my laboratory applies techniques from structural biology, molecular simulation, and machine learning to design new protein structures and predict protein interactions. We design new protein structures in order to better understand the principles of protein folding and to create highly stable and robust molecular scaffolds for a range of biomedical applications including multivalent display of binding or signaling domains, hosting of binding or catalytic sites, and use as building blocks to assemble higher-order complexes. We predict protein interactions in order to better understand the principles of macromolecular recognition and to gain insight into the process by which the adaptive immune system discriminates self from non-self in the context of infectious and autoimmune diseases and cancer. Our research during the project period will be directed toward two broad goals: de novo design and functionalization of tandem repeat proteins, and prediction of peptide-MHC recognition by T cell receptors (TCRs). The proposed protein design work builds on our recent progress designing circular tandem repeat proteins with a range of repeat numbers and diameters and applying these designs as multivalent display scaffolds for the presentation of binding and signalling domains. Our TCR studies leverage the tools we have recently developed to model—structurally and bioinformatically—repertoires of T cell receptors and their peptide:MHC specificity. Looking ahead, I am optimistic that by combining atomically-detailed molecular simulations and data-intensive machine learning techniques we will be able to generate designed protein constructs and predictive algorithms that have a significant positive impact on human health.

项目概要/摘要 随着算法的进步，结构生物学提供了一个强大的视角来观察生命系统。 和计算，分子模拟已经开始作为工具补充传统的实验方法 与此同时，数据密集型机器学习方法正变得越来越多。 在我的实验室高通量实验研究的快速增长的推动下，这在生物学中很重要。 应用结构生物学、分子模拟和机器学习技术来设计新蛋白质 我们设计新的蛋白质结构，以便更好地理解蛋白质的相互作用。 蛋白质折叠原理并为一系列生物医学创建高度稳定和坚固的分子支架 应用包括结合或信号传导域的多价展示、结合或催化位点的托管、 并用作组装高阶复合物的构建块，以便预测蛋白质相互作用。 更好地理解大分子识别原理并深入了解大分子识别的过程 适应性免疫系统在感染和自身免疫的情况下区分自我和非自我 我们在项目期间的研究将针对两个主要目标：从头开始。 串联重复蛋白的设计和功能化，以及 T 细胞对肽-MHC 识别的预测 拟议的蛋白质设计工作建立在我们最近设计环形串联的进展之上。 具有一系列重复次数和直径的重复蛋白质，并将这些设计应用为多价 我们的 TCR 研究利用了我们的工具来展示结合和信号传导域。 最近开发出了 T 细胞受体及其结构和生物信息学模型 肽：MHC 特异性，我乐观地认为，通过结合原子详细的分子。 模拟和数据密集型机器学习技术我们将能够生成设计的蛋白质 对人类健康产生重大积极影响的构造和预测算法。