Molecular modeling and machine learning for protein structures and interactions

项目摘要/摘要 结构生物学提供了一个观察生命系统的强大透镜。随着算法的进步 在计算方面，分子模拟已经开始作为工具补充传统的实验方法 为了发现。与此同时，数据密集型机器学习方法正变得越来越多 由于高通量实验的快速增长，这在生物学中具有重要意义。我的实验室里的研究 应用结构生物学、分子模拟和机器学习技术来设计新的蛋白质 结构和预测蛋白质的相互作用。我们设计新的蛋白质结构是为了更好地理解 蛋白质折叠原理，并为一系列生物医学创造高度稳定和坚固的分子支架 应用包括结合或信号域的多价展示，结合或催化部位的宿主， 并用作构建块来组装更高阶的复合体。我们预测蛋白质的相互作用是为了 更好地理解大分子识别的原理，并深入了解 适应性免疫系统在感染和自身免疫的背景下区分自我和非我 疾病和癌症。我们在项目期间的研究将朝着两个广泛的目标进行：从头开始 串联重复蛋白的设计、功能化及T细胞识别多肽-MHC的预测 受体(TCR)。建议的蛋白质设计工作建立在我们设计环形串联的最新进展的基础上 具有一系列重复数和直径的重复蛋白，并将这些设计应用为多价 展示结合域和信令域的展示支架。我们的TCR研究利用我们的工具 最近发展到从结构和生物信息上模拟T细胞受体和它们的 多肽：MHC特异性。展望未来，我乐观地认为，通过将原子细节分子 模拟和数据密集型机器学习技术，我们将能够产生设计的蛋白质 对人类健康有重大积极影响的结构和预测算法。

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.