Advanced approaches to protein structure prediction

蛋白质结构预测的先进方法

• 批准号: 10132358
• 负责人: Yang Zhang
• 金额: $ 51.41万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2021-12-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10132358
• 关键词: ART protein; Amino Acid Sequence; Bioinformatics; Biological; Cells; Communities; Computing Methodologies; Data; Databases; Drug Industry; Goals; Health; Human; Induced Mutation; Information Networks; Knowledge; Maps; Medical; Methods; Molecular Conformation; Network-based; Pharmacologic Substance; Proteins; Quaternary Protein Structure; Research Personnel; Structural Models; Structural Protein; Structure; Techniques; base; deep neural network; design; drug discovery; fight against; genome sequencing; human disease; improved; protein data bank; protein function; protein protein interaction; protein structure function; protein structure prediction; screening; simulation; success; three dimensional structure; tool

Abstract The success of genome sequencing over the last four decades has resulted in a rapidly increasing gap between the number of known protein sequences and the number of known protein structures and functions. Because protein sequence on its own cannot tell us what each molecule does in cells, the large-scale absence of protein structure and function information severely hinders the progress of contemporary biological and medical studies. These gaps in understanding strongly call for efficient computational approaches for automated, yet highly accurate protein structure prediction and function annotation. The PI’s lab has a successful track record in developing and disseminating high-quality structural bioinformatics methods which have been widely used by the global community. In this project, the lab seeks to develop new advanced methods for both tertiary and quaternary protein structure prediction. Built on the tools and databases previously developed in the PI’s lab, new deep neural-network based techniques will be extended to residue-level intra- and inter-chain contact- and distance-map predictions. These predictions will then be used to constrain the conformational searching space of threading-based fragment assembly simulations, with the aim to significantly improve the accuracy and success rate of structure modeling of monomeric proteins and protein-protein interactions (PPIs), especially for the difficult targets that lack homologous templates in the Protein Data Bank. Next, the structure and PPI network information will be used to help elucidate multiple levels of biological and biomedical functions for protein molecules, including mutation-induced changes in protein stability and human disease predictions. The long- term goals of this project are to significantly improve the state of the art of protein structure prediction and to narrow the gap between the abundance of protein sequence information and the dearth of protein structure and function data, thus significantly enhancing the usefulness and impact of structural bioinformatics. Success in this project will also help reveal the general principles governing the fundamental relations across sequence, structure and function of protein molecules.

抽象的 在过去的四十年中，基因组测序的成功导致了迅速增加的差距 已知蛋白质序列的数量以及已知的蛋白质结构和功能的数量。因为 蛋白质序列本身不能告诉我们每个分子在细胞中的作用，蛋白质的大规模缺失 结构和功能信息严重阻碍了当代生物学和医学研究的进步。 这些差距强烈要求为自动化但高度的高效计算方法 准确的蛋白质结构预测和功能注释。 Pi的实验室在 开发和传播高质量的结构生物信息学方法，这些方法已被广泛使用 全球社区。在这个项目中，实验室试图为第三纪和 第四纪蛋白结构预测。建立在以前在PI实验室中开发的工具和数据库的基础上 新的基于神经网络的新技术将扩展到居住级内和链间接触和 距离图预测。然后，这些预测将用于限制构象搜索空间 基于线程的碎片组装模拟，目的是显着提高准确性和 单体蛋白质和蛋白质蛋白质相互作用（PPI）的结构建模成功率，尤其是针对 蛋白质数据库中缺乏同源模板的困难目标。接下来，结构和PPI网络 信息将用于帮助阐明蛋白质的多个生物学和生物医学功能 分子，包括突变引起的蛋白质稳定性变化和人类疾病预测。长期 该项目的术语目标是显着改善蛋白质结构预测的艺术状态和 缩小蛋白质序列信息的抽象和蛋白质结构的死亡和 功能数据，因此显着增强了结构生物信息学的实用性和影响。成功 项目还将有助于揭示有关跨顺序之间基本关系的一般原则， 蛋白质分子的结构和功能。