Data-driven Computational Modeling and Refinement of Protein Structures on Genomic Scales

数据驱动的计算建模和基因组尺度蛋白质结构的细化

• 批准号: 10604529
• 负责人: Debswapna Bhattacharya
• 金额: $ 38.05万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10604529
• 关键词: Data; driven; Computational; Modeling; Refinement

PROJECT SUMMARY/ABSTRACT: A key remaining gap in our understanding of biological systems at the molecular level is how to structurally annotate the “dark” protein families—the portion of protein families unsolved by experimental structure determination techniques and inaccessible to homology modeling. Nearly a quarter of protein families are currently dark, where molecular conformation is completely unknown and this gap is likely to expand further with the rapid accumulation of new protein sequences without annotated structures. The key challenge is now how to bridge this gap to gain a comprehensive understanding of biology and disease, thereby paving the way to structure-based drug design at genomic scale. Computational protein modeling plays a key role in this effort due to its scalability and genome-wide applicability. My laboratory focuses on the development and application of novel data-driven computational modeling and refinement methods to increase accuracy and coverage of protein structure prediction on genomic scale irrespective of homology. Future research focuses on improving homology-free protein folding using multiscale de novo modeling driven by deep learning-based inter-residue interactions, enhancing low-homology threading or fold recognition by formulating new algorithms for remote template identification despite low evolutionary relatedness, and developing methods for high-resolution restrained structure refinement guided by generalized ensemble search for driving computational models to near-experimental accuracy. Proteome-wide computational modeling and refinement effort will be conducted, leveraging our unique access to large-scale supercomputing infrastructure, to build high-confidence models covering the dark protein families, which will be organized in a database for public access. This comprehensive database of structural annotations will shed light on the structures, functions, and interactions of the dark proteome, with broad implications in drug discovery and human health. Software and web servers will be freely disseminated to help worldwide community of biomedical researchers to apply these methods to their specific research problems, thus multiplying the impact of computational modeling on basic research in biology and medicine. My research program will involve close collaborations with other NIGMS-supported investigators, create training opportunities for the next generation of researchers including members from underrepresented groups, and foster future research advances in structural bioinformatics and computational biology.

项目摘要/摘要： 我们对分子水平上生物系统的理解的关键差距是如何在结构上进行结构 注释“黑暗”蛋白质家族 - 通过实验结构未解决的蛋白质家族的一部分 确定技术，无法获得同源性建模。将近四分之一的蛋白质家族是 目前黑暗，分子会议完全未知，此差距可能会进一步扩大 随着没有注释结构的新蛋白质序列的快速积累。关键挑战现在是 如何弥合这一差距以获得对生物学和疾病的全面理解，从而铺平了道路 以基因组规模为基于结构的药物设计。计算蛋白建模在这项工作中起关键作用 由于其可伸缩性和全基因组的应用。我的实验室专注于开发和应用 新型数据驱动的计算建模和改进方法，以提高准确性和覆盖范围 与同源性无关的基因组量表上的蛋白质结构预测。未来的研究重点是改善 通过深度学习间的互过程，使用多尺度的从头建模驱动器进行无同源蛋白质折叠 通过制定远程新算法，相互作用，增强低体现线程或折叠识别 模板标识目的地低进化相关性，并开发高分辨率的方法 受约束结构的精致以广义合奏为指导 几乎实验的准确性。将进行全蛋白质组范围的计算建模和完善工作， 利用我们独特的大规模超级计算基础架构的访问，建立高信心模型 涵盖黑暗蛋白质家族，该家族将在数据库中组织以供公众访问。这个全面 结构注释的数据库将阐明黑暗的结构，功能和相互作用 蛋白质组，对药物发现和人类健康具有广泛的影响。软件和网络服务器将免费 传播以帮助全球生物医学研究人员社区应用这些方法 研究问题，从而使计算建模对生物学基础研究的影响和 药品。我的研究计划将涉及与其他由NIGMS支持的调查人员进行密切合作， 为下一代研究人员创造培训机会，包括代表性不足的成员 小组，并促进了结构生物信息学和计算生物学的未来研究进展。