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: 10029150
• 负责人: Debswapna Bhattacharya
• 金额: $ 36.21万
• 依托单位: AUBURN UNIVERSITY AT AUBURN
• 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/10029150
• 关键词: Algorithms; Amino Acid Sequence; Automobile Driving; Basic Science; Bioinformatics; Biology; Collaborations; Communities; Complex; Computational Biology; Computer Models; Computer software; Data; Databases; Development; Disease; Drug Design; Family; Fostering; Genomics; Health; Homology Modeling; Human; Infrastructure; Laboratories; Light; Medicine; Methods; Modeling; Molecular; Molecular Biology; Molecular Conformation; Molecular Disease; National Institute of General Medical Sciences; Play; Protein Family; Proteins; Proteome; Research; Research Personnel; Resolution; Structure; Supercomputing; Techniques; Underrepresented Groups; base; biological systems; database structure; deep learning; drug discovery; genome-wide; improved; member; next generation; novel; programs; protein folding; protein structure; protein structure prediction; training opportunity; web server

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.

项目概要/摘要： 我们在分子水平上对生物系统的理解中仍然存在的一个关键差距是如何在结构上 注释“暗”蛋白质家族——实验结构未解决的蛋白质家族部分 近四分之一的蛋白质家族无法进行测定技术和同源建模。 目前处于黑暗状态，分子构象完全未知，并且这种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，现在的关键挑战是。 如何弥合这一差距，获得对生物学和疾病的全面了解，从而铺平道路 基因组规模的基于结构的药物设计在这项工作中发挥着关键作用。 由于其可扩展性和全基因组适用性，我的实验室专注于开发和应用。 新颖的数据驱动计算建模和细化方法，以提高准确性和覆盖范围 基因组规模的蛋白质结构预测，无论同源性如何，未来的研究重点是改进。 使用基于深度学习的残基间驱动的多尺度从头建模进行无同源蛋白质折叠 通过制定新的远程算法来增强低同源性线程或折叠识别 尽管进化相关性较低，但仍可进行模板识别，并开发高分辨率方法 由广义集成搜索引导的约束结构细化，用于驱动计算模型 将进行接近实验的准确性的蛋白质组计算建模和细化工作， 利用我们对大规模超级计算基础设施的独特访问权限，构建高可信度模型 涵盖暗蛋白质家族，这些蛋白质将被组织在一个数据库中供公众访问。 结构注释数据库将揭示黑暗的结构、功能和相互作用 对药物发现和人类健康具有广泛影响的蛋白质组软件和网络服务器将免费开放。 传播以帮助全球生物医学研究人员将这些方法应用于他们的特定领域 研究问题，从而倍增计算模型对生物学和基础研究的影响 我的研究计划将涉及与其他 NIGMS 支持的研究人员的密切合作， 为下一代研究人员（包括代表性不足的成员）创造培训机会 组，并促进结构生物信息学和计算生物学的未来研究进展。