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

项目总结/摘要： 我们在分子水平上理解生物系统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 测定技术和同源性建模不可访问。近四分之一的蛋白质家族 目前黑暗，分子构象是完全未知的，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，以全面了解生物学和疾病，从而铺平道路， to structure-based结构为基础的drug药物design设计at genomic基因组scale规模.计算蛋白质建模在这一努力中起着关键作用 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 新的数据驱动的计算建模和细化方法，以提高准确性和覆盖面， 蛋白质结构预测的基因组规模无关的同源性。未来的研究重点是改善 使用由基于深度学习的残基间驱动的多尺度从头建模的无同源性蛋白质折叠 相互作用，通过制定新的远程算法来增强低同源性线程或折叠识别 模板识别，尽管低进化相关性，并开发高分辨率的方法 受约束的结构细化引导的广义系综搜索驱动计算模型， 接近实验的精确度。将进行蛋白质组范围的计算建模和细化工作， 利用我们对大型超级计算基础设施的独特访问， 涵盖了暗蛋白家族，将被组织在一个数据库中供公众访问。这一全面 结构注释数据库将揭示黑暗的结构，功能和相互作用 蛋白质组，在药物发现和人类健康的广泛影响。软件和网络服务器将免费 传播，以帮助世界各地的生物医学研究人员将这些方法应用于他们的具体领域。 研究问题，从而倍增计算建模对生物学基础研究的影响， 药我的研究计划将涉及与其他NIGMS支持的研究人员密切合作， 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，并促进结构生物信息学和计算生物学的未来研究进展。

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.