Novel Algorithms to expedite experimental protein structure determination

加快实验蛋白质结构测定的新算法

• 批准号: 341661-2009
• 负责人: Lilien, Ryan
• 金额: $ 1.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=482219
• 关键词: Novel; Algorithms; expedite; experimental; protein

Despite the importance of macromolecular structure in unraveling the biochemical basis of life, experimental structure determination remains a long, difficult, and expensive endeavour. The interest in protein structures is motivated by the general mantra that protein function is dictated by protein structure. This suggests that knowledge of protein structure is helpful not only in analysis of wildtype (or natural) proteins but also for mutant proteins such as those implicated in disease processes. Consequently, knowledge of protein structure has emerged as a critical resource for drug discovery and development. In the future, macromolecular structure determination will be an automated turn-key operation, and should be fast, inexpensive, and high-yield. My proposed research focuses on developing accurate and efficient algorithms to expedite the process of experimental protein structure determination. The proposed research program integrates knowledge from structural biology with algorithms in machine learning, machine vision, and computational search. The long-term goals of my interdisciplinary research program are to develop and apply techniques from Computer Science and Engineering to problems in Structural Biology (the study of protein structure and function). The research highlighted in this proposal represents part of a family of projects in my lab including algorithm development for experimental structure determination, protein design, structure-based drug design, and chemical synthesis planning. In this proposal, we describe several novel computational approaches for automating current experimental bottlenecks in X-Ray Crystallography and Electron CryoMicroscopy. As useful algorithmic milestones are achieved, we will package our software and make it freely available to the research community.

尽管大分子结构在解开生命的生化基础方面很重要，但实验结构测定仍然是一项漫长、困难和昂贵的工作。对蛋白质结构的兴趣是由蛋白质结构决定蛋白质功能这一普遍信条所激发的。这表明，对蛋白质结构的了解不仅有助于分析野生型(或天然)蛋白质，而且还有助于分析突变蛋白质，如那些与疾病过程有关的蛋白质。因此，蛋白质结构的知识已经成为药物发现和开发的关键资源。在未来，大分子结构的测定将是一项自动化的交钥匙操作，应该是快速、廉价和高产率的。我提出的研究重点是开发准确和高效的算法来加快实验蛋白质结构确定的过程。拟议的研究计划将结构生物学的知识与机器学习、机器视觉和计算搜索中的算法相结合。我的跨学科研究计划的长期目标是开发和应用计算机科学和工程技术来解决结构生物学(研究蛋白质结构和功能)中的问题。这项提案中强调的研究是我实验室一系列项目的一部分，这些项目包括用于实验结构确定、蛋白质设计、基于结构的药物设计和化学合成规划的算法开发。在这项建议中，我们描述了几种新的计算方法，用于自动化当前X射线结晶学和电子冷冻显微镜中的实验瓶颈。随着有用的算法里程碑的实现，我们将打包我们的软件，并将其免费提供给研究社区。