USING THE FOLDING PROCESS TO IMPROVE PROTEIN STRUCTURE PREDICTION

利用折叠过程改进蛋白质结构预测

• 批准号: 8364286
• 负责人: KARL F FREED
• 金额: $ 0.11万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8364286
• 关键词: Algorithms; Amino Acid Sequence; Biomedical Research; Computers; Computing Methodologies; Development; Funding; Genome; Grant; High Performance Computing; Knowledge; Machine Learning; Methods; National Center for Research Resources; Peptide Sequence Determination; Principal Investigator; Process; Property; Proteins; Research; Research Infrastructure; Resources; Scheme; Simulate; Source; Structure; Tertiary Protein Structure; Testing; Time; United States National Institutes of Health; computer code; cost; improved; novel; protein structure; protein structure prediction; simulation

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Computational methods for determining protein structure are essential to the genome project due to the huge number of new sequences for proteins whose properties are completely unknown. A major limitation to current protein structure prediction algorithms is the inadequate quality of the input secondary structure predictions produced by the two major secondary strcture servers. These servers use machine learning methods that extensively rely on sequence similarity (called homology) and, hence, use sequence local information despite the knowledge that tertiary context often influences the secondary structure. We have devised a Monte Carlo simulated annealing algorithm and a corresponding set of computer codes to implement a novel scheme in which both secondary and tertiary structure are predicted in a self-consistent bootstrap fashion without the use of homology information. Tests made using a teragrid development grant demonstrate that our method outperforms the leading servers in secondary structure prediction and provides comparable tertiary structures to the best methods (using two orders of magnitude less computer time!) for small (less than 120 residues) single domain proteins. This proposal seeks to improve and extend our predictive methods as well as increase their computational efficiency. Proposed projects include the use of sequence similarity to improve our move set, the introduction of dynamic criteria for secondary structure assignment that vary depending of the fraction of structure previously assigned during the simulations, the improvement of the energy function to enhance the predictive quality and enable treating larger proteins, etc. Extensive applications will consider a wide range of proteins with unusual or difficult tertiary structures.

该子项目是利用资源的众多研究子项目之一 由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持 并且子项目的主要研究者可能是由其他来源提供的， 包括其他 NIH 来源。 子项目可能列出的总成本 代表子项目使用的中心基础设施的估计数量， NCRR 赠款不直接向子项目或子项目工作人员提供资金。 由于存在大量性质完全未知的蛋白质新序列，确定蛋白质结构的计算方法对于基因组计划至关重要。当前蛋白质结构预测算法的一个主要限制是两个主要二级结构服务器产生的输入二级结构预测的质量不足。这些服务器使用广泛依赖于序列相似性（称为同源性）的机器学习方法，因此尽管知道三级上下文经常影响二级结构，但仍使用序列局部信息。我们设计了蒙特卡罗模拟退火算法和相应的计算机代码集来实现一种新颖的方案，其中以自洽引导方式预测二级和三级结构，而不使用同源信息。使用 teragrid 开发补助金进行的测试表明，我们的方法在二级结构预测方面优于领先的服务器，并为小型（少于 120 个残基）单域蛋白质提供了与最佳方法相当的三级结构（使用的计算机时间少了两个数量级！）。该提案旨在改进和扩展我们的预测方法并提高其计算效率。拟议的项目包括使用序列相似性来改进我们的移动集、引入二级结构分配的动态标准（该标准根据模拟过程中先前分配的结构分数而变化）、改进能量函数以提高预测质量并能够处理更大的蛋白质等。广泛的应用将考虑具有不寻常或困难的三级结构的各种蛋白质。