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: 7956344
• 负责人: KARL F FREED
• 金额: $ 0.08万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7956344
• 关键词: Algorithms; Amino Acid Sequence; Biomedical Research; Computer Retrieval of Information on Scientific Projects Database; Computers; Computing Methodologies; Development; Funding; Genome; Grant; High Performance Computing; Institution; Knowledge; Machine Learning; Methods; Peptide Sequence Determination; Process; Property; Proteins; Research; Research Personnel; Resources; Scheme; Simulate; Source; Structure; Tertiary Protein Structure; Testing; Time; United States National Institutes of Health; computer code; 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 确定蛋白质结构的计算方法对于基因组计划是必不可少的，因为大量的蛋白质新序列的性质是完全未知的。当前蛋白质结构预测算法的一个主要限制是由两个主要的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，尽管知道三级背景通常会影响二级结构，但仍使用序列局部信息。我们已经设计了一个Monte Carlo模拟退火算法和相应的一组计算机代码来实现一个新的计划，其中二级和三级结构预测在一个自洽的引导方式，而不使用同源性信息。使用teragrid开发基金进行的测试表明，我们的方法在二级结构预测方面优于领先的服务器，并提供与最佳方法相当的三级结构（使用两个数量级的计算机时间！）对于小的（小于120个残基）单结构域蛋白。该建议旨在改进和扩展我们的预测方法，并提高其计算效率。建议的项目包括使用序列相似性，以提高我们的移动集，引入动态二级结构分配的标准，根据不同的结构的分数先前分配在模拟过程中，改进的能量函数，以提高预测质量，使治疗更大的蛋白质，等广泛的应用将考虑广泛的蛋白质与不寻常的或困难的三级结构。