Computer Simulation Theory of Globular Protein Dynamics

球状蛋白质动力学的计算机模拟理论

• 批准号: 7205539
• 负责人: JEFFREY SKOLNICK
• 金额: $ 24.89万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 1986
• 资助国家: 美国
• 起止时间: 1986-12-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7205539
• 关键词: Caenorhabditis elegans; Drosophilidae; Escherichia coli; Mycoplasma; Saccharomyces cerevisiae; active sites; computer program /software; computer simulation; conformation; globular protein; method development; model design /development; molecular dynamics; physical model; protein folding; protein sequence; protein structure

DESCRIPTION (provided by applicant): With the genome sequencing projects providing a deluge of sequences, to utilize this information requires knowledge of the function of all the proteins in a given genome. Because biochemical function is determined by a protein's active site structure, protein structures are becoming essential tools for genome functional annotation. This has spurred structural genomics approaches that aim to develop high-throughput protein structure determination methods. Structure prediction is important not only for target selection in structural genomics but also for genome scale functional prediction. The goal of this project is to improve our TOUCHSTONE tertiary structure prediction algorithm that employs predicted secondary and tertiary restraints by addressing the key issues in protein folding: the lack of potentials that recognize the native state from the myriad of protein like, misfolded structures and the lack effective search conformational algorithms, especially for proteins over 150 residues. The Specific Aims designed to address these problems are: (1) The comprehensive native structure prediction of a representative set of all proteins with solved structures less than 201 residues in length (no pair with more than 35% sequence identity; at present 2282 proteins) will be done. This comprehensive test, made possible due to our new 4,000 processor PC cluster, will establish the full range of validity of TOUCHSTONE and provide a benchmark against which subsequent improvements can be assessed. (2). The model will be reparameterized based on the results from and use of the comprehensive set of decoys provided by (1). Each term in the potential will be examined, relative weights adjusted and where necessary extended or rederived. Due to the large number of native and decoy structures, the greatly improved statistics will allow for the derivation of terms in the potential, (e.g. 3-body secondary structure dependent pair potentials) that was not previously possible. (3). Improved protocols to predict the tertiary restraints that permit the folding of complex topologies will be developed. (4). Methods to select native like structures will be improved, e.g. by a series of simulations where previously generated clustered structures are used to derive restraints for subsequent simulations. (4). The prediction of tertiary structure of all small (<201 residues) proteins in the M. genitalium, E. coli, S. cerevisiae, D.. melanogaster C. elegans, and human genomes will be done. (5). The algorithm will be the object of continual independent testing including participation in future CASPs. The overall goal is to range of validity of our ab initio folding algorithms and to provide significant improvements in the state of the art of tertiary structure prediction.

描述（由申请人提供）：随着基因组测序计划提供大量序列，为了利用这些信息，需要了解给定基因组中所有蛋白质的功能。由于生物化学功能是由蛋白质的活性位点结构决定的，蛋白质结构正成为基因组功能注释的重要工具。这刺激了旨在开发高通量蛋白质结构测定方法的结构基因组学方法。结构预测不仅对结构基因组学中的靶标选择，而且对基因组规模的功能预测都具有重要意义。该项目的目标是通过解决蛋白质折叠中的关键问题来改进我们的TOUCHSTONE三级结构预测算法，该算法采用预测的二级和三级限制：缺乏从无数蛋白质样错误折叠结构中识别天然状态的潜力，以及缺乏有效的搜索构象算法，特别是对于超过150个残基的蛋白质。旨在解决这些问题的具体目标是：（1）将对长度小于201个残基的所有蛋白质的代表性集合（没有序列同一性超过35%的蛋白质对;目前有2282个蛋白质）进行全面的天然结构预测。由于我们新的4，000处理器PC集群，这种全面的测试成为可能，将建立TOUCHSTONE的有效性的全方位，并提供一个基准，根据该基准可以评估后续的改进。（二）.该模型将重新参数化的基础上的结果和使用的综合套诱饵（1）。潜力中的每一项都将得到审查，调整相对权重，并在必要时进行扩展或重新推导。由于大量的天然和诱饵结构，大大改善的统计将允许推导的条款的潜力，（例如，3-体二级结构依赖对电位），这是以前不可能的。（三）、改进的协议，以预测三级限制，允许折叠复杂的拓扑结构将被开发。（四）、将改进选择类天然结构的方法，例如，通过一系列模拟，其中使用先前生成的聚类结构来导出后续模拟的约束。（四）、对M. genitalium、E. coli、S. cerevisiae，D.黑腹树C.线虫和人类基因组都将完成。（五）、该算法将成为持续独立测试的对象，包括参与未来的CASP。我们的总体目标是我们的从头折叠算法的有效性范围，并提供显着的改进，在最先进的三级结构预测。