NOVEL STATISTICAL ENERGY FUNCTIONS AND APPLICATIONS TO PROTEIN STRUCTURE PREDIC

新颖的统计能量函数及其在蛋白质结构预测中的应用

• 批准号: 8364305
• 负责人: JIANPENG MA
• 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/8364305
• 关键词: Amino Acid Sequence; Area; Binding; Biomedical Research; Biophysics; Drug Design; Funding; Grant; High Performance Computing; Methods; National Center for Research Resources; Play; Principal Investigator; Research; Research Infrastructure; Resources; Role; Side; Source; Structure; United States National Institutes of Health; base; cost; novel; protein structure; protein structure prediction; structural genomics

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. Protein structure prediction is one of the most challenging areas in theoretical biophysics, and it plays an essential role in structural genomics and rational drug design. We have recently developed two novel statistical energy functions: OPUS-Ca, a C-alpha-based potential function composed of seven representative molecular interactions; and OPUS-PSP, an orientation-dependent statistical all-atom potential derived from side-chain packing. We wish to apply these two potential functions to OPUS, a conceptually new structure prediction method that employs multi-scale, multi-layer and top-down prediction strategies. OPUS combines template-based and de novo methods to predict 3D protein structures from primary sequences.

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