Prioritized Assembly in Protein Conformational Search

蛋白质构象搜索中的优先组装

• 批准号: 7216276
• 负责人: KAIZHI YUE
• 金额: $ 12.87万
• 依托单位: CONFORMATIONAL SEARCH SOLUTIONS
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-05-28 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7216276
• 关键词: Algorithms; Amino Acid Sequence; Architecture; Be++ element; Beryllium; Binding; Complement; Complex; Databases; Depth; EEF1A1 gene; Effectiveness; Elements; Genetic Programming; Growth; Helix (Snails); Individual; Lead; Maps; Methods; Modeling; Molecular Conformation; Pattern; Peptide Sequence Determination; Phase; Potential Energy; Process; Proline; Protein Analysis; Protein Region; Proteins; Purpose; Research; Sampling; Scheme; Solutions; Speed; Structural Protein; Structure; Techniques; Testing; Time; Trees; base; beta pleated sheet; ear helix; improved; molecular dynamics; molecular mechanics; peer; programs; protein folding; protein protein interaction; protein structure prediction; research study; size; success; tool

DESCRIPTION (provided by applicant): An efficient and effective conformational search method is essential for solving the computational protein folding problem. Prevalent conformational search methods are fast but incomplete. Proposed here is an exhaustive enumeration-based search method that combines a focus on the most promising search paths with a complete coverage of a discretized conformational space. The search is a prioritized building-up process: Structural templates of low energy are generated for short segments of the chain. Structural elements, either templates or individual residues, are added to a growing conformation one at a time. The addition of a structural element is prioritized on its energetical contribution to the conformation as a whole. Simple templates such as tight turns, helices or strands form packing clusters such as hairpins and beta-sheets. A finished conformation is often the assembly of packing clusters. Search efficiency is achieved by pruning unpromising search branches at each building-up step. In phase I, the architecture of prioritized assembly-based search has been implemented, demonstrating the feasibility of the approach. Phase II will develop algorithms for computing tight lower bounds of conformational energy, which are crucial for effective branch-and-bound search, and for integrating exhaustive enumeration with minimization. Phase III, with peer-to-peer parallelization, will develop a comprehensive conformational search program for computational protein folding.

描述(申请人提供)：一种高效有效的构象搜索方法是解决蛋白质折叠计算问题的关键。流行的构象搜索方法是快速的，但不完整。本文提出了一种基于穷举的搜索方法，它结合了对最有希望的搜索路径的关注和对离散化构象空间的完全覆盖。搜索是一个按优先顺序建立的过程：为链条的短片段生成低能量的结构模板。结构元素，无论是模板还是单个残基，一次一个地添加到增长的构象中。结构元素的添加优先考虑其对整体构象的能量贡献。简单的模板，如紧旋，螺旋或链形成堆积簇，如发夹和测试片。最终的构象通常是堆积簇的组装。搜索效率是通过在每个建立步骤中修剪前景不佳的搜索分支来实现的。 在第一阶段，已经实现了基于集合的优先搜索的体系结构，证明了该方法的可行性。第二阶段将开发算法来计算构象能量的紧密下限，这对于有效的分支定界搜索以及将穷举与最小化相结合是至关重要的。第三阶段，通过对等并行，将开发一个全面的构象搜索程序，用于计算蛋白质折叠。