Automated Distant Homology Modeling Meta-server

自动远程同源建模元服务器

• 批准号: 7367174
• 负责人: DANIEL FISCHER
• 金额: $ 23.98万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7367174
• 关键词: Algorithms; Arts; Biological; Blast Cell; Code; Complement Factor B; Computer Simulation; Computer software; Data; Dependency; Deposition; Detection; Development; Distant; Evaluation; Future; Generations; Genomics; Goals; Homology Modeling; Housing; Human; Hybrids; Individual; Jackals; Machine Learning; Manuals; Methodology; Methods; Modeling; Numbers; Performance; Principal Investigator; Procedures; Process; Proteins; Reaction Time; Research; Running; Score; Services; Shotguns; Standards of Weights and Measures; Structure; System; Technology; Testing; Translating; Variant; Weight; improved; interest; novel; novel strategies; numb protein; programs; protein structure; protein structure prediction; restraint; structural genomics; three dimensional structure; three-dimensional modeling; tool

The goal of this proposal is to facilitate post-genomic era research by creating a protein structure prediction meta-server for close and distant homology modeling (HM).The proposed meta-server will be a fully automated, fast, widely available, autonomous, multi-component system, aimed at significantly increasing the number of proteins that can be characterized in-silico using current methods. Currently, successful distant HM requires considerable human expertise, including the manual detection and selection of templates and of sequence-template alignments using various fold-recognition (FR) methods, the adjustment of alignments and the refinement, evaluation and selection of alternative models. We aim at automating this process, using state-of-the-art FR meta-prediction technology. We propose 5 specific aims: 1) Evaluate the performance of various top-of-the-line, autonomous FR methods in order to identify the most accurate ones; 2) Develop improved algorithms to generate more accurate FR hybrid models and to better select FR- generated sequence-template alignments; 3) Develop improved methodologies to integrate the information from the FR models and/or the selected alignments to obtain more accurate refined full-atom models; 4) Test, optimize and implement all external software in-house; and 5) Apply the meta-server to specific problems of biological, experimental interest. From the users' perspective this meta-system will facilitate accomplishing one of the goals of Structural Genomics projects, namely, to better exploit the structural information from the experimentally solved proteins in order to obtain relatively accurate computational models for the majority of the remaining proteins, including those at close and those at distant HMdistances. The long term goal is to create a meta-server that will become a standard and will revolutionize the protein structure prediction field in an analogous way to the revolution that PSI-BLAST brought about in the sequence-comparison field, a few years ago.

该提案的目标是通过创建蛋白质结构预测来促进后基因组时代的研究 用于近距离和远距离同源建模（HM）的元服务器。所提出的元服务器将是一个完全 自动化、快速、广泛可用、自主、多组件系统，旨在显着提高 使用当前方法可以在计算机上表征的蛋白质数量。目前，已成功 远程 HM 需要大量的人类专业知识，包括手动检测和选择 模板和使用各种折叠识别（FR）方法的序列模板比对，调整 的调整以及替代模型的细化、评估和选择。我们的目标是实现自动化 过程，使用最先进的 FR 元预测技术。我们提出 5 个具体目标： 1) 评估 各种顶级自主 FR 方法的性能，以确定最准确的方法； 2）开发改进的算法以生成更准确的FR混合模型并更好地选择FR- 生成序列-模板比对； 3）开发改进的方法来整合信息 从FR模型和/或选定的比对中获得更准确的精炼全原子模型； 4） 内部测试、优化和实施所有外部软件； 5) 将元服务器应用到特定的 生物学、实验兴趣的问题。从用户的角度来看，这个元系统将促进 实现结构基因组学项目的目标之一，即更好地利用结构基因组学 来自实验解决的蛋白质的信息，以获得相对准确的计算 大多数剩余蛋白质的模型，包括近距离和远距离的蛋白质。 长期目标是创建一个元服务器，它将成为标准并彻底改变蛋白质 结构预测领域，类似于 PSI-BLAST 带来的革命 序列比较领域，几年前。