High-Accuracy Models of Proteins from Remote Homology

来自远程同源性的高精度蛋白质模型

• 批准号: 7495070
• 负责人: Jie Liang
• 金额: $ 27.07万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7495070
• 关键词: Address; Algorithms; Amino Acid Sequence; Area; Arts; Back; Belief; Classification; Computational Technique; Computer Simulation; Computing Methodologies; Data; Development; Disease; Drug Design; Elements; Generations; Goals; Homologous Gene; Liver Acinus Zone 2; Modeling; Nature; Numbers; Outcome; Peptide Sequence Determination; Phase; Potential Energy; Production; Protein Structure Initiative; Proteins; Rest; Scientist; Side; Strategic Planning; Structural Models; Structure; Techniques; United States National Institutes of Health; Vertebral column; analog; base; bone; design; falls; improved; novel; novel strategies; numb protein; protein folding; protein structure; protein structure prediction; research study; scale up; structural genomics

DESCRIPTION (provided by applicant): The goal of this project is to develop novel computational techniques to significantly improve the state-of-the-art in four areas of protein structure prediction: (1) protein fold recognition capable of identifying structural homologs when the sequence similarity is within or below the so-called "twilight-zone"; (2) empirical potential functions that can effectively identify the best models from those that are problematic and can guide the generation of high-quality structural models; (3) accurate sequence-structure alignments, through expanded structural rules, through simultaneous backbone threading to take full advantage of the more accurate two-body and three-body energy functions, and through systematic and rapid generation and application of limited structural data from experiments; and (4) accurate prediction of loops and side-chains through applications of novel loop generation and prediction techniques, and through development of rigorous and efficient algorithms for side-chain packing prediction. The key elements of the outcome of this project will be an ensemble of very effective prediction and modeling computational methods and implementations, which can address the most challenging problems in building high quality structures from sequences. A computational pipeline for high accuracy structure prediction designed for proteins that do not have close structural homologs in PDB will be developed. These computational capabilities could have profound impacts to drug design and disease studies.

描述（由申请人提供）：该项目的目标是开发新颖的计算技术，以显着提高蛋白质结构预测四个领域的最新技术：（1）蛋白质折叠识别，能够在序列相似性在所谓的“暮光区”之内或之下； （2）经验势函数，能够有效地从有问题的模型中识别出最佳模型，并能够指导高质量结构模型的生成； （3）精确的序列结构比对，通过扩展的结构规则，通过同时主干线程充分利用更精确的二体和三体能量函数，并通过实验中有限的结构数据系统快速地生成和应用; (4)通过应用新颖的循环生成和预测技术，以及通过开发严格且高效的侧链打包预测算法来准确预测循环和侧链。该项目成果的关键要素将是非常有效的预测和建模计算方法和实现的集合，它可以解决从序列构建高质量结构中最具挑战性的问题。将开发专为 PDB 中没有紧密结构同源物的蛋白质设计的高精度结构预测的计算管道。这些计算能力可能对药物设计和疾病研究产生深远的影响。