I-TASSER based atomic-level protein structure prediction

基于 I-TASSER 的原子级蛋白质结构预测

• 批准号: 8705534
• 负责人: Yang Zhang
• 金额: $ 28.62万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8705534
• 关键词: Algorithms; Amino Acid Sequence; Biochemical; Biological; Biological Models; Categories; Chemicals; Communities; Computational algorithm; Computer Simulation; Country; Crystal Formation; Data; Databases; Detection; Development; Disease; Distant; Family; Genome; Goals; Homologous Protein; Homology Modeling; Hybrids; Industry; Knowledge; Length; Libraries; Medical; Methods; Modeling; Peptide Sequence Determination; Preclinical Drug Evaluation; Protein Structure Initiative; Proteins; Public Health; Resolution; Roentgen Rays; Scientist; Shapes; Solutions; Speed; Structural Models; Structure; Tertiary Protein Structure; Uncertainty; Weight; base; design; drug discovery; fight against; improved; next generation; programs; protein folding; protein structure; protein structure prediction; public health relevance; research study; screening; simulation; success; three dimensional structure

DESCRIPTION (provided by applicant): Recent CASP experiments have witnessed considerable progress in protein structure prediction. The state of the art algorithms, including I TASSER, can build models of correct fold for ~3/4 of single-domain protein targets, where template models can be driven closer to the native state in more than 80% of cases. As a consequence, the highly efficient protein structure modeling systems have been widely used by the biological and medical communities. Nevertheless, the accuracy of computational models for the proteins of distant-homology templates is usually low, which are of no practical use to most of biomedical studies. For proteins of >150 residues, ab initio modeling cannot successfully construct the correct fold. This project extends the development of the I-TASSER-based algorithms for high-resolution protein structure predictions, with the focus on improving the ability of distant-homology modeling and ab initio folding for large-size proteins. It also sees to increase the modeling accuracy by the aid of sparse and easily accessible experiment data including small-angle X-ray scattering. Built on the strength of the well-established I-TASSER and QUARK methods, the project aims to significantly improving the state of the art of tertiary protein structure prediction, especially for the non- and distant-homology proteins, so that the computational structure prediction can be of real use to modern drug screening and biochemical functional inference for the majority of proteins in genomes.

描述（由申请人提供）：最近的CASP实验在蛋白质结构预测方面取得了相当大的进展。最先进的算法，包括I TASSER可以为约3/4的单结构域蛋白质靶标构建正确折叠的模型，其中模板模型可以在超过80%的情况下更接近天然状态。因此，高效的蛋白质结构建模系统已被生物和医学界广泛使用。然而，远同源模板蛋白质的计算模型的准确性通常较低，这是没有实际用途的大多数生物医学研究。对于>150个残基的蛋白质，从头建模不能成功构建正确的折叠。该项目扩展了基于I-TASSER的高分辨率蛋白质结构预测算法的开发，重点是提高大尺寸蛋白质的距离同源建模和从头折叠的能力。它还看到，以提高建模精度的帮助下，稀疏和容易获得的实验数据，包括小角X射线散射。该项目以成熟的I-TASSER和QUARK方法为基础，旨在显著提高蛋白质三级结构预测的技术水平，特别是对于非同源和远距离同源蛋白质，使计算结构预测能够真实的用于现代药物筛选和基因组中大多数蛋白质的生化功能推断。