Integrated Prediction of Protein Struture at 1D, 2D and 3D Levels

1D、2D 和 3D 水平的蛋白质结构综合预测

• 批准号: 8476234
• 负责人: Jianlin Cheng
• 金额: $ 28.36万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8476234
• 关键词: 3-Dimensional; Algorithms; Alternative Splicing; Amino Acid Sequence; Bioinformatics; Biomedical Research; Communities; Computational Biology; Computing Methodologies; Coupled; Crystallization; Drug Design; Evolution; Generations; Genetic Recombination; Genomics; Growth; Linear Programming; Machine Learning; Maps; Markov Chains; Methods; Modeling; Mutagenesis; Peptide Sequence Determination; Pharmaceutical Preparations; Potential Energy; Production; Protein Analysis; Protein Engineering; Proteins; Signal Transduction; Site; Spliced Genes; Structural Models; Structural Protein; Structure; Techniques; Tertiary Protein Structure; base; computerized tools; design; disulfide bond; improved; innovation; knowledge base; novel; protein folding; protein function; protein structure; protein structure prediction; public health relevance; restraint; simulation; spatial relationship; three dimensional structure; tool; two-dimensional; user friendly software; web services

DESCRIPTION (provided by applicant): Computational prediction of protein structure from the amino acid sequence is one of the most important and challenging problems in bioinformatics and computational biology. With the exponential growth of protein sequences without solved protein structures in the post-genomic era, accurate protein structure prediction methods and tools are in urgent need. Here, we propose to develop an integrated approach to advance protein structure prediction at the 1-dimensional (1D), 2-dimensional (2D) and 3-dimensional (3D) levels. At the 1D level, novel information such as domain evolution signals, alternative gene splicing sites, and 2D protein contact map will be used to predict protein domain boundaries from the sequences. At the 2D level, new methods such as residue contact propagation, machine learning boosting, linear programming, and Markov Chain Monte Carlo simulations will be used to advance residue-residue contact prediction for a domain, or a protein. At the 3D level, 2D contact prediction, fold recognition via machine learning, and multi-template combination will be used to enhance both template-based and ab initio structure prediction. Finally, knowledge-based statistical machine learning methods and model combination algorithms will be developed to reliably evaluate and refine the quality of predicted protein structural models. One of several innovative aspects of this approach is to integrate 1D, 2D, and 3D predictions in order to improve each other through protein structural unit - domains. The 1D, 2D, and 3D protein structure prediction methods will be implemented as user-friendly software packages and web services released to the scientific community. These tools and web services will be useful for protein structure prediction, structure determination, functional analysis, protein engineering, protein mutagenesis analysis, and protein design.

描述（由申请人提供）：从氨基酸序列计算预测蛋白质结构是生物信息学和计算生物学中最重要和最具挑战性的问题之一。在后基因组时代，随着蛋白质结构未解的蛋白质序列呈指数增长，迫切需要准确的蛋白质结构预测方法和工具。在这里，我们建议开发一种综合方法来推进一维（1D），二维（2D）和三维（3D）水平的蛋白质结构预测。在一维水平上，新的信息，如结构域进化信号，替代基因剪接位点和二维蛋白质接触图将用于预测蛋白质结构域边界的序列。在二维层面，残差接触传播、机器学习增强、线性规划和马尔可夫链蒙特卡罗模拟等新方法将用于推进残差接触预测域或蛋白质。在3D层面，将使用2D接触预测、通过机器学习进行折叠识别和多模板组合来增强基于模板和从头开始的结构预测。最后，将开发基于知识的统计机器学习方法和模型组合算法，以可靠地评估和改进预测蛋白质结构模型的质量。该方法的几个创新方面之一是整合1D， 2D和3D预测，以便通过蛋白质结构单元域相互改进。蛋白质结构的1D、2D和3D预测方法将以用户友好的软件包和web服务的形式发布给科学界。这些工具和web服务将用于蛋白质结构预测、结构测定、功能分析、蛋白质工程、蛋白质突变分析和蛋白质设计。