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

描述(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。随着后基因组时代没有解决蛋白质结构的蛋白质序列的指数性增长，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的方法来推进一维(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、基因选择性剪接位点和二维蛋白质接触图，将被用来从序列中预测蛋白质结构域边界。在2D水平上，将使用新的方法，如残基接触传播、机器学习增强、线性规划和马尔可夫链蒙特卡罗模拟来推进结构域或蛋白质的残基-残基接触预测。在3D水平上，将使用2D接触预测、通过机器学习的折叠识别和多模板组合来增强基于模板的结构预测和从头计算结构预测。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，以可靠地评估和提炼预测的蛋白质结构模型的质量。这种方法的几个创新方面之一是集成一维、二维和三维预测，以便通过蛋白质结构单位域相互改进。一维、二维和三维蛋白质结构预测方法将作为用户友好的软件包和网络服务发布给科学界。这些工具和Web服务将用于蛋白质结构预测、结构确定、功能分析、蛋白质工程、蛋白质突变分析和蛋白质设计。 与公共健康相关：该项目将为基础生物医学研究开发准确的计算方法和工具，如蛋白质结构预测、蛋白质功能分析、蛋白质设计、蛋白质工程和基于结构的药物设计。

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. PUBLIC HEALTH RELEVANCE: The project will develop accurate computational methods and tools for basic biomedical research such as protein structure prediction, protein function analysis, protein design, protein engineering, and structure-based drug design.