EFFICIENT ALGORITHMS FOR PROTEIN TERTIARY STRUCTURE PREDICTION

蛋白质三级结构预测的高效算法

• 批准号: 7610018
• 负责人: XI HUANG
• 金额: $ 1.81万
• 依托单位: UNIV OF ARKANSAS FOR MED SCIS
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-05-01 至 2008-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7610018
• 关键词: Agriculture; Algorithms; Area; Benchmarking; Bioinformatics; Biological; Biological Sciences; Chemicals; Computer Retrieval of Information on Scientific Projects Database; Data; Data Set; Disease; Funding; Goals; Grant; Health; Human; Institution; Internet; Mentors; Mutagenesis; NMR Spectroscopy; Pharmacologic Substance; Production; Property; Protein Databases; Protein Sequence Analysis; Protein Structure Databases; Proteins; Proteomics; Rate; Reaction; Research; Research Personnel; Resources; Sampling; Services; Source; Structure; Techniques; Tertiary Protein Structure; Time; United States National Institutes of Health; X-Ray Crystallography; base; blind; comparative; coping; cost; design; improved; mutant; numb protein; plant genetics; protein structure; research study; structural genomics

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The structure of the molecules determines their possible reactions. Structural genomics studies protein structures and infers their functions based on structure. Protein threading is a comparative proteomic approach that determines an unknown proteins tertiary structure. However, it is difficult to cope with the inefficiency of this approach without compromising accuracy, especially for structure search in large protein databases. Currently, protein structure is predicted with ~80% accuracy. Other techniques such as X-ray crystallography and NMR spectroscopy are expensive and have low throughput. The goal of this proposed research in bioinformatics is to develop efficient parameterized algorithms for protein tertiary structure prediction. By identifying small parameters from the analysis of protein sequence and structure properties, parameterized approaches have the advantage of being very efficient, i.e., having low computational cost compared to the other traditional approaches such as approximation algorithms and statistical approaches. The parameterized approach could determine a large number of protein structures in a high throughput mode. The specific aims of the proposed research include the following: Aim 1: We design and implement efficient parameterized algorithms for protein tertiary structure prediction. Implementations will be made publicly available through a web services interface. Using sample techniques, from existing protein structure databases, the algorithms accuracy will be analyzed and compared to other available algorithms. Aim 2: Based on biological data provided by the mentor and other publicly-accessible sources, the parameterized algorithms will be improved to increase their accuracy with the goal of exceeding the current benchmark of an 80% predictive rate. Aim 3: Applying the implemented algorithms to the mentors data sets, we will predict protein tertiary structures which can be used to improve mutant protein stability in mutagenesis studies. The proposed research could provide useful information to tremendously reduce the time and expenses on doing biological experiments on blind prediction. Combined with physico-chemical analysis of protein structures, the proposed research has the potential to enable important biological discoveries, which could positively impact scientific discovery in the areas of biological science such as agricultural plant genetics, new pharmaceuticals design, and new protein production related to human health and disease.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 分子的结构决定了它们可能的反应。结构基因组学研究蛋白质结构，并根据结构推断其功能。蛋白质穿线是一种比较蛋白质组学方法，的三级结构。然而，这是很难科普这种方法的效率低下，而不影响准确性，特别是在大型蛋白质数据库中的结构搜索。目前，蛋白质结构预测的准确率约为80%。其他技术，如X射线晶体学和NMR光谱学是昂贵的，并具有低吞吐量。本研究的目标是在生物信息学中开发高效的蛋白质三级结构预测的参数化算法。通过从蛋白质序列和结构特性的分析中识别小参数，参数化方法具有非常有效的优点，即，与诸如近似算法和统计方法的其它传统方法相比具有低计算成本。 参数化方法可以在高通量模式下确定大量蛋白质结构。 本研究的具体目标如下：目标1：设计并实现高效的蛋白质三级结构预测参数化算法。将通过一个网络服务接口公开提供这些实现。使用样本技术，从现有的蛋白质结构数据库，算法的准确性将进行分析，并与其他可用的算法进行比较。目标二：根据导师和其他公开来源提供的生物数据，将改进参数化算法，以提高其准确性，目标是超过80%的预测率。目标3：将实现的算法应用于导师的数据集，我们将预测蛋白质的三级结构，可用于提高突变蛋白质的稳定性，在诱变研究。该研究可以提供有用的信息，大大减少了进行盲预测生物实验的时间和费用。结合蛋白质结构的物理化学分析，拟议的研究有可能实现重要的生物学发现，这可能会对生物科学领域的科学发现产生积极影响，如农业植物遗传学，新药设计以及与人类健康和疾病相关的新蛋白质生产。