EFFICIENT ALGORITHMS FOR PROTEIN TERTIARY STRUCTURE PREDICTION

蛋白质三级结构预测的高效算法

• 批准号: 7725070
• 负责人: XI HUANG
• 金额: $ 1.74万
• 依托单位: UNIV OF ARKANSAS FOR MED SCIS
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2009-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7725070
• 关键词: Agriculture; Algorithms; Amino Acid Sequence; Area; Benchmarking; Bioinformatics; Biological; Biological Sciences; Biopolymers; Chemicals; Computer Retrieval of Information on Scientific Projects Database; Data; Data Set; Databases; Disease; Funding; Genome; Goals; Grant; Health; Homologous Gene; Human; Institution; Internet; Mentors; Methods; Molecular Conformation; Mutagenesis; Peptide Sequence Determination; Pharmacologic Substance; Production; Property; Protein Structure Databases; Proteins; RNA; RNA analysis; Rate; Research; Research Personnel; Resources; Sampling; Sequence Alignment; Services; Source; Structure; Techniques; Tertiary Protein Structure; Time; United States National Institutes of Health; base; blind; coping; cost; design; experimental analysis; improved; insight; mutant; novel; novel strategies; plant genetics; protein structure; protein structure prediction; research study

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. Computational alignment of a biopolymer sequence (e.g., an RNA or a protein) to a structure is an effective approach to predict and search for the structure of new sequences. To identify the structure of remote homologs, the structure-sequence alignment has to consider not only sequence similarity but also spatially conserved conformations caused by residue interactions, and consequently is computationally intractable. It is difficult to cope with the inefficiency without compromising alignment accuracy, especially for structure search in genomes or large databases. The goal of this proposed research in bioinformatics is to introduce novel methods and develop efficient parameterized algorithms for RNA/protein structure prediction. By identifying small parameters from the analysis of RNA/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 specific aims of the proposed research include the following: Aim 1: We introduce novel approaches and design efficient parameterized algorithms for RNA/protein structure prediction. The efficiency and accuracy of our algorithm will be analyzed and compared to other available approaches. Our preliminary experimental results demonstrate our algorithm is very efficient for RNA structural search. Aim 2: Based on biological data provided by the mentor in UALR, collaborators in ASU, and other publicly-accessible sources, the parameterized algorithms will be improved to increase their accuracy. Aim 3: Applying the implemented algorithms to the biological data sets provided by the mentor in UALR, and collaborators in ASU, we will predict RNA/protein structures which can be used to provide insights information to improve biological studies. Combined with biological experimental analysis, the proposed research has the potential to enable important biological discoveries. 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 mentor's 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。 生物聚合物序列的计算比对（例如，RNA或蛋白质）与结构的关系是预测和搜索新序列结构的有效方法。为了识别远程同源物的结构，结构-序列比对不仅要考虑序列相似性，还要考虑由残基相互作用引起的空间保守构象，因此在计算上是难以处理的。在不影响比对精度的情况下，很难科普低效率，特别是对于基因组或大型数据库中的结构搜索。 生物信息学研究的目标是引入新的方法，并开发有效的参数化算法用于RNA/蛋白质结构预测。通过从RNA/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。 具体目标包括以下几个方面：目标1：我们引入新的方法和设计有效的参数化算法的RNA/蛋白质结构预测。我们的算法的效率和准确性进行了分析，并与其他可用的方法。初步的实验结果表明，该算法对RNA结构搜索是非常有效的。目标二：基于UALR导师、ASU合作者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。目标三：将实现的算法应用于UALR导师和ASU合作者提供的生物数据集，我们将预测RNA/蛋白质结构，这些结构可用于提供见解信息，以改善生物学研究。结合生物实验分析，拟议的研究有可能实现重要的生物学发现。 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。目标三：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，其可用于在诱变研究中提高突变蛋白质的稳定性。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。