EFFICIENT ALGORITHMS FOR PROTEIN TERTIARY STRUCTURE PREDICTION

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 生物聚合物序列(例如，RNA或蛋白质)与结构的计算比对是预测和搜索新序列结构的有效方法。为了识别远端同源物的结构，结构-序列比对不仅要考虑序列相似性，而且要考虑由残基相互作用引起的空间保守构象，因此在计算上是困难的。很难在不影响比对精度的情况下解决效率低下的问题，特别是对于基因组或大型数据库中的结构搜索。 这项生物信息学研究的目标是引入新的方法和开发有效的参数化算法来预测RNA/蛋白质结构。通过对RNA/蛋白质序列和结构性质的分析来识别小参数，参数化方法具有非常高效的优点，即与其他传统方法如近似算法和统计方法相比，具有较低的计算成本。 本研究的具体目标包括以下几个方面：目标1：引入新的RNA/蛋白质结构预测方法并设计高效的参数化算法。我们将对算法的效率和准确性进行分析，并与其他可用的方法进行比较。我们的初步实验结果表明，该算法对于RNA结构搜索是非常有效的。目的2：基于UALR的导师、亚利桑那州立大学的合作者以及其他可公开获取的来源提供的生物学数据，将对参数化算法进行改进，以提高其准确性。目的3：将所实现的算法应用于UALR的导师和亚利桑那州立大学的合作者提供的生物数据集，我们将预测RNA/蛋白质结构，这些结构可用于为改进生物学研究提供见解信息。结合生物学实验分析，拟议的研究有可能实现重要的生物学发现。 本研究的具体目标包括以下几个方面：目标1：设计并实现高效的蛋白质三级结构预测的参数化算法。实现将通过Web服务接口公开提供。利用样本技术，从现有的蛋白质结构数据库中，分析算法的准确性，并与其他可用的算法进行比较。目的2：基于导师和其他公开来源提供的生物学数据，将改进参数化算法，以提高其准确性，目标是超过目前80%的预测率基准。目的3：将所实现的算法应用于Mentor的数据集，我们将预测蛋白质的三级结构，这些结构可用于在突变研究中提高突变蛋白质的稳定性。建议的研究可以提供有用的信息，极大地减少进行盲目预测的生物学实验的时间和费用。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，这些发现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、新药物设计以及与人类健康和疾病相关的新蛋白质生产。

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.