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。 分子的结构决定了它们可能发生的反应。结构基因组学研究蛋白质结构，并根据结构推断其功能。蛋白质穿线是一种确定未知蛋白质三级结构的比较蛋白质组学方法。然而，这种方法很难在不影响精度的情况下解决效率低下的问题，特别是对于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射线结晶学和核磁共振光谱学，价格昂贵，产量低。这项生物信息学研究的目标是开发有效的蛋白质三级结构预测的参数化算法。通过对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，具有较低的计算代价。这种参数化方法可以在高通量模式下确定大量的蛋白质结构。 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Mentors数据集，我们将预测蛋白质的三级结构，这些结构可用于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. 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.