New Scoring, Assembly and Evaulation Techiniques for Protein Structure Prediction

描述(由申请人提供)：R21/R33提案的目标是开发基于微线程方法的蛋白质结构预测的新模型、评分方案和技术。在R21阶段，我们将专注于我们新方法的原则证明开发。首先，我们将开发新的统计模型和计算方法来识别PDB中用于查询蛋白质的有用片段。特别是，我们将根据统计上有意义的匹配来识别PDB中不同长度的蛋白质片段，而不是像现有方法那样将片段限制在9-MERS。其次，除了当前线程方法中使用的角度约束外，我们还将根据查询序列与其已知结构的片段命中在笛卡尔坐标下的比对来制定空间约束。第三，我们将研究新的优化问题公式，以建立粗粒度结构模型。具体地说，我们将定制先进的优化技术，如半定规划和进化算法，以找到组装局部结构的有效方法。第四，我们将通过对样本构象、相关突变和神经网络进行聚类来评估预测蛋白质结构的可信度。第五，我们将为选定的粗粒度模型建立全原子结构模型，并进一步使用原子结构在扰动(如高温或力)下的性质来评估模型。在R33阶段，我们将重点评估、改进、推广和应用在R21阶段开发的方法。首先，我们将对方法进行大规模评估，并在评估和测试的基础上细化方法。其次，我们将把这些方法作为一个独立的软件包进行公开分发，并提供一个Web服务器供公众使用。第三，我们将把我们的方法扩展到膜蛋白的结构预测。最后，我们将选择对人类健康有重大影响的蛋白质，如CFTR通道、SARS基因组中编码的蛋白质、斜视(STBM)/梵高(Vang)蛋白质、ARC超家族等，这些新技术可能会在节省计算时间的同时显著提高蛋白质结构预测的准确性。它们将延伸到膜蛋白，其结构已被许多疾病的主要药物靶标研究不足。我们的研究将揭示一系列关键人类蛋白质的结构和功能，这可能有助于研究人员确定疾病基因的特征，并开发新的治疗方法，从而节省大量资源。

DESCRIPTION (provided by applicant): The goal of this R21/R33 proposal is to develop novel models, scoring schemes, and techniques based on the mini-threading approach for protein structure prediction. During the R21 phase, we will focus on the proof-of-principle development for our new methods. First, we will develop new statistical models and computational methods to identify useful fragments in PDB for a query protein. In particular, we will identify protein fragments of variable lengths in PDB according to statistically significant matches instead of limiting the fragments to 9-mers as practiced by existing methods. Second, besides angular restraints used in the current threading methods, we will formulate spatial restraints derived from the alignments between a query sequence and its fragment hits of known structures in Cartesian coordinates. Third, we will investigate new optimization problem formulations to build coarse-grain structural models. Specifically, we will tailor advanced optimization techniques, such as semidefinite programming and evolutionary algorithms, to find the efficient methods of assembling local structures. Fourth, we will evaluate confidence of predicted protein structures through clustering sampled conformations, correlated mutation, and neural networks. Fifth, we will build all-atom structural models for selected coarse-grain models, and further evaluate the models using properties of atomic structures under perturbation (e.g., high temperature or force). During the R33 phase, we will focus on the evaluation, refinement, extension and application of the methods developed during the R21 phase. First, we will perform large-scale evaluations of the methods, and we will refine the methods based on the evaluations and tests. Second, we will implement the methods as a stand-alone software package for public distribution and a Web server available for the public. Third, we will expand our methods to structure prediction of membrane proteins. Finally, we will apply the methods to selected proteins that have significant impact to human health, such as CFTR channels, proteins coded in the SARS genome, strabismus (stbm)/van Gogh (Vang) protein, ARC superfamily, etc. The new techniques may significantly increase the accuracy of the protein structure prediction whiling saving computing time. They will extend to membrane proteins, whose structures have understudied by major drug targets for many diseases. Our studies will shed some light on the structures and functions of a set of key human proteins, which may help researchers characterize disease genes and develop new treatment with substantial savings of resources.