New Scoring, Assembly and Evaulation Techiniques for Protein Structure Prediction

描述（由申请人提供）：该R21/R33提案的目标是开发基于用于蛋白质结构预测的迷你线程方法的新型模型、评分方案和技术。在R21阶段，我们将专注于新方法的原理验证开发。首先，我们将开发新的统计模型和计算方法，以确定查询蛋白质的PDB中的有用片段。特别地，我们将根据统计学上显著的匹配来鉴定PDB中可变长度的蛋白质片段，而不是如现有方法所实践的那样将片段限制为9聚体。其次，除了在目前的线程方法中使用的角度限制，我们将制定来自查询序列和已知结构的片段命中在笛卡尔坐标系之间的对齐的空间约束。第三，我们将研究新的优化问题公式来构建粗粒度结构模型。具体来说，我们将定制先进的优化技术，如半定规划和进化算法，找到组装局部结构的有效方法。第四，我们将通过聚类采样构象，相关突变和神经网络来评估预测蛋白质结构的置信度。第五，我们将为选定的粗粒度模型构建全原子结构模型，并使用微扰下原子结构的特性（例如，高温或力）。在R33阶段，我们将专注于R21阶段开发的方法的评估，改进，扩展和应用。首先，我们将对这些方法进行大规模的评估，并根据评估和测试来改进这些方法。其次，我们将把这些方法实现为一个独立的软件包，用于公共分发和一个可供公众使用的Web服务器。第三，我们将我们的方法扩展到膜蛋白的结构预测。最后，我们将适用于选定的蛋白质，有显着影响人类健康，如CFTR通道，编码的SARS基因组中的蛋白质，斜视（stbm）/货车高（旺）蛋白，ARC超家族等的新技术可能会显着提高蛋白质结构预测的准确性，同时节省计算时间。他们将扩展到膜蛋白，其结构尚未被许多疾病的主要药物靶标研究。我们的研究将揭示一组关键人类蛋白质的结构和功能，这可能有助于研究人员表征疾病基因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.