Protein structure from theory and experiment

描述（由申请人提供）：蛋白质结构介导蛋白质功能，并最终介导组织行为。计算和实验方法的补充是必要的，以确定从全基因组测序项目中获得的大量蛋白质序列的结构。我们提出了一种新的方法，将核磁共振（NMR）蛋白质实验中容易获得的数据与我们的预测方法相结合，以快速准确地建立结构模型。具体来说，我们的目标是：1。使用化学移位、j -耦合、未分配NOE数据和基于序列的算法自动分配二级结构。我们将使用神经网络来高效准确地组合不同的数据集。2. 通过将二级结构、化学位移、j -耦合和数据库趋势转化为主角概率分布来采样蛋白质构象空间。这些由神经网络生成的分布将用于对我们的新方法对给定蛋白质序列探索的样本空间进行偏置，以便遇到与输入数据一致的大部分天然类构象。3. 通过将核磁共振数据与现有的统计和物理功能相结合，选择最像原生的构象。核磁共振评分函数将基于主角和模拟NOE谱与计算概率分布和输入NOE数据的相似度。4. 改进构象集成的质量，自动分配NOE数据以获得非局部约束。最佳得分构象的模拟光谱将用于获得初始约束子集，该约束子集将被纳入新构象的生成中，从而迭代分配NOE数据并提高构象的质量，直到获得与输入数据拟合的最终结构集。5.以稳健和公正的方式测试所开发的方法。我们将建立内部测试机制，以避免对特定类别的蛋白质产生偏见；将预测的组成部分与整体预测分开评估，以确定哪些有效，哪些需要进一步改进；并对我们的方法进行持续的基准测试。使核磁共振实验员能够提交序列，我们将使用上述方法进行预测。我们将利用数据库驱动的接口在万维网上发布所生产的软件和所获得的信息。

DESCRIPTION (provided by applicant): Protein structure mediates protein function and, ultimately, organismal behavior. A complement of computational and experimental approaches is necessary to determine structures for the large numbers of protein sequences available from whole genome sequencing projects. We propose a novel approach to integrate easily-obtained data from Nuclear Magnetic Resonance (NMR) experiments on proteins with our prediction methodologies to accurately model structures in a rapid manner. Specifically, our aims are to: 1. Automate secondary structure assignment using chemical shift, J-coupling, unassigned NOE data and sequence based algorithms. We will use neural networks to efficiently and accurately combine the different datasets. 2. Sample protein conformational space by translating secondary structure, chemical shift, J-coupling and database tendencies into backbone angle probability distributions. These distributions, generated using neural networks, will be used to bias the sample space explored by our de novo methods for a given protein sequence such that a large proportion of native-like conformations consistent with the input data are encountered. 3. Select the most native-like conformations by combining NMR data with existing statistical and physical functions. NMR scoring functions will be based on the similarity of backbone angles and simulated NOE spectra with the calculated probability distributions and the input NOE data. 4. Refine the quality of the conformational ensemble automatically assigning the NOE data to obtain non-local constraints. The simulated spectra from the best scoring conformations will be used to obtain an initial subset of constraints which will be incorporated into the generation of new conformations, thus iteratively assigning the NOE data and improving the quality of the conformations until a final set of structures fitting the input data is obtained. 5.Test the methods developed in a robust and unbiased manner. We will set up internal testing mechanisms that avoid bias to particular classes of proteins; evaluate components of predictions separately from whole predictions to identify those that work well and those that need further improvement; and perform continuous benchmarking of our methods 6. Enable NMR experimentalists to submit sequences for which we will make prediction using the methods described above. We will publish the software produced, and the information obtained, using database driven interfaces on the world wide web.