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Protein structure from theory and experiment

理论和实验的蛋白质结构

基本信息

批准号：
6888939
负责人：
RAM SAMUDRALA
金额：
$ 31.51万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2003
资助国家：
美国
起止时间：
2003-05-01 至 2006-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6888939
关键词：
biotechnology computer assisted sequence analysis computer program /software computer system design /evaluation informatics molecular biology information system protein structure function proteomics

项目摘要

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.

描述(由申请人提供)：蛋白质结构调节蛋白质功能，并最终调节生物行为。为了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，计算和实验方法的补充是必要的。我们提出了一种新的方法，将蛋白质核磁共振实验中容易获得的数据与我们的预测方法相结合，以快速准确地建模结构。具体地说，我们的目标是：1.使用化学位移、J偶联、未分配NOE数据和基于序列的算法自动进行二级结构分配。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偶联和数据库趋势转化为主链角度概率分布来采样蛋白质构象空间。这些使用神经网络产生的分布将被用来对我们的从头方法探索的给定蛋白质序列的样本空间进行偏置，以便遇到与输入数据一致的很大比例的天然类构象。3.通过将核磁共振数据与现有的统计和物理函数相结合来选择最接近天然的构象。核磁共振评分函数将基于主干角度和模拟的NOE谱与计算的概率分布和输入的NOE数据的相似性。4.改进构象系综的质量，自动分配NOE数据以获得非局部约束。来自最佳评分构象的模拟光谱将被用来获得约束的初始子集，该约束子集将被合并到新构象的生成中，从而迭代地分配NOE数据并改进构象的质量，直到获得适合输入数据的最终结构集。5.测试以稳健和不偏不倚的方式开发的方法。我们将建立内部测试机制，避免偏向特定类别的蛋白质；分别评估预测的组成部分和整体预测，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6.使核磁共振实验者能够提交序列，我们将使用上述方法对这些序列进行预测。我们将在万维网上使用数据库驱动的界面发布制作的软件和获得的信息。