Protein structure from theory and experiment

理论和实验的蛋白质结构

• 批准号: 6635369
• 负责人: RAM SAMUDRALA
• 金额: $ 27.97万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 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/6635369
• 关键词: biotechnology; computer assisted sequence analysis; computer program /software; computer system design /evaluation; informatics; molecular biology information system; protein structure function; proteomics

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）：蛋白质结构介导蛋白质功能，并最终介导生物体行为。一个计算和实验方法的补充是必要的，以确定结构的大量蛋白质序列可从全基因组测序计划。我们提出了一种新的方法，将核磁共振（NMR）实验中容易获得的蛋白质数据与我们的预测方法相结合，以快速准确地建模结构。具体而言，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有效和准确地联合收割机组合不同的数据集。2.通过将二级结构、化学位移、J-耦合和数据库趋势转换为主链角概率分布，对蛋白质构象空间进行采样。这些分布，使用神经网络生成，将被用来偏置的样本空间探索我们的从头方法为一个给定的蛋白质序列，这样一个大比例的天然样构象与输入数据一致。3.通过将NMR数据与现有的统计和物理函数相结合，选择最接近天然的构象。NMR评分函数将基于主链角度和模拟NOE光谱与计算的概率分布和输入NOE数据的相似性。4.优化构象系综的质量，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用于获得约束的初始子集，其将被并入新构象的生成中，从而迭代地分配NOE数据并提高构象的质量，直到获得拟合输入数据的最终一组结构。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，以避免对特定类别蛋白质的偏见;将预测的组成部分与整体预测分开评估，以确定哪些工作良好，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NMR实验人员能够提交序列，我们将使用上述方法进行预测。我们将在万维网上使用数据库驱动界面发布所制作的软件和所获得的信息。