Understanding the Book of Life: Non-Local Interaction Modeling for Protein Beta-Sheets and Its Integration to Protein Structure Prediction

理解生命之书：蛋白质β-折叠层的非局部相互作用建模及其与蛋白质结构预测的集成

• 批准号: 0728808
• 负责人: Yucel Altunbasak
• 金额: $ 19.82万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0728808&HistoricalAwards=false
• 关键词: Understanding; Book; Life; Non; Local

Massive amounts of protein sequence data are derived from such large-scale DNA sequencing efforts as the Human Genome Project. However, the rate of new protein sequences is growing exponentially relative to that of protein structures being solved by experimental methods. Once a protein sequence has been determined, deducing its unique three-dimensional native structure is a daunting task. Such experimental methods to determine detailed protein structure as x-ray diffraction studies and nuclear magnetic resonance analysis are highly labor intensive. Computational approaches offer a considerably faster and cheaper alternative. However, despite substantial efforts, the protein folding still remains largely unsolved.Prediction of the three-dimensional structure greatly benefits from information related to secondary structure, solvent accessibility, and non-local bonds that stabilize a protein's structure. Thus, prediction of such components is vital to our understanding of the structure and function of a protein. In this context, the investigators focus on protein secondary structure prediction, beta-sheet topology prediction, and contact map prediction. They study a Bayesian beta-sheet model to characterize the non-local interactions in beta-sheets. Starting with the secondary structure and solvent accessibility predictions, the model scores possible beta-sheet architectures by considering groupings of beta-strands into beta-sheets, spatial arrangement of beta-strands in each sheet, interaction types of beta-strands, and base pairing patterns of the amino acid residues on interacting segment pairs.The investigators also improve the accuracy of secondary structure predictions by modeling the non-local interactions in beta-sheets. This is achieved by a two stage approach. First, suboptimal segmentations of secondary structure are generated from a hidden semi-Markov model. Then, each segmentation score is updated using the beta-sheet model. Finally, the prediction is computed by applying a voting procedure.

大量的蛋白质序列数据来源于大规模的DNA测序工作，如人类基因组计划。然而，相对于用实验方法解决的蛋白质结构，新的蛋白质序列的速度呈指数增长。一旦确定了蛋白质序列，推断其独特的三维天然结构是一项艰巨的任务。诸如x射线衍射研究和核磁共振分析等确定蛋白质详细结构的实验方法是高度劳动密集型的。计算方法提供了一个更快更便宜的选择。然而，尽管付出了巨大的努力，蛋白质折叠在很大程度上仍未得到解决。三维结构的预测很大程度上得益于与二级结构、溶剂可及性和稳定蛋白质结构的非局部键相关的信息。因此，预测这些成分对我们理解蛋白质的结构和功能至关重要。在此背景下，研究人员将重点放在蛋白质二级结构预测、β -薄片拓扑预测和接触图预测上。他们研究了贝叶斯β -片模型来描述β -片中的非局部相互作用。从二级结构和溶剂可及性预测开始，该模型通过考虑β -链在β -片中的分组、β -链在每个片中的空间排列、β -链的相互作用类型以及相互作用片段对上氨基酸残基的碱基配对模式，对可能的β -片结构进行评分。研究人员还通过模拟β -薄片中的非局部相互作用来提高二级结构预测的准确性。这是通过两阶段方法实现的。首先，利用隐式半马尔可夫模型生成二级结构的次优分割。然后，使用beta-sheet模型更新每个分割分数。最后，通过投票程序计算预测结果。