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

大量的蛋白质序列数据来自于人类基因组计划等大规模DNA测序工作。然而，新的蛋白质序列的速度相对于通过实验方法解决的蛋白质结构的速度呈指数增长。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。确定详细蛋白质结构的实验方法，如X射线衍射研究和核磁共振分析，是高度劳动密集型的。计算方法提供了一种更快、更便宜的选择。然而，尽管有大量的努力，蛋白质的折叠仍然在很大程度上没有得到解决。预测的三维结构大大受益于二级结构，溶剂的可及性，和非本地键，稳定蛋白质的结构相关的信息。因此，预测这些组分对于我们理解蛋白质的结构和功能至关重要。在这种情况下，研究人员专注于蛋白质二级结构预测，β-折叠拓扑预测和接触图预测。 他们研究了贝叶斯β折叠模型来表征β折叠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-链分组为β-折叠、每个折叠中β-链的空间排列、β-链的相互作用类型，和相互作用片段对上氨基酸残基的碱基配对模式。研究人员还通过模拟非-β折叠中的局部相互作用。这是通过两阶段方法实现的。 首先，次优分割的二级结构产生的隐半马尔可夫模型。然后，使用β片层模型更新每个分割分数。最后，通过应用投票程序计算预测。

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.