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Prediction of protein structures and protein-protein interactions: A bioinformatics approach.

机译:蛋白质结构和蛋白质-蛋白质相互作用的预测:一种生物信息学方法。

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Prediction of protein structures from sequences and protein-protein interaction from structures are two grand challenges for computational biologists in the genomic era. Both remain unsolved problems despite considerable effort. Thus, simplification of the problems, i.e. prediction of solvent accessibility (exposed or buried) and protein-protein interface residues can be useful as a first step and aid the structure modeling processes.; We refined of a suite of five methods in solvent accessibility prediction over a large dataset and proposed a metamethod based on an ensemble average of the individual methods, leading to a two-state classification accuracy of 80%. Our results are 0.7--6.7% better than current state-of-art methods. Results suggest solvent accessibility prediction could be a valuable tool in improving protein structure prediction. We applied these methods for predicting sites of deleterious mutations and 80% accuracy was achieved, suggesting that the methods for prediction of solvent accessibility can be the basis for accurate predictions of deleterious mutations.; We developed of a robust program, cons-PPISP, for predicting interface residues in protein-protein complexes. Having successfully improved the prediction accuracy from 70% to 80%, we showed potentially it can complement experimental methods such as chemical-shift perturbations in characterizing protein-protein interfaces. Incorporation of cons-PPISP predictions in a docking program in the latest CAPRI rounds demonstrated its ability in guiding the docking process.
机译:根据序列预测蛋白质结构以及根据蛋白质结构相互作用,是基因组时代计算生物学家面临的两个重大挑战。尽管付出了巨大的努力,但这两个问题仍未解决。因此,简化问题,即预测溶剂可及性(暴露的或埋藏的)和蛋白质-蛋白质界面残基的预测可以用作第一步,并有助于结构建模过程。我们对大型数据集上的溶剂可及性预测中的五种方法进行了改进,并基于单个方法的总体平均值提出了一种元方法,从而使两种状态的分类精度达到80%。我们的结果比目前的最新方法好0.7--6.7%。结果表明溶剂可及性预测可能是改善蛋白质结构预测的有价值的工具。我们将这些方法用于预测有害突变的位点,并达到了80%的准确性,这表明预测溶剂可及性的方法可以作为准确预测有害突变的基础。我们开发了一个强大的程序cons-PPISP,用于预测蛋白质-蛋白质复合物中的界面残基。成功地将预测准确度从70%提高到80%,我们证明了它可以补充实验方法,例如表征蛋白质-蛋白质界面的化学位移扰动。在最新的CAPRI轮次中,将cons-PPISP预测结合到对接程序中,证明了其在引导对接过程中的能力。

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