Predicting putative protein-protein interface segments

描述(由申请人提供)：日常专家通过观察蛋白质家族的模式得出关于蛋白质功能和结构的假说。人们希望推测蛋白质X是否与Y结合，如果是，在哪里结合。已发表的两种方法解决了从序列预测蛋白质相互作用的目标。第一个简单地使用疏水矩图中的特定区域来建议结合界面；它预测了太多的假定界面(所有残基的66%)。第二种方法是根据序列基序和结构域的共同出现来预测界面，即以一种聪明的方式利用现有的实验信息。显然，这种方法不能预测新的相互作用。在这里，我们建议开发预测界面片段的方法，即序列中连续与其他界面片段接触的残基区域。我们为内部和外部接口提出了不同的方法。我们最近表明，内部、链链和蛋白质-蛋白质界面片段平均而言是不同的。因此，我们的第一个目标是基于序列特征和预测的属性(二级结构、可访问性和翻译后修饰)的组合来预测界面类型。这样的预测已经为功能区提供了第一个提示，并将简化下一步。第二个目标将是开发一个系统，该系统可以建议可能的蛋白质-蛋白质相互作用伙伴，以及假定的低聚物界面。第三个目标将是开发预测内部接口之间距离的新方法。这最后一步有望补充外部界面的预测，并将有助于预测蛋白质结构的方法。探索的基本方法将是使用多序列比对的统计学和神经网络的组合。我们的目标是一种低分辨率的预测，能够经常成功地区分内部和外部接口，以帮助设计分子和医学生物学的实验。在最坏的情况下，我们预计将开发新的技术，使实验者能够在大多数时间正确地识别最可能的蛋白质-蛋白质相互作用片段。在最好的情况下，我们希望引入方法，允许自动发现整个蛋白质组，并在很大程度上帮助结构预测。

DESCRIPTION (provided by applicant): Everyday experts derive hypotheses about protein function and structure from looking at patterns in protein families. The hope is to speculate whether protein X binds to Y, and if so, where. Two published methods address the goal of predicting protein interactions from sequence. The first simply uses a particular region in the hydrophobic moment plot to suggest binding interfaces; it predicts too many putative interfaces (66% of all residues). The second predicts interfaces from the co-occurrence of sequence motifs and domains, i.e. by using existing experimental information in a clever way. Obviously, this method cannot predict novel interactions. Here, we propose to develop methods predicting interface segments, i.e. regions of residues consecutive in sequence that are in contact with other interface segments. We propose separate methods for internal and external interfaces. We have recently shown that internal, chain-chain, and protein-protein interface segments differ - on average. Thus, our first aim is to predict the interface type based on a combination of sequence features and predicted properties (secondary structure, accessibility, and post-translational modifications). Such a prediction will already provide the first hint for functional regions, and it will simplify the next step. The second aim will be to develop a system that suggests possible protein-protein interaction partners, as well, as putative oligomer interfaces. The third goal will be to develop new methods that predict distances between internal interfaces. This final step will hopefully complement the prediction of external interfaces and will assist methods predicting protein structure. The basic means explored will be combinations of statistics and neural networks using multiple sequence alignments. The goal is a low-resolution prediction succeeding often enough to distinguish between internal and external interfaces to assist the design of experiments in molecular and medical biology. In the worst case, we anticipate to develop novel techniques that will enable experimentalists to correctly identify the most likely protein-protein interaction segments most of the time. In the best case, we hope to introduce methods that allow automatic discoveries for entire proteomes and considerably help structure prediction.