Predicting putative protein-protein interface segments

描述（由申请人提供）：日常专家从蛋白质家族的模式中得出关于蛋白质功能和结构的假设。希望是推测蛋白X是否与Y结合，如果是，在哪里。两种公布的方法 解决了从序列预测蛋白质相互作用的目标。第一个简单地使用特定区域， 疏水矩图表明结合界面;它预测了太多的假定界面（66%的 所有残留物）。第二个预测接口的共同出现的序列基序和结构域，即通过 巧妙地利用现有的实验信息。显然，这种方法不能预测新的 交互.在这里，我们建议开发预测界面片段的方法，即连续的残基区域， 与其他接口段接触的序列。我们提出了不同的方法为内部和 外部接口。我们最近表明，内部，链链，蛋白质-蛋白质界面 平均而言，各部分不同。因此，我们的第一个目标是根据以下组合来预测接口类型： 序列特征和预测性质（二级结构、可及性和翻译后 修改）。这样的预测已经为功能区域提供了第一个提示，并将简化下一步。第二个目标将是开发一个系统，表明可能的蛋白质-蛋白质相互作用 伴侣，以及，作为推定的低聚物接口。第三个目标是开发新的方法， 内部接口之间的距离。这最后一步有望补充外部预测 界面，并将有助于预测蛋白质结构的方法。探索的基本手段将是使用多序列比对的统计学和神经网络的组合。我们的目标是一个低分辨率的预测成功往往足以区分内部和外部接口，以协助分子和医学生物学实验的设计。在最坏的情况下，我们期望开发新的技术，使实验人员能够正确地识别最有可能的蛋白质-蛋白质相互作用的片段的大部分时间。在最好的情况下，我们希望引入允许自动发现整个蛋白质组并大大帮助结构预测的方法。

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.