LARGE-SCALE PREDICTION OF PROTEIN-PROTEIN INTERACTIONS FROM STRUCTURE

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 背景：蛋白质相互作用的预测是阐明蛋白质功能和了解细胞内分子机制的重要一步。尽管识别这些相互作用的实验方法仍然昂贵且往往存在噪音，但越来越多的3D蛋白质结构解析表明，在计算机技术中，预测两个蛋白质结构之间的相互作用的方法将在筛选候选相互作用对方面发挥越来越重要的作用。使用结构知识的方法可能比仅基于序列的方法更准确。基于对接蛋白质结构的方法解决了这个问题的一个变体，但这些方法仍然非常计算密集，在不久的将来不会扩展到检测涉及数百万候选蛋白质对的相互作用组水平上的相互作用。 结果：在这里，我们描述了一种在计算机中有效地预测两个蛋白质结构是否相互作用的计算方法。这个是/否的问题大概比标准的蛋白质对接问题更容易回答，“这两种蛋白质结构是如何相互作用的？”我们的方法是使用一种统计模式识别方法来区分相互作用和非相互作用的蛋白质对，这种方法被称为支持向量机(SVM)。我们证明了我们的基于结构的方法在这个任务上执行得很好，并且可以很好地扩展到交互组的大小。 结论：使用结构信息来预测蛋白质相互作用产生了比其他基于序列的方法更好的性能。在基于结构的分类器中，结合度量学习成对核和最大核的支持向量机算法在我们的实验中表现最好。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Background: The prediction of protein-protein interactions is an important step toward the elucidation of protein functions and the understanding of the molecular mechanisms inside the cell. While experimental methods for identifying these interactions remain costly and often noisy, the increasing quantity of solved 3D protein structures suggests that in silico methods to predict interactions between two protein structures will play an increasingly important role in screening candidate interacting pairs. Approaches using the knowledge of the structure are presumably more accurate than those based on sequence only. Approaches based on docking protein structures solve a variant of this problem, but these methods remain very computationally intensive and will not scale in the near future to the detection of interactions at the level of an interactome, involving millions of candidate pairs of proteins. Results: Here, we describe a computational method to predict efficiently in silico whether two protein structures interact. This yes/no question is presumably easier to answer than the standard protein docking question, "How do these two protein structures interact?" Our approach is to discriminate between interacting and non-interacting protein pairs using a statistical pattern recognition method known as a support vector machine (SVM). We demonstrate that our structure-based method performs well on this task and scales well to the size of an interactome. Conclusions: The use of structure information for the prediction of protein interaction yields significantly better performance than other sequence-based methods. Among structure-based classifiers, the SVM algorithm, combined with the metric learning pairwise kernel and the MAMMOTH kernel, performs best in our experiments.