LARGE-SCALE PREDICTION OF PROTEIN-PROTEIN INTERACTIONS FROM STRUCTURE

从结构大规模预测蛋白质-蛋白质相互作用

• 批准号: 8171275
• 负责人: William Noble
• 金额: $ 3.71万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171275
• 关键词: Algorithms; Base Sequence; Cells; Computer Retrieval of Information on Scientific Projects Database; Computer Simulation; Computing Methodologies; Detection; Docking; Funding; Future; Grant; Institution; Knowledge; Learning; Machine Learning; Methods; Metric; Molecular; Pattern Recognition; Performance; Play; Proteins; Research; Research Personnel; Resources; Role; Screening procedure; Source; Structure; United States National Institutes of Health; Variant; base; protein function; protein protein interaction; protein structure; research study

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.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 背景：蛋白质 - 蛋白质相互作用的预测是迈出蛋白质功能和对细胞内部分子机制的理解的重要步骤。虽然用于识别这些相互作用的实验方法仍然昂贵且常常嘈杂，但已解决的3D蛋白结构的数量增加表明，在预测两种蛋白质结构之间相互作用的硅方法中，在筛选候选候选对相互作用对中的作用越来越重要。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该问题的一种变体，但是这些方法在计算中仍然非常密集，并且不会在不久的将来以相互作用级别检测相互作用，涉及数百万候选蛋白质。 结果：在这里，我们描述了一种计算方法，可以在计算机中有效预测两个蛋白质结构是否相互作用。这是/否问题比标准蛋白对接问题更容易回答：“这两种蛋白质结构如何相互作用？”我们的方法是使用称为支持向量机（SVM）的统计模式识别方法区分相互作用和非相互作用蛋白对。我们证明了我们的基于结构的方法在此任务上表现良好，并且可以很好地扩展到相互作用组的大小。 结论：与基于其他基于序列的方法相比，使用结构信息用于预测蛋白质相互作用的性能明显更好。在基于结构的分类器中，SVM算法与度量学习成对内核和猛mm象内核相结合，在我们的实验中表现最好。