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.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 背景资料：蛋白质相互作用的预测是阐明蛋白质功能和理解细胞内分子机制的重要一步。虽然用于识别这些相互作用的实验方法仍然昂贵且经常有噪音，但解决的3D蛋白质结构的数量不断增加表明，预测两种蛋白质结构之间相互作用的计算机方法将在筛选候选相互作用对中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，并且在不久的将来不会扩展到在相互作用组水平上检测相互作用，涉及数百万个候选蛋白质对。 结果：在这里，我们描述了一种计算方法来有效地预测两个蛋白质结构是否相互作用。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，“这两个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来区分相互作用和非相互作用的蛋白质对。我们证明了我们的基于结构的方法在这项任务上表现良好，并且可以很好地扩展到相互作用体的大小。 结论：使用结构信息预测蛋白质相互作用产生显着更好的性能比其他基于序列的方法。在基于结构的分类器中，SVM算法，结合度量学习成对核和猛犸核，在我们的实验中表现最好。