Multimeric threading-based prediction of protein-protein interactions on a genomic scale:: Application to the Saccharomyces cerevisiae proteome
Multimeric threading-based prediction of protein-protein interactions on a genomic scale:: Application to the Saccharomyces cerevisiae proteome
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DOI：
10.1101/gr.1145203
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发表时间：
2003-06-01
期刊：
影响因子：
7
通讯作者：
Skolnick, J
中科院分区：
文献类型：
--
作者：
Lu, L;Arakaki, AK;Skolnick, J
将预测蛋白质-蛋白质相互作用的多聚体线程算法--多探索者算法应用于酿酒酵母基因组。通过使用折叠分配的置信度估计和统计界面势的大小，对照包含768个复杂结构的二聚体数据库来评估6000多个编码蛋白质之间的每一种可能的成对相互作用。总体而言，基于304个复杂结构，预测了7321对不同蛋白质之间的相互作用。基于亚细胞定位和预测相互作用的生物功能的一致性进行的质量评估表明，与所有其他大规模方法相比，我们的方法排名第三。与其他电子方法不同，MULTIPROSPECTOR能够识别直接参与相互作用的残基。我们的374的预测可以通过至少一项其他研究找到，这与两种不同的其他方法之间的重叠是一致的。从对信使核糖核酸丰度数据的分析来看，我们的方法不偏向于高丰度的蛋白质。最后，给出了各种函数所涉及的几个相关预测。总之，我们提供了一种在基因组水平上预测蛋白质-蛋白质相互作用的新方法，这是对实验方法的有益补充。
MULTI PROSPECTOR, a Multimeric threading algorithm for the prediction of protein-protein interactions, is applied to the genome of Saccharomyces cerevisiae. Each possible pairwise interaction among more than 6000 encoded proteins is evaluated against a dimer database of 768 complex structures by using a confidence estimate of the fold assignment and the magnitude of the statistical interfacial potentials. In total, 7321 interactions between pairs of different proteins are predicted, based on 304 complex structures. Quality estimation based on the coincidence Of subcellular localizations and biological functions of the predicted interactors shows that our approach ranks third when compared with all other large-scale methods. Unlike other in silico methods, MULTIPROSPECTOR is able to identify the residues that participate directly in the interaction. Three hundred seventy-four Of Our predictions can be found by at least one of the other Studies, which is compatible with the overlap between two different other methods. From the analysis of the mRNA abundance data, Our method does not bias towards proteins with high abundance. Finally, several relevant predictions involved in various functions are presented. In summary, we provide a novel approach to predict protein-protein interactions on a genomic scale that is a useful complement to experimental methods.