Department of Informatics, Bioinformatics & Computational Biology, TUM (Technische Universität München)-I12, Boltzmannstr. 3, 85748 Garching/Munich, Germany.
Bioinformatics. 2015 Jun 15;31(12):1945-50. doi: 10.1093/bioinformatics/btv077. Epub 2015 Feb 4.
Many methods predict the physical interaction between two proteins (protein-protein interactions; PPIs) from sequence alone. Their performance drops substantially for proteins not used for training.
Here, we introduce a new approach to predict PPIs from sequence alone which is based on evolutionary profiles and profile-kernel support vector machines. It improved over the state-of-the-art, in particular for proteins that are sequence-dissimilar to proteins with known interaction partners. Filtering by gene expression data increased accuracy further for the few, most reliably predicted interactions (low recall). The overall improvement was so substantial that we compiled a list of the most reliably predicted PPIs in human. Our method makes a significant difference for biology because it improves most for the majority of proteins without experimental annotations.
Implementation and most reliably predicted human PPIs available at https://rostlab.org/owiki/index.php/Profppikernel.
许多方法仅从序列预测两个蛋白质（蛋白质-蛋白质相互作用；PPIs）之间的物理相互作用。对于未用于训练的蛋白质，它们的性能会大幅下降。
在这里，我们介绍了一种从序列预测 PPIs 的新方法，该方法基于进化轮廓和轮廓核支持向量机。它的性能优于最新技术，特别是对于与具有已知相互作用伙伴的蛋白质序列不同的蛋白质。通过基因表达数据过滤进一步提高了少数最可靠预测相互作用（低召回率）的准确性。整体改进非常显著，以至于我们编制了一份人类最可靠预测的 PPIs 列表。我们的方法对生物学有重大意义，因为它对大多数没有实验注释的蛋白质有显著的改进。
实现和最可靠预测的人类 PPIs 可在 https://rostlab.org/owiki/index.php/Profppikernel 上获得。
