Improving prediction of heterodimeric protein complexes using combination with pairwise kernel

• 阿久津, 達也

  Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)

• Hayashida, Morihiro

  Department of Electrical Engineering and Computer Science, National Institute of Technology, Matsue College

• Akutsu, Tatsuya

  Bioinformatics Center, Institute for Chemical Research, Kyoto University

• Vert, Jean-Philippe

  MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology･Institut Curie･INSERM U900･Ecole Normale Supérieure, Department of Mathematics and Applications

抄録

[Background] Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. [Results] In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. [Conclusions] We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

収録刊行物

• BMC Bioinformatics

  BMC Bioinformatics 19 (Supplement 1), 2018-02-19

  Springer Nature

キーワード

• Heterodimeric protein complex
• Combination kernel
• Pairwise kernel

詳細情報

• CRID: 1050282813184515072
• NII論文ID: 120006537092
• ISSN: 14712105
• DOI: 10.1186/s12859-018-2017-5
• HANDLE: 2433/235018
• Web Site: http://link.springer.com/content/pdf/10.1186/s12859-018-2017-5.pdf
• 本文言語コード: en
• 資料種別: journal article
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