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Improving prediction of heterodimeric protein complexes using combination with pairwise kernel
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836830/ https://www.ncbi.nlm.nih.gov/pubmed/29504897 http://dx.doi.org/10.1186/s12859-018-2017-5 |
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author | Ruan, Peiying Hayashida, Morihiro Akutsu, Tatsuya Vert, Jean-Philippe |
author_facet | Ruan, Peiying Hayashida, Morihiro Akutsu, Tatsuya Vert, Jean-Philippe |
author_sort | Ruan, Peiying |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5836830 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-58368302018-03-07 Improving prediction of heterodimeric protein complexes using combination with pairwise kernel Ruan, Peiying Hayashida, Morihiro Akutsu, Tatsuya Vert, Jean-Philippe BMC Bioinformatics Research 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. BioMed Central 2018-02-19 /pmc/articles/PMC5836830/ /pubmed/29504897 http://dx.doi.org/10.1186/s12859-018-2017-5 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Ruan, Peiying Hayashida, Morihiro Akutsu, Tatsuya Vert, Jean-Philippe Improving prediction of heterodimeric protein complexes using combination with pairwise kernel |
title | Improving prediction of heterodimeric protein complexes using combination with pairwise kernel |
title_full | Improving prediction of heterodimeric protein complexes using combination with pairwise kernel |
title_fullStr | Improving prediction of heterodimeric protein complexes using combination with pairwise kernel |
title_full_unstemmed | Improving prediction of heterodimeric protein complexes using combination with pairwise kernel |
title_short | Improving prediction of heterodimeric protein complexes using combination with pairwise kernel |
title_sort | improving prediction of heterodimeric protein complexes using combination with pairwise kernel |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836830/ https://www.ncbi.nlm.nih.gov/pubmed/29504897 http://dx.doi.org/10.1186/s12859-018-2017-5 |
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