Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.

Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2...

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Autores principales:	Kousik Kundu, Fabrizio Costa, Michael Huber, Michael Reth, Rolf Backofen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f35489c5c55e4b639f9e07db970d9c99
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spelling	oai:doaj.org-article:f35489c5c55e4b639f9e07db970d9c992021-11-18T07:45:22ZSemi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.1932-620310.1371/journal.pone.0062732https://doaj.org/article/f35489c5c55e4b639f9e07db970d9c992013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23690949/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2-domains, freely available to the scientific community under the following URLs: http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/SH2PepInt.tar.gz and http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/Genome-wide-predictions.tar.gz, respectively.Kousik KunduFabrizio CostaMichael HuberMichael RethRolf BackofenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 5, p e62732 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kousik Kundu Fabrizio Costa Michael Huber Michael Reth Rolf Backofen Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.
description	Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2-domains, freely available to the scientific community under the following URLs: http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/SH2PepInt.tar.gz and http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/Genome-wide-predictions.tar.gz, respectively.
format	article
author	Kousik Kundu Fabrizio Costa Michael Huber Michael Reth Rolf Backofen
author_facet	Kousik Kundu Fabrizio Costa Michael Huber Michael Reth Rolf Backofen
author_sort	Kousik Kundu
title	Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.
title_short	Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.
title_full	Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.
title_fullStr	Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.
title_full_unstemmed	Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.
title_sort	semi-supervised prediction of sh2-peptide interactions from imbalanced high-throughput data.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/f35489c5c55e4b639f9e07db970d9c99
work_keys_str_mv	AT kousikkundu semisupervisedpredictionofsh2peptideinteractionsfromimbalancedhighthroughputdata AT fabriziocosta semisupervisedpredictionofsh2peptideinteractionsfromimbalancedhighthroughputdata AT michaelhuber semisupervisedpredictionofsh2peptideinteractionsfromimbalancedhighthroughputdata AT michaelreth semisupervisedpredictionofsh2peptideinteractionsfromimbalancedhighthroughputdata AT rolfbackofen semisupervisedpredictionofsh2peptideinteractionsfromimbalancedhighthroughputdata
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Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.

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