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A neural strategy for the inference of SH3 domain-peptide interaction specificity

BACKGROUND: The SH3 domain family is one of the most representative and widely studied cases of so-called Peptide Recognition Modules (PRM). The polyproline II motif PxxP that generally characterizes its ligands does not reflect the complex interaction spectrum of the over 1500 different SH3 domains...

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Autores principales: Ferraro, Enrico, Via, Allegra, Ausiello, Gabriele, Helmer-Citterich, Manuela
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866395/
https://www.ncbi.nlm.nih.gov/pubmed/16351739
http://dx.doi.org/10.1186/1471-2105-6-S4-S13
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author Ferraro, Enrico
Via, Allegra
Ausiello, Gabriele
Helmer-Citterich, Manuela
author_facet Ferraro, Enrico
Via, Allegra
Ausiello, Gabriele
Helmer-Citterich, Manuela
author_sort Ferraro, Enrico
collection PubMed
description BACKGROUND: The SH3 domain family is one of the most representative and widely studied cases of so-called Peptide Recognition Modules (PRM). The polyproline II motif PxxP that generally characterizes its ligands does not reflect the complex interaction spectrum of the over 1500 different SH3 domains, and the requirement of a more refined knowledge of their specificity implies the setting up of appropriate experimental and theoretical strategies. Due to the limitations of the current technology for peptide synthesis, several experimental high-throughput approaches have been devised to elucidate protein-protein interaction mechanisms. Such approaches can rely on and take advantage of computational techniques, such as regular expressions or position specific scoring matrices (PSSMs) to pre-process entire proteomes in the search for putative SH3 targets. In this regard, a reliable inference methodology to be used for reducing the sequence space of putative binding peptides represents a valuable support for molecular and cellular biologists. RESULTS: Using as benchmark the peptide sequences obtained from in vitro binding experiments, we set up a neural network model that performs better than PSSM in the detection of SH3 domain interactors. In particular our model is more precise in its predictions, even if its performance can vary among different SH3 domains and is strongly dependent on the number of binding peptides in the benchmark. CONCLUSION: We show that a neural network can be more effective than standard methods in SH3 domain specificity detection. Neural classifiers identify general SH3 domain binders and domain-specific interactors from a PxxP peptide population, provided that there are a sufficient proportion of true positives in the training sets. This capability can also improve peptide selection for library definition in array experiments. Further advances can be achieved, including properly encoded domain sequences and structural information as input for a global neural network.
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spelling pubmed-18663952007-05-11 A neural strategy for the inference of SH3 domain-peptide interaction specificity Ferraro, Enrico Via, Allegra Ausiello, Gabriele Helmer-Citterich, Manuela BMC Bioinformatics Research Article BACKGROUND: The SH3 domain family is one of the most representative and widely studied cases of so-called Peptide Recognition Modules (PRM). The polyproline II motif PxxP that generally characterizes its ligands does not reflect the complex interaction spectrum of the over 1500 different SH3 domains, and the requirement of a more refined knowledge of their specificity implies the setting up of appropriate experimental and theoretical strategies. Due to the limitations of the current technology for peptide synthesis, several experimental high-throughput approaches have been devised to elucidate protein-protein interaction mechanisms. Such approaches can rely on and take advantage of computational techniques, such as regular expressions or position specific scoring matrices (PSSMs) to pre-process entire proteomes in the search for putative SH3 targets. In this regard, a reliable inference methodology to be used for reducing the sequence space of putative binding peptides represents a valuable support for molecular and cellular biologists. RESULTS: Using as benchmark the peptide sequences obtained from in vitro binding experiments, we set up a neural network model that performs better than PSSM in the detection of SH3 domain interactors. In particular our model is more precise in its predictions, even if its performance can vary among different SH3 domains and is strongly dependent on the number of binding peptides in the benchmark. CONCLUSION: We show that a neural network can be more effective than standard methods in SH3 domain specificity detection. Neural classifiers identify general SH3 domain binders and domain-specific interactors from a PxxP peptide population, provided that there are a sufficient proportion of true positives in the training sets. This capability can also improve peptide selection for library definition in array experiments. Further advances can be achieved, including properly encoded domain sequences and structural information as input for a global neural network. BioMed Central 2005-12-01 /pmc/articles/PMC1866395/ /pubmed/16351739 http://dx.doi.org/10.1186/1471-2105-6-S4-S13 Text en Copyright © 2005 Ferraro et al; licensee BioMed Central Ltd http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ferraro, Enrico
Via, Allegra
Ausiello, Gabriele
Helmer-Citterich, Manuela
A neural strategy for the inference of SH3 domain-peptide interaction specificity
title A neural strategy for the inference of SH3 domain-peptide interaction specificity
title_full A neural strategy for the inference of SH3 domain-peptide interaction specificity
title_fullStr A neural strategy for the inference of SH3 domain-peptide interaction specificity
title_full_unstemmed A neural strategy for the inference of SH3 domain-peptide interaction specificity
title_short A neural strategy for the inference of SH3 domain-peptide interaction specificity
title_sort neural strategy for the inference of sh3 domain-peptide interaction specificity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866395/
https://www.ncbi.nlm.nih.gov/pubmed/16351739
http://dx.doi.org/10.1186/1471-2105-6-S4-S13
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