Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains woul...

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Autores principales:	Waqasuddin Khan, Fergal Duffy, Gianluca Pollastri, Denis C Shields, Catherine Mooney
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/89fbd469641243698a1d0d5ce0d0d31c
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spelling	oai:doaj.org-article:89fbd469641243698a1d0d5ce0d0d31c2021-11-18T08:57:15ZPredicting binding within disordered protein regions to structurally characterised peptide-binding domains.1932-620310.1371/journal.pone.0072838https://doaj.org/article/89fbd469641243698a1d0d5ce0d0d31c2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24019881/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif) containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58).Next, we trained a bidirectional recurrent neural network (BRNN) using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72) showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods) clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors.Waqasuddin KhanFergal DuffyGianluca PollastriDenis C ShieldsCatherine MooneyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e72838 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Waqasuddin Khan Fergal Duffy Gianluca Pollastri Denis C Shields Catherine Mooney Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.
description	Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif) containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58).Next, we trained a bidirectional recurrent neural network (BRNN) using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72) showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods) clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors.
format	article
author	Waqasuddin Khan Fergal Duffy Gianluca Pollastri Denis C Shields Catherine Mooney
author_facet	Waqasuddin Khan Fergal Duffy Gianluca Pollastri Denis C Shields Catherine Mooney
author_sort	Waqasuddin Khan
title	Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.
title_short	Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.
title_full	Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.
title_fullStr	Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.
title_full_unstemmed	Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.
title_sort	predicting binding within disordered protein regions to structurally characterised peptide-binding domains.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/89fbd469641243698a1d0d5ce0d0d31c
work_keys_str_mv	AT waqasuddinkhan predictingbindingwithindisorderedproteinregionstostructurallycharacterisedpeptidebindingdomains AT fergalduffy predictingbindingwithindisorderedproteinregionstostructurallycharacterisedpeptidebindingdomains AT gianlucapollastri predictingbindingwithindisorderedproteinregionstostructurallycharacterisedpeptidebindingdomains AT deniscshields predictingbindingwithindisorderedproteinregionstostructurallycharacterisedpeptidebindingdomains AT catherinemooney predictingbindingwithindisorderedproteinregionstostructurallycharacterisedpeptidebindingdomains
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Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

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