Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.

Computational prediction of residues that participate in protein-protein interactions is a difficult task, and state of the art methods have shown only limited success in this arena. One possible problem with these methods is that they try to predict interacting residues without incorporating inform...

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Autores principales:	Shandar Ahmad, Kenji Mizuguchi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/50c7f09e77ce4581ad20f6acaaba44b2
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spelling	oai:doaj.org-article:50c7f09e77ce4581ad20f6acaaba44b22021-11-18T07:32:06ZPartner-aware prediction of interacting residues in protein-protein complexes from sequence data.1932-620310.1371/journal.pone.0029104https://doaj.org/article/50c7f09e77ce4581ad20f6acaaba44b22011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22194998/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Computational prediction of residues that participate in protein-protein interactions is a difficult task, and state of the art methods have shown only limited success in this arena. One possible problem with these methods is that they try to predict interacting residues without incorporating information about the partner protein, although it is unclear how much partner information could enhance prediction performance. To address this issue, the two following comparisons are of crucial significance: (a) comparison between the predictability of inter-protein residue pairs, i.e., predicting exactly which residue pairs interact with each other given two protein sequences; this can be achieved by either combining conventional single-protein predictions or making predictions using a new model trained directly on the residue pairs, and the performance of these two approaches may be compared: (b) comparison between the predictability of the interacting residues in a single protein (irrespective of the partner residue or protein) from conventional methods and predictions converted from the pair-wise trained model. Using these two streams of training and validation procedures and employing similar two-stage neural networks, we showed that the models trained on pair-wise contacts outperformed the partner-unaware models in predicting both interacting pairs and interacting single-protein residues. Prediction performance decreased with the size of the conformational change upon complex formation; this trend is similar to docking, even though no structural information was used in our prediction. An example application that predicts two partner-specific interfaces of a protein was shown to be effective, highlighting the potential of the proposed approach. Finally, a preliminary attempt was made to score docking decoy poses using prediction of interacting residue pairs; this analysis produced an encouraging result.Shandar AhmadKenji MizuguchiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 12, p e29104 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shandar Ahmad Kenji Mizuguchi Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.
description	Computational prediction of residues that participate in protein-protein interactions is a difficult task, and state of the art methods have shown only limited success in this arena. One possible problem with these methods is that they try to predict interacting residues without incorporating information about the partner protein, although it is unclear how much partner information could enhance prediction performance. To address this issue, the two following comparisons are of crucial significance: (a) comparison between the predictability of inter-protein residue pairs, i.e., predicting exactly which residue pairs interact with each other given two protein sequences; this can be achieved by either combining conventional single-protein predictions or making predictions using a new model trained directly on the residue pairs, and the performance of these two approaches may be compared: (b) comparison between the predictability of the interacting residues in a single protein (irrespective of the partner residue or protein) from conventional methods and predictions converted from the pair-wise trained model. Using these two streams of training and validation procedures and employing similar two-stage neural networks, we showed that the models trained on pair-wise contacts outperformed the partner-unaware models in predicting both interacting pairs and interacting single-protein residues. Prediction performance decreased with the size of the conformational change upon complex formation; this trend is similar to docking, even though no structural information was used in our prediction. An example application that predicts two partner-specific interfaces of a protein was shown to be effective, highlighting the potential of the proposed approach. Finally, a preliminary attempt was made to score docking decoy poses using prediction of interacting residue pairs; this analysis produced an encouraging result.
format	article
author	Shandar Ahmad Kenji Mizuguchi
author_facet	Shandar Ahmad Kenji Mizuguchi
author_sort	Shandar Ahmad
title	Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.
title_short	Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.
title_full	Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.
title_fullStr	Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.
title_full_unstemmed	Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.
title_sort	partner-aware prediction of interacting residues in protein-protein complexes from sequence data.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/50c7f09e77ce4581ad20f6acaaba44b2
work_keys_str_mv	AT shandarahmad partnerawarepredictionofinteractingresiduesinproteinproteincomplexesfromsequencedata AT kenjimizuguchi partnerawarepredictionofinteractingresiduesinproteinproteincomplexesfromsequencedata
_version_	1718423283365314560

Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.

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