A novel scoring approach for protein co-purification data reveals high interaction specificity.

Large-scale protein interaction networks (PINs) have typically been discerned using affinity purification followed by mass spectrometry (AP/MS) and yeast two-hybrid (Y2H) techniques. It is generally recognized that Y2H screens detect direct binary interactions while the AP/MS method captures co-comp...

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Main Authors: Xueping Yu, Joseph Ivanic, Anders Wallqvist, Jaques Reifman
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2009-09-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC2738424?pdf=render
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spelling doaj-f946c96f3f2d4a0a8bf5adbe8ae78ec12020-11-25T01:11:55ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582009-09-0159e100051510.1371/journal.pcbi.1000515A novel scoring approach for protein co-purification data reveals high interaction specificity.Xueping YuJoseph IvanicAnders WallqvistJaques ReifmanLarge-scale protein interaction networks (PINs) have typically been discerned using affinity purification followed by mass spectrometry (AP/MS) and yeast two-hybrid (Y2H) techniques. It is generally recognized that Y2H screens detect direct binary interactions while the AP/MS method captures co-complex associations; however, the latter technique is known to yield prevalent false positives arising from a number of effects, including abundance. We describe a novel approach to compute the propensity for two proteins to co-purify in an AP/MS data set, thereby allowing us to assess the detected level of interaction specificity by analyzing the corresponding distribution of interaction scores. We find that two recent AP/MS data sets of yeast contain enrichments of specific, or high-scoring, associations as compared to commensurate random profiles, and that curated, direct physical interactions in two prominent data bases have consistently high scores. Our scored interaction data sets are generally more comprehensive than those of previous studies when compared against four diverse, high-quality reference sets. Furthermore, we find that our scored data sets are more enriched with curated, direct physical associations than Y2H sets. A high-confidence protein interaction network (PIN) derived from the AP/MS data is revealed to be highly modular, and we show that this topology is not the result of misrepresenting indirect associations as direct interactions. In fact, we propose that the modularity in Y2H data sets may be underrepresented, as they contain indirect associations that are significantly enriched with false negatives. The AP/MS PIN is also found to contain significant assortative mixing; however, in line with a previous study we confirm that Y2H interaction data show weak disassortativeness, thus revealing more clearly the distinctive natures of the interaction detection methods. We expect that our scored yeast data sets are ideal for further biological discovery and that our scoring system will prove useful for other AP/MS data sets.http://europepmc.org/articles/PMC2738424?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Xueping Yu
Joseph Ivanic
Anders Wallqvist
Jaques Reifman
spellingShingle Xueping Yu
Joseph Ivanic
Anders Wallqvist
Jaques Reifman
A novel scoring approach for protein co-purification data reveals high interaction specificity.
PLoS Computational Biology
author_facet Xueping Yu
Joseph Ivanic
Anders Wallqvist
Jaques Reifman
author_sort Xueping Yu
title A novel scoring approach for protein co-purification data reveals high interaction specificity.
title_short A novel scoring approach for protein co-purification data reveals high interaction specificity.
title_full A novel scoring approach for protein co-purification data reveals high interaction specificity.
title_fullStr A novel scoring approach for protein co-purification data reveals high interaction specificity.
title_full_unstemmed A novel scoring approach for protein co-purification data reveals high interaction specificity.
title_sort novel scoring approach for protein co-purification data reveals high interaction specificity.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2009-09-01
description Large-scale protein interaction networks (PINs) have typically been discerned using affinity purification followed by mass spectrometry (AP/MS) and yeast two-hybrid (Y2H) techniques. It is generally recognized that Y2H screens detect direct binary interactions while the AP/MS method captures co-complex associations; however, the latter technique is known to yield prevalent false positives arising from a number of effects, including abundance. We describe a novel approach to compute the propensity for two proteins to co-purify in an AP/MS data set, thereby allowing us to assess the detected level of interaction specificity by analyzing the corresponding distribution of interaction scores. We find that two recent AP/MS data sets of yeast contain enrichments of specific, or high-scoring, associations as compared to commensurate random profiles, and that curated, direct physical interactions in two prominent data bases have consistently high scores. Our scored interaction data sets are generally more comprehensive than those of previous studies when compared against four diverse, high-quality reference sets. Furthermore, we find that our scored data sets are more enriched with curated, direct physical associations than Y2H sets. A high-confidence protein interaction network (PIN) derived from the AP/MS data is revealed to be highly modular, and we show that this topology is not the result of misrepresenting indirect associations as direct interactions. In fact, we propose that the modularity in Y2H data sets may be underrepresented, as they contain indirect associations that are significantly enriched with false negatives. The AP/MS PIN is also found to contain significant assortative mixing; however, in line with a previous study we confirm that Y2H interaction data show weak disassortativeness, thus revealing more clearly the distinctive natures of the interaction detection methods. We expect that our scored yeast data sets are ideal for further biological discovery and that our scoring system will prove useful for other AP/MS data sets.
url http://europepmc.org/articles/PMC2738424?pdf=render
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