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Functional maps of protein complexes from quantitative genetic interaction data.

Bandyopadhyay S, Kelley R, Krogan NJ, Ideker T - PLoS Comput. Biol. (2008)

Bottom Line: Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits.Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism.These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.

View Article: PubMed Central - PubMed

Affiliation: Program in Bioinformatics, University of California San Diego, La Jolla, California, United States of America.

ABSTRACT
Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.

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Combining physical and genetic interactions to define protein complexes.Correspondence of the physical interaction score (A) and the genetic interaction score (B) with the known small-scale, manually annotated protein complexes in MIPS. To compute the enrichment over random (y-axis), one first computes the fraction f of interactions at each score x that fall inside a MIPS small-scale complex (bin size of 1.5). The enrichment is the ratio f/r, where r is the fraction of random protein pairs within MIPS complexes. (C) Proteins are grouped into physically interacting sets called modules (gray ovals; m1–m6). Pairs of modules may be linked to indicate a functional relationship (dotted lines; b1–b6). The assignment of proteins to modules along with the list of inter-module links comprises the state of the system.
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pcbi-1000065-g001: Combining physical and genetic interactions to define protein complexes.Correspondence of the physical interaction score (A) and the genetic interaction score (B) with the known small-scale, manually annotated protein complexes in MIPS. To compute the enrichment over random (y-axis), one first computes the fraction f of interactions at each score x that fall inside a MIPS small-scale complex (bin size of 1.5). The enrichment is the ratio f/r, where r is the fraction of random protein pairs within MIPS complexes. (C) Proteins are grouped into physically interacting sets called modules (gray ovals; m1–m6). Pairs of modules may be linked to indicate a functional relationship (dotted lines; b1–b6). The assignment of proteins to modules along with the list of inter-module links comprises the state of the system.

Mentions: Figure 1A confirms that protein pairs with higher PE-scores are more likely to operate in a known small-scale protein complex recorded in the MIPS database [19] (versus protein pairs chosen at random). This result is expected considering that PE-scores were trained based on these complexes [18]. Figure 1B shows that protein pairs with both positive and negative S-scores are more likely to operate within a known complex. Positive (alleviating) interactions are well-known to occur between subunits of a complex [6]. Negative (aggravating) interactions are to a lesser degree so, although the mechanism has not been as clear as for the alleviating case [20]. By comparing the magnitudes of enrichment between Figures 1A and 1B, it is apparent that extreme S-scores are at least as indicative of co-complex membership as strong PE-scores, if not more so (∼100-fold enrichment versus ∼50-fold enrichment, respectively). Together, these exploratory findings suggest that the physical and genetic information can indeed provide a basis for the identification of protein pairs involved in the same complex.


Functional maps of protein complexes from quantitative genetic interaction data.

Bandyopadhyay S, Kelley R, Krogan NJ, Ideker T - PLoS Comput. Biol. (2008)

Combining physical and genetic interactions to define protein complexes.Correspondence of the physical interaction score (A) and the genetic interaction score (B) with the known small-scale, manually annotated protein complexes in MIPS. To compute the enrichment over random (y-axis), one first computes the fraction f of interactions at each score x that fall inside a MIPS small-scale complex (bin size of 1.5). The enrichment is the ratio f/r, where r is the fraction of random protein pairs within MIPS complexes. (C) Proteins are grouped into physically interacting sets called modules (gray ovals; m1–m6). Pairs of modules may be linked to indicate a functional relationship (dotted lines; b1–b6). The assignment of proteins to modules along with the list of inter-module links comprises the state of the system.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2289880&req=5

pcbi-1000065-g001: Combining physical and genetic interactions to define protein complexes.Correspondence of the physical interaction score (A) and the genetic interaction score (B) with the known small-scale, manually annotated protein complexes in MIPS. To compute the enrichment over random (y-axis), one first computes the fraction f of interactions at each score x that fall inside a MIPS small-scale complex (bin size of 1.5). The enrichment is the ratio f/r, where r is the fraction of random protein pairs within MIPS complexes. (C) Proteins are grouped into physically interacting sets called modules (gray ovals; m1–m6). Pairs of modules may be linked to indicate a functional relationship (dotted lines; b1–b6). The assignment of proteins to modules along with the list of inter-module links comprises the state of the system.
Mentions: Figure 1A confirms that protein pairs with higher PE-scores are more likely to operate in a known small-scale protein complex recorded in the MIPS database [19] (versus protein pairs chosen at random). This result is expected considering that PE-scores were trained based on these complexes [18]. Figure 1B shows that protein pairs with both positive and negative S-scores are more likely to operate within a known complex. Positive (alleviating) interactions are well-known to occur between subunits of a complex [6]. Negative (aggravating) interactions are to a lesser degree so, although the mechanism has not been as clear as for the alleviating case [20]. By comparing the magnitudes of enrichment between Figures 1A and 1B, it is apparent that extreme S-scores are at least as indicative of co-complex membership as strong PE-scores, if not more so (∼100-fold enrichment versus ∼50-fold enrichment, respectively). Together, these exploratory findings suggest that the physical and genetic information can indeed provide a basis for the identification of protein pairs involved in the same complex.

Bottom Line: Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits.Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism.These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.

View Article: PubMed Central - PubMed

Affiliation: Program in Bioinformatics, University of California San Diego, La Jolla, California, United States of America.

ABSTRACT
Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.

Show MeSH
Related in: MedlinePlus