How and when should interactome-derived clusters be used to predict functional modules and protein function?

Song, Jimin; Singh, Mona

doi:10.1093/bioinformatics/btp551

Cited by 112 publications

(128 citation statements)

References 41 publications

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“…Protein products of the connecting genes also form a protein-protein interaction network with a higher connectivity than randomized versions of the same network (clustering coefficient=0.27 versus 0.09, P=0.01), which suggests a strong functional interconnection of the encoded proteins (which has previously been shown for Saccharomyces cerevisiae) 13 (Supplemental Figure 1).…”

Section: Association Of Pathway Network With Ckdsupporting

confidence: 58%

Integrative Biology Identifies Shared Transcriptional Networks in CKD

Martini

Nair

Keller

et al. 2014

Journal of the American Society of Nephrology

120

118

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A previous meta-analysis of genome-wide association data by the Cohorts for Heart and Aging Research in Genomic Epidemiology and CKDGen consortia identified 16 loci associated with eGFR. To define how each of these single-nucleotide polymorphisms (SNPs) could affect renal function, we integrated GFRassociated loci with regulatory pathways, producing a molecular map of CKD. In kidney biopsy specimens from 157 European subjects representing nine different CKDs, renal transcript levels for 18 genes in proximity to the SNPs significantly correlated with GFR. These 18 genes were mapped into their biologic context by testing coregulated transcripts for enriched pathways. A network of 97 pathways linked by shared genes was constructed and characterized. Of these pathways, 56 pathways were reported previously to be associated with CKD; 41 pathways without prior association with CKD were ranked on the basis of the number of candidate genes connected to the respective pathways. All pathways aggregated into a network of two main clusters comprising inflammation-and metabolism-related pathways, with the NRF2-mediated oxidative stress response pathway serving as the hub between the two clusters. In all, 78 pathways and 95% of the connections among those pathways were verified in an independent North American biopsy cohort. Disease-specific analyses showed that most pathways are shared between sets of three diseases, with closest interconnection between lupus nephritis, IgA nephritis, and diabetic nephropathy. Taken together, the network integrates candidate genes from genome-wide association studies into their functional context, revealing interactions and defining established and novel biologic mechanisms of renal impairment in renal diseases.

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Section: Association Of Pathway Network With Ckdsupporting

confidence: 58%

Integrative Biology Identifies Shared Transcriptional Networks in CKD

Martini

Nair

Keller

et al. 2014

Journal of the American Society of Nephrology

120

118

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“…Only recently has this method been applied to the study of protein complexes using label-free AP-QMS strategies (32,33,38) with multiple baits. Various strategies and algorithms have been developed for network topology analysis that have been evaluated and reviewed in detail elsewhere (103)(104)(105). Nevertheless, most of the strategies use co-purification profiles to assign pairwise interaction scores and/or probabilistic measurements to group proteins into three main topological clusters: cores, modules, and attachments (103)(104)(105)(106)(107)(108).…”

Section: Protein-protein Interaction (Ppi) Network Analysis and Functmentioning

confidence: 99%

“…Various strategies and algorithms have been developed for network topology analysis that have been evaluated and reviewed in detail elsewhere (103)(104)(105). Nevertheless, most of the strategies use co-purification profiles to assign pairwise interaction scores and/or probabilistic measurements to group proteins into three main topological clusters: cores, modules, and attachments (103)(104)(105)(106)(107)(108). Core proteins stably associate and stoichiometrically co-purify with every protein complex subunit.…”

Section: Protein-protein Interaction (Ppi) Network Analysis and Functmentioning

confidence: 99%

Profiling of Protein Interaction Networks of Protein Complexes Using Affinity Purification and Quantitative Mass Spectrometry

Kaake

Wang

Huang

2010

Molecular & Cellular Proteomics

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Protein-protein interactions are important for nearly all biological processes, and it is known that aberrant protein-protein interactions can lead to human disease and cancer. Recent evidence has suggested that protein interaction interfaces describe a new class of attractive targets for drug development. Full characterization of protein interaction networks of protein complexes and their dynamics in response to various cellular cues will provide essential information for us to understand how protein complexes work together in cells to maintain cell viability and normal homeostasis. Affinity purification coupled with quantitative mass spectrometry has become the primary method for studying in vivo protein interactions of protein complexes and whole organism proteomes. Recent developments in sample preparation and affinity purification strategies allow the capture, identification, and quantification of protein interactions of protein complexes that are stable, dynamic, transient, and/or weak. Essential biological processes in cells such as DNA replication, transcription, translation, protein degradation, and cell cycle control are carried out by an assembly of protein molecules that interact and form multisubunit protein complexes (1-5). These macromolecular complexes are important cellular machineries that work hand in hand to maintain normal cell homeostasis. Although the function of each individual subunit is important, the function of the complex as a whole is not simply the sum of its individual components (6 -8). Traditional biochemical methods focus on characterizing a single protein.Although still valid and useful, these methods cannot account for the complexity of macromolecular protein machines. Recently, technological developments in mass spectrometrybased proteomics approaches have made comprehensive characterization of protein complexes possible by enabling the determination of dynamic protein complex compositions, stoichiometries, posttranslational modifications, assemblies, structures, and protein interaction networks (8 -17). This review will report the advances in the study of protein-protein interactions by mass spectrometry.Protein-protein interactions are important for nearly all biological processes, and it is known that aberrant protein-protein interactions can lead to human disease and cancer (18 -20). Sufficient evidence has demonstrated that modulation of protein-protein binding represents an emerging therapeutic paradigm, and protein interaction interfaces describe a new class of attractive targets for drug development. For example, inactivation of tumor suppressor p53 has been implicated in various cancers, and one mechanism of inactivation is the inappropriate interaction with the E3 ubiquitin ligase MDM2, leading to excessive degradation of p53 (21). Disruption of this interaction by small molecule inhibitors has shown promise as a potential tool for cancer treatment (21-25). Current work has illustrated how generating protein interaction networks of protein complexes and profiling t...

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“…Even with the proposed null model for multiprotein modules, biological process enrichment may retain limitations as a measure of biological meaning. Studies have shown that biological process enrichment tends to be an optimistic measure, with many multiprotein modules, even those unreasonable by other measures, often deemed significant (5,21). This problem is at least partially addressed by the improved null model presented in this study.…”

Section: Discussionmentioning

confidence: 94%

“…Subtracting a baseline value averaged over randomly generated modules of the desired size corrects for much of this optimism. An alternative measure of biological meaning is semantic density (21), which can also be normalized by module size. It would be interesting to see whether the general trends observed with biological process enrichment hold for semantic density and other measures of biological meaning.…”

Section: Discussionmentioning

confidence: 99%

Optimization criteria and biological process enrichment in homologous multiprotein modules

Hodgkinson

Karp

2013

Proc. Natl. Acad. Sci. U.S.A.

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Biological process enrichment is a widely used metric for evaluating the quality of multiprotein modules. In this study, we examine possible optimization criteria for detecting homologous multiprotein modules and quantify their effects on biological process enrichment. We find that modularity, linear density, and module size are the most important criteria considered, complementary to each other, and that graph theoretical attributes account for 36% of the variance in biological process enrichment. Variations in protein interaction similarity within module pairs have only minor effects on biological process enrichment. As random modules increase in size, both biological process enrichment and modularity tend to improve, although modularity does not show this upward trend in modules with size at most 50 proteins. To adjust for these trends, we recommend a size correction based on random sampling of modules when using biological process enrichment or other attributes to evaluate module boundaries. Characteristics of homologous multiprotein modules optimized for each of the optimization criteria are examined.V estiges of similarity surviving evolution, homology, appear in distantly related taxa at many levels. Algorithms have been designed to detect homology at the level of multiprotein modules (1-5). These algorithms have been evaluated using overlap with literature-based protein complexes (4) and by graph theoretical measures (5); however, the most popular evaluation metric by far is Gene Ontology biological process enrichment.Gene Ontology (6) stores a database of protein annotations that include participation in biological processes. Methods for biological process enrichment attempt to determine whether the module proteins have more similarity in biological process annotations than would be expected by chance. The most widely used method for biological process enrichment calculates the probability of obtaining at least as many proteins with the observed annotations if the module proteins were selected at random from a background set of proteins (7). Several variations on this basic idea have been introduced, for example, to consider the graphical structure of the ontology (8). Biological process enrichment algorithms have been implemented in Ontologizer (9).The consolidated protein interaction databases contain a wealth of knowledge from numerous experiments, experimenters, and experimental assays. All assays have limitations on accuracy and precision, and the experimental protocols often differ between experiments (10). Searching for homologous modules in these data is a daunting challenge. Optimization criteria that may assist this search are of great interest.In this study, we examine potential optimization criteria for detecting homology between Homo sapiens and Drosophila melanogaster at the level of multiprotein modules. We explore whether optimizing these attributes contributes significantly to biological process enrichment of the modules. After generating pairs of modules containing proteins homologous ...

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How and when should interactome-derived clusters be used to predict functional modules and protein function?

Cited by 112 publications

References 41 publications

Integrative Biology Identifies Shared Transcriptional Networks in CKD

Integrative Biology Identifies Shared Transcriptional Networks in CKD

Profiling of Protein Interaction Networks of Protein Complexes Using Affinity Purification and Quantitative Mass Spectrometry

Optimization criteria and biological process enrichment in homologous multiprotein modules

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