ArticleProceedings of the National Academy of Sciences of the United States of America2006
Characterization and prediction of protein-protein interactions within and between complexes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Protein-Protein Interactions in Translesion Synthesis.Molecules (Basel, Switzerland) · 2021Review
- ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer.Nucleic acids research · 2017Article
- Prediction of Protein-Protein Interactions by Evidence Combining Methods.International journal of molecular sciences · 2016Review
- Great interactions: How binding incorrect partners can teach us about protein recognition and function.Proteins · 2016Article
- Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell.Chemical reviews · 2016Review
- Motif mediated protein-protein interactions as drug targets.Cell communication and signaling : CCS · 2016Review
- A highly efficient approach to protein interactome mapping based on collaborative filtering framework.Scientific reports · 2015Article
- Algorithmic approaches to protein-protein interaction site prediction.Algorithms for molecular biology : AMB · 2015Article
- Article
- Integration strategy is a key step in network-based analysis and dramatically affects network topological properties and inferring outcomes.BioMed research international · 2014Article
- Interaction of proteins associated with the magnetosome assembly in magnetotactic bacteria as revealed by two-hybrid two-photon excitation fluorescence lifetime imaging microscopy Förster resonance energy transfer.The journal of physical chemistry. B · 2013Article
- Predicting physical interactions between protein complexes.Molecular & cellular proteomics : MCP · 2013Article
- Features of protein-protein interactions that translate into potent inhibitors: topology, surface area and affinity.Expert reviews in molecular medicine · 2012Review
- Shared molecular strategies of the malaria parasite P. falciparum and the human virus HIV-1.Molecular & cellular proteomics : MCP · 2011Article
- The zinc-ribbon domain of Helicobacter pylori HP0958: requirement for RpoN accumulation and possible roles of homologs in other bacteria.Microbial informatics and experimentation · 2011Article
- Global organization of protein complexome in the yeast Saccharomyces cerevisiae.BMC systems biology · 2011Article
- Viral organization of human proteins.PloS one · 2010Article
- Inferring modules from human protein interactome classes.BMC systems biology · 2010Article
- Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions.BMC bioinformatics · 2010Article
- Getting a grip on complexes.Current genomics · 2009Article
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3 authors.
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Abstract
Databases of experimentally determined protein interactions provide information on binary interactions and on involvement in multiprotein complexes. These data are valuable for understanding the general properties of the interaction between proteins as well as for the development of prediction schemes for unknown interactions. Here we analyze experimentally determined protein interactions by measuring various sequence, genomic, transcriptomic, and proteomic attributes of each interacting pair in the yeast Saccharomyces cerevisiae. We find that dividing the data into two groups, one that includes binary interactions within protein complexes (stable) and another that includes binary interactions that are not within complexes (transient), enables better characterization of the interactions by the different attributes and improves the prediction of new interactions. This analysis revealed that most attributes were more indicative in the set of intracomplex interactions. Using this data set for training, we integrated the different attributes by logistic regression and developed a predictive scheme that distinguishes between interacting and noninteracting protein pairs. Analysis of the logistic-regression model showed that one of the strongest contributors to the discrimination between interacting and noninteracting pairs is the presence of distinct pairs of domain signatures that were suggested previously to characterize interacting proteins. The predictive algorithm succeeds in identifying both intracomplex and other interactions (possibly the more stable ones), and its correct identification rate is 2-fold higher than that of large-scale yeast two-hybrid experiments.
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