Shoemaker, B. A. & Panchenko, A. R. Deciphering protein–protein interactions. Part I. Experimental techniques and databases. PLOS Comput. Biol.3, e42 (2007) ADSPubMedPubMed Central Google Scholar
Reguly, T. et al. Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae. J. Biol.5, 11 (2006) PubMedPubMed Central Google Scholar
Shoemaker, B. A. & Panchenko, A. R. Deciphering protein–protein interactions. Part II. Computational methods to predict protein and domain interaction partners. PLOS Comput. Biol.3, e43 (2007) ADSPubMedPubMed Central Google Scholar
Salwinski, L. & Eisenberg, D. Computational methods of analysis of protein–protein interactions. Curr. Opin. Struct. Biol.13, 377–382 (2003) CASPubMed Google Scholar
von Mering, C. et al. Comparative assessment of large-scale data sets of protein–protein interactions. Nature417, 399–403 (2002) ADSCASPubMed Google Scholar
Braun, P. et al. An experimentally derived confidence score for binary protein–protein interactions. Nature Methods6, 91–97 (2009) CASPubMed Google Scholar
Deane, C. M., Salwinski, L., Xenarios, I. & Eisenberg, D. Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol. Cell. Proteomics1, 349–356 (2002) CASPubMed Google Scholar
Pieper, U. et al. MODBASE: a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res.34, D291–D295 (2006) ADSCASPubMed Google Scholar
Mirkovic, N., Li, Z., Parnassa, A. & Murray, D. Strategies for high-throughput comparative modeling: applications to leverage analysis in structural genomics and protein family organization. Proteins66, 766–777 (2007) CASPubMed Google Scholar
Henrick, K. & Thornton, J. M. PQS: a protein quaternary structure file server. Trends Biochem. Sci.23, 358–361 (1998) CASPubMed Google Scholar
Aloy, P. & Russell, R. B. Interrogating protein interaction networks through structural biology. Proc. Natl Acad. Sci. USA99, 5896–5901 (2002) ADSCASPubMed Google Scholar
Lu, L., Lu, H. & Skolnick, J. MULTIPROSPECTOR: an algorithm for the prediction of protein–protein interactions by multimeric threading. Proteins49, 350–364 (2002) CASPubMed Google Scholar
Davis, F. P. et al. Protein complex compositions predicted by structural similarity. Nucleic Acids Res.34, 2943–2952 (2006) CASPubMedPubMed Central Google Scholar
Tuncbag, N., Gursoy, A., Guney, E., Nussinov, R. & Keskin, O. Architectures and functional coverage of protein–protein interfaces. J. Mol. Biol.381, 785–802 (2008) CASPubMedPubMed Central Google Scholar
Zhang, Q. C., Petrey, D., Norel, R. & Honig, B. H. Protein interface conservation across structure space. Proc. Natl Acad. Sci. USA107, 10896–10901 (2010) ADSCASPubMed Google Scholar
Gao, M. & Skolnick, J. Structural space of protein–protein interfaces is degenerate, close to complete, and highly connected. Proc. Natl Acad. Sci. USA107, 22517–22522 (2010) ADSCASPubMed Google Scholar
Wass, M. N., Fuentes, G., Pons, C., Pazos, F. & Valencia, A. Towards the prediction of protein interaction partners using physical docking. Mol. Syst. Biol.7, 469 (2011) PubMedPubMed Central Google Scholar
Chen, H. L. & Zhou, H. X. Prediction of interface residues in protein–protein complexes by a consensus neural network method: test against NMR data. Proteins61, 21–35 (2005) CASPubMed Google Scholar
Liang, S., Zhang, C., Liu, S. & Zhou, Y. Protein binding site prediction using an empirical scoring function. Nucleic Acids Res.34, 3698–3707 (2006) CASPubMedPubMed Central Google Scholar
Zhang, Q. C. et al. PredUs: a web server for predicting protein interfaces using structural neighbors. Nucleic Acids Res.39, 283–287 (2011) Google Scholar
Lefebvre, C. et al. A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol. Syst. Biol.6, 377 (2010) PubMedPubMed Central Google Scholar
Jansen, R. et al. A Bayesian networks approach for predicting protein–protein interactions from genomic data. Science302, 449–453 (2003) ADSCASPubMed Google Scholar
von Mering, C. et al. STRING: known and predicted protein–protein associations, integrated and transferred across organisms. Nucleic Acids Res.33, D433–D437 (2005) CASPubMed Google Scholar
Stolovitzky, G., Prill, R. J. & Califano, A. Lessons from the DREAM2 challenges. Ann. NY Acad. Sci.1158, 159–195 (2009) ADSCASPubMed Google Scholar
Keskin, O., Nussinov, R. & Gursoy, A. PRISM: protein–protein interaction prediction by structural matching. Methods Mol. Biol.484, 505–521 (2008) CASPubMedPubMed Central Google Scholar
Ewing, R. M. et al. Large-scale mapping of human protein–protein interactions by mass spectrometry. Mol. Syst. Biol.3, 89 (2007) PubMedPubMed Central Google Scholar
Levitt, M. Nature of the protein universe. Proc. Natl Acad. Sci. USA106, 11079–11084 (2009) ADSCASPubMed Google Scholar
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res.25, 3389–3402 (1997) CASPubMedPubMed Central Google Scholar
Sanchez, R. & Sali, A. Large-scale protein structure modeling of the Saccharomyces cerevisiae genome. Proc. Natl Acad. Sci. USA95, 13597–13602 (1998) ADSCASPubMed Google Scholar
Petrey, D. & Honig, B. GRASP2: visualization, surface properties, and electrostatics of macromolecular structures and sequences. Methods Enzymol.374, 492–509 (2003) CASPubMed Google Scholar
Yang, A. S. & Honig, B. An integrated approach to the analysis and modeling of protein sequences and structures. I. Protein structural alignment and a quantitative measure for protein structural distance. J. Mol. Biol.301, 665–678 (2000) CASPubMed Google Scholar
Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol.372, 774–797 (2007) CASPubMed Google Scholar
The Gene Ontology Consortium Gene ontology: tool for the unification of biology. Nature Genet.25, 25–29 (2000) Google Scholar
Mewes, H. W., Albermann, K., Heumann, K., Liebl, S. & Pfeiffer, F. MIPS: a database for protein sequences, homology data and yeast genome information. Nucleic Acids Res.25, 28–30 (1997) CASPubMedPubMed Central Google Scholar
Huynen, M., Snel, B., Lathe, W., III & Bork, P. Predicting protein function by genomic context: quantitative evaluation and qualitative inferences. Genome Res.10, 1204–1210 (2000) CASPubMedPubMed Central Google Scholar
Sun, L. et al. Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell9, 287–300 (2006) CASPubMed Google Scholar
Barrett, T. et al. NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Res.39, D1005–D1010 (2011) CASPubMed Google Scholar
Enault, F., Suhre, K. & Claverie, J. M. Phydbac “Gene Function Predictor”: a gene annotation tool based on genomic context analysis. BMC Bioinformatics6, 247 (2005) PubMedPubMed Central Google Scholar