An experimentally derived confidence score for binary protein-protein interactions

• Published in: Nature methods Vol. 6; no. 1; pp. 91 - 97
• Main Authors: Braun, Pascal, Tasan, Murat, Dreze, Matija, Barrios-Rodiles, Miriam, Lemmens, Irma, Yu, Haiyuan, Sahalie, Julie M, Murray, Ryan R, Roncari, Luba, de Smet, Anne-Sophie, Venkatesan, Kavitha, Rual, Jean-François, Vandenhaute, Jean, Cusick, Michael E, Pawson, Tony, Hill, David E, Tavernier, Jan, Wrana, Jeffrey L, Roth, Frederick P, Vidal, Marc
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.01.2009; Nature Publishing Group
• Subjects: Analysis; Animals; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedical research; Evaluation; Humans; Life Sciences; Mass spectrometry; Protein Binding; Protein Interaction Mapping - methods; Protein microarrays; Protein-protein interactions; Proteins - analysis; Proteins - metabolism; Proteomics; Quality control; Research methodology; Sensitivity and Specificity; United States
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/nmeth.1281

Use of the protein-protein interaction reference sets reported in this issue in Venkatesan et al . to benchmark four complementary protein-protein interaction assays, followed by the training of a logistic regression model, allows the assignment of standardized confidence scores to individual protein-protein interactions. Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.