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IntPred: a structure-based predictor of protein–protein interaction sites

Northey, Thomas C; Barešić, Anja; Martin, Andrew C R (2017) IntPred: a structure-based predictor of protein–protein interaction sites. Bioinformatics, 34 (2). pp. 223-229. ISSN 1367-4803

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Motivation Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein– protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods. Results On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent. Availability and implementation IntPred is implemented in Perl and may be downloaded for local use or run via a web server at Supplementary information Supplementary data are available at Bioinformatics online.

Item Type: Article
Additional Information: TCN thanks the BBSRC and UCB for funding under an industrial CASE studentship (BB/J013110/1). AB thanks the UCL Overseas Research Scholarship and UCL Graduate Research Scholarship schemes for funding.
Uncontrolled Keywords: protein-protein interactions; protein interface; random forest
Subjects: INTERDISCIPLINARY AREAS OF KNOWLEDGE > Biotechnology in Biomedicine (natural science, biomedicine and healthcare, bioethics area
Depositing User: Anja Barešić
Date Deposited: 28 Jul 2020 10:30
DOI: 10.1093/bioinformatics/btx585

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