ISPRED4: interaction sites PREDiction in protein structures with a refining grammar model

Castrense Savojardo; Piero Fariselli; Pier Luigi Martelli; Rita Casadio

doi:10.1093/bioinformatics/btx044

ISPRED4: interaction sites PREDiction in protein structures with a refining grammar model

Bioinformatics. 2017 Jun 1;33(11):1656-1663. doi: 10.1093/bioinformatics/btx044.

Authors

Castrense Savojardo^{1

2}, Piero Fariselli³, Pier Luigi Martelli^{1

2}, Rita Casadio^{1

2}

Affiliations

¹ Biocomputing Group, Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy.
² CIG, Interdepartmental Center «Luigi Galvani» for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Bologna, Italy.
³ Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Padova, Italy.

PMID: 28130235
DOI: 10.1093/bioinformatics/btx044

Abstract

Motivation: The identification of protein-protein interaction (PPI) sites is an important step towards the characterization of protein functional integration in the cell complexity. Experimental methods are costly and time-consuming and computational tools for predicting PPI sites can fill the gaps of PPI present knowledge.

Results: We present ISPRED4, an improved structure-based predictor of PPI sites on unbound monomer surfaces. ISPRED4 relies on machine-learning methods and it incorporates features extracted from protein sequence and structure. Cross-validation experiments are carried out on a new dataset that includes 151 high-resolution protein complexes and indicate that ISPRED4 achieves a per-residue Matthew Correlation Coefficient of 0.48 and an overall accuracy of 0.85. Benchmarking results show that ISPRED4 is one of the top-performing PPI site predictors developed so far.

Contact: gigi@biocomp.unibo.it.

Availability and implementation: ISPRED4 and datasets used in this study are available at http://ispred4.biocomp.unibo.it .

MeSH terms

Computational Biology / methods*
Machine Learning*
Protein Conformation*
Protein Interaction Domains and Motifs*
Sequence Analysis, Protein / methods
Software*