PBRpredict-Suite: a suite of models to predict peptide-recognition domain residues from protein sequence.

Motivation: Machine learning plays a substantial role in bioscience owing to the explosive growth in sequence data and the challenging application of computational methods. Peptide-recognition domains (PRDs) are critical as they promote coupled-binding with short peptide-motifs of functional importance through transient interactions. It is challenging to build a reliable predictor of peptide-binding residue in proteins with diverse types of PRDs from protein sequence alone. On the other hand, it is vital to cope up with the sequencing speed and to broaden the scope of study.

Results: In this paper, we propose a machine-learning-based tool, named PBRpredict, to predict residues in peptide-binding domains from protein sequence alone. To develop a generic predictor, we train the models on peptide-binding residues of diverse types of domains. As inputs to the models, we use a high-dimensional feature set of chemical, structural and evolutionary information extracted from protein sequence. We carefully investigate six different state-of-the-art classification algorithms for this application. Finally, we use the stacked generalization approach to non-linearly combine a set of complementary base-level learners using a meta-level learner which outperformed the winner-takes-all approach. The proposed predictor is found competitive based on statistical evaluation.

Availability and implementation: PBRpredict-Suite software: https://cs.uno.edu/~tamjid/Software/PBRpredict/pbrpredict-suite.zip.

Supplementary information: Supplementary data are available at Bioinformatics online.