  The worldwide surge of multiresistant microbial strains has propelled the
search for alternative treatment options. The study of Protein-Protein
Interactions (PPIs) has been a cornerstone in the clarification of complex
physiological and pathogenic processes, thus being a priority for the
identification of vital components and mechanisms in pathogens. Despite the
advances of laboratorial techniques, computational models allow the screening
of protein interactions between entire proteomes in a fast and inexpensive
manner. Here, we present a supervised machine learning model for the prediction
of PPIs based on the protein sequence. We cluster amino acids regarding their
physicochemical properties, and use the discrete cosine transform to represent
protein sequences. A mesh of classifiers was constructed to create
hyper-specialised classifiers dedicated to the most relevant pairs of molecular
function annotations from Gene Ontology. Based on an exhaustive evaluation that
includes datasets with different configurations, cross-validation and
out-of-sampling validation, the obtained results outscore the state-of-the-art
for sequence-based methods. For the final mesh model using SVM with RBF, a
consistent average AUC of 0.84 was attained.
