An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening

The
prediction of peptide–protein binding sites is of utmost
importance to tackle the onset of severe neurodegenerative diseases
and cancer. In this work, we detail a novel machine learning model
based on Linear Discriminant Analysis (LDA) demonstrating to be highly
predictive in detecting the putative protein binding regions of small
peptides. Starting from 439 high-quality pockets derived from peptide–protein
crystallographic complexes, three sets of well-established peptide-binding
regions were first selected through a Partitioning Around Medoids
(PAM) clustering algorithm based on morphological and energetic 3D
GRID-MIF molecular descriptors. Next, the best combination between
all the putative interacting peptide pockets and related GRID-MIF
scores was automatically explored by using the LDA-based protocol
implemented in BioGPS. This approach proved successful to recognize
the actual interacting peptide regions (that is, AUC = 0.86 and partial
ROC enrichment at 5% of 0.48) from all the other pockets of the protein.
Validated on two external collections sets, including 445 and 347
crystallographic peptide–protein complexes, our LDA-based model
could be effective to further run peptide–protein virtual screening
campaigns.