Abstract
Protein-protein interactions (PPI) play important roles in almost all cellular processes. PPI also includes protein-peptide interactions (PPepI), which, by an estimate, account for 15–40% of all such interactions. Even though protein-protein and protein-peptide recognition mechanisms sound similar, seemingly subtle differences exist among them. Knowledge of such differences is essential for biologics design when augmentation or disruption of protein-protein interactions is substituted with peptide-based mimics. Peptide-based leads have multiple advantages, including longer shelf life, feasibility of oral delivery, flexibility of optimisation, screening, and versatility of mimetics synthesis. To characterise differences between protein-protein and protein-peptide interactions, we have used machine learning approaches to classify these interactions. We compiled three datasets, comprising protein-protein, protein-peptide, and non-interacting protein complexes, each of which has 212 high-quality crystal structures. We calculated 583 sequence and physicochemical properties based on the on the features of one protein partner in all three datasets. With the correlation-based feature selection attribute evaluator and the best first search method, 56 features were chosen for classification. We performed different supervised machine learning algorithms with a 10-fold cross-validation method for unbiased classification of PPI and PPepI datasets. The Bayesian network method yielded the highest accuracy of 80.53%. Further, to achieve the highest accuracy and identify relevant features that can be employed for peptide-based biologic design applications, we calculated 73 PPI-specific molecular descriptors and applied ML methods. Application of the simple logistic method resulted in the highest accuracy of 92.21% (recall 0.925, precision 0.920, ROC 0.978, and PRC 0.981). The present attempt to discriminate between the between the recognition mechanisms of protein-protein and protein-peptide in cellular processes should aid in the design of peptide-based biologics.