Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions

Protein-protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein-protein interactions. Cross-docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross-docking simulations of 358 proteins with two different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity-sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, i.e. partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.\n\nAbbreviationsANOVA: ANalysis Of Variance; AUC: Area Under the Curve; Best Interface: BI; CAPRI: Critical Assessment of Prediction of Interactions; CC-D: Complete Cross-Docking; DNA: DesoxyriboNucleic Acid; FDR: False Discovery Rate; FRIres(type): Fraction of each Residue type in the Interface; FP: False Positives; GI: Global Interface; HCMD: Help Cure Muscular Dystrophy; JET: Joint Evolutionary Tree; MAXDo: Molecular Association via Cross Docking; NAI: Nucleic Acid Interface; NPV: Negative Predicted Value; PDB: Protein Data Bank; PIP: Protein Interface Propensity; PiQSi: Protein Quaternary Structure investigation; PPIs: Protein-Protein Interactions; PPV: Positive Predicted Value; Prec.: Precision; PrimI: Primary Interface; RNA: RiboNucleic Acid; ROC: Receiver Operating Characteristic; SecI: Secondary Interface; Sen.: Sensitivity; Spe.: Specificity; TN: True Negatives; TP: True Positives; WCG: World Community Grid.