Improved Prediction Method for Protein Interactions Using Both Structural and Functional Characteristics of Proteins

Abstract

To identify protein-protein interaction pairs with high accuracy, we propose a method for predicting these interactions based on characteristics obtained from protein-protein docking evaluations. Previous studies assumed that the required protein affinity strength for an interaction was not dependent on protein functions. However, the protein affinity strength appears to differ with different docking schemes, such as rigid-body or flexible docking, and these schemes may be related to protein functions. Thus, we propose a new scoring system that is based on statistical analysis of affinity score distributions sampled by their protein functions. As a result, of all possible protein pair combinations, a newly developed method improved prediction accuracy of F-measures. In particular, for bound antibody-antigen pairs, we obtained 50.0% recall (=sensitivity) with higher F-measures compared with previous studies. In addition, by combining two proposed scoring systems, Receptor-Focused Z-scoring and Ligand-Focused Z-scoring, further improvement was achieved. This result suggested that the proposed prediction method improved the prediction accuracy (i.e., F-measure), with few false positives, by taking biological functions of protein pairs into consideration.