Supplementary data to the paper: The power and limits of predicting inter-protein exon-exon interactions using protein 3D structures

Alternative splicing (AS) effects on cellular functions can be captured by studying changes in the underlying protein-protein interactions (PPIs). Because AS results in the gain or loss of exons, existing methods for predicting AS-related PPI changes utilize known inter-protein exon-exon interactions (EEIs), which cover less than 0.5% of known human PPIs. Hence, there is a need to extend the existing limited EEI knowledge to advance the functional understanding of AS. Here, we explore whether existing 3-dimensional (3D) protein structure-based computational PPI interface prediction (PPIIP) methods, originally designed to predict inter-protein residue-residue interactions (RRIs), can be utilized to predict EEIs. We evaluate the PPIIP methods for the RRI- as well as EEI-prediction tasks using all known experimentally determined 3D structures of human protein heterodimers from the Protein Data Bank available at the time of data collection. From these heterodimers we determined ~230,000 RRIs and ~20,400 EEIs as ground truth. We provide the first evidence of the adaptability of existing PPIIP methods to predict EEIs, with a performance score of up to ~50% based on F-score with a false discovery rate of 5%. Insights, data, and computational pipelines from our study have the potential to guide future developments of computational methods for solving the task of predicting EEIs.