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 <0.5% of known human PPIs. Hence, there is a need to extend the 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- and 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 $\sim 230\hspace{0.17em}000$ RRIs and $\sim 20\hspace{0.17em}400$ 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 $\sim 76\%$ based on the area under the receiver operating characteristic curve. Insights, data, and computational pipelines from our study can guide future developments of computational methods for solving the task of predicting EEIs.

Data and source code are available at https://github.com/lieboldj/EEIpred.