ArticleBioinformatics advances2026
The power and limits of predicting inter-protein exon-exon interactions using protein 3D structures.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- PDBe-SIFTS: an open-source tool for Structure Integration with Function, Taxonomy, and Sequences, featuring improved alignment, scoring scheme, and accelerated search.bioRxiv : the preprint server for biology · 2026Article
- Toward a comprehensive profiling of alternative splicing proteoform structures, interactions and functions.Current opinion in structural biology · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: 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. Results: 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 Availability and implementation: Data and source code are available at https://github.com/lieboldj/EEIpred.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.