Journal of Structural BiologyBioinformaticsPeer-reviewed journal article
Building alternative splicing and evolution-aware sequence-structure maps for protein repeats
Antoine SzatkownikiD, Diego Javier ZeaiD, Hugues RichardiD, Élodie LaineiD
Abstract
Alternative splicing of repeats in proteins provides a mechanism for rewiring and fine-tuning protein interaction networks. In this work, we developed a robust and versatile method, ASPRING, to identify alternatively spliced protein repeats from gene annotations. ASPRING leverages evolutionary meaningful alternative splicing-aware hierarchical graphs to provide maps between protein repeats sequences and 3D structures. We re-think the definition of repeats by explicitly accounting for transcript diversity across several genes/species. Using a stringent sequence-based similarity criterion, we detected over 5,000 evolutionary conserved repeats by screening virtually all human protein-coding genes and their orthologs across a dozen species. Through a joint analysis of their sequences and structures, we extracted specificity-determining sequence signatures and assessed their implication in experimentally resolved and modelled protein interactions. Our findings demonstrate the widespread alternative usage of protein repeats in modulating protein interactions and open avenues for targeting repeat-mediated interactions. HighlightsO_LIRobust detection of alternatively used repeated protein regions in evolution C_LIO_LIThe approach relies on sequence similarity and identifies conserved signatures C_LIO_LIMapping of the repeats onto protein isoform 3D models predicted by AlphaFold C_LIO_LI5 000 repeats detected over the human coding fraction, about one third disordered C_LIO_LIAssessment of the structural coverage of their interactions with protein partners C_LI
- organism: human
- organism: multiple species
- assay: protein sequence
- assay: protein structure
- research focus: gene regulatory network inference
- research focus: protein structure prediction
- research focus: database / resource
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 1
- FWCI
- 0.09
- Field percentile
- 31th percentile
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