Journal of Biological ChemistryBiophysicsPeer-reviewed journal article
Missense mutations in intrinsically disordered protein regions link pathogenicity and phase separation
Oliver L KippiD, Karen A. LewisiD, Loren E. HoughiD, Steven T. WhitteniD
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Abstract
The impact of missense genetic variations on protein function is often enigmatic, especially for mutations that map to intrinsically disordered regions (IDRs). Given the functional importance of phase separation of IDRs, it has been proposed that mutations that modulate phase separation might preferentially lead to disease. To examine this idea, we used the robust predictability of phase-separating (PS) IDRs and annotation of disease-associated proteins and mutations to map the correlation between disease and phase separation. Consistent with previous work linking phase separation to cancer and autism spectrum disorder, we find a higher prevalence of predicted phase separation behavior in disease-associated proteins than typical for human proteins. We map the prevalence of phase separation across a wide range of diseases, finding that many, but not all, show an enrichment of phase separation in the proteins associated with them. Strikingly, the pathogenic mutation rate in predicted PS IDRs was elevated three-fold relative to IDRs not predicted to phase separate. Substitutions involving arginine and the aromatic types were among the most pathogenic for PS IDRs, while substitutions involving serine, threonine, and alanine the most benign. We applied these trends to mutations of uncertain clinical significance and predict that half found in PS IDRs are likely pathogenic. We find that phosphorylation sites were enriched in PS IDRs when compared to other protein regions, though mutations at such sites were mostly benign. Pathogenicity was highest for mutations in predicted PS IDRs when also found in a short linear motif, known mediators of protein-protein interactions.
- organism: human
- assay: protein sequence
- research focus: differential expression
- research focus: protein folding & dynamics
- approach: statistical model
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