Natural and pathogenic protein sequence variation affecting prion-like domains within and across human proteomes

Abstract

Background: Impaired proteostatic regulation of proteins with prion-like domains (PrLDs) is associated with a variety of human diseases including neurodegenerative disorders, myopathies, and certain forms of cancer. For many of these disorders, current models suggest a prion-like molecular mechanism of disease, whereby proteins aggregate and spread to neighboring cells in an infectious manner. The development of prion prediction algorithms has facilitated the large-scale identification of PrLDs among "reference" proteomes for various organisms. However, the degree to which intraspecies protein sequence diversity influences predicted prion propensity has not been systematically examined. Results: Here, we explore protein sequence variation introduced at genetic, post-transcriptional, and posttranslational levels, and its influence on predicted aggregation propensity for human PrLDs. We find that sequence variation is relatively common among PrLDs and in some cases can result in relatively large differences in predicted prion propensity. Sequence variation introduced at the post-transcriptional level (via alternative splicing) also commonly affects predicted aggregation propensity, often by direct inclusion or exclusion of a PrLD. Finally, analysis of a database of sequence variants associated with human disease reveals a number of mutations within PrLDs that are predicted to increase prion propensity. Conclusions: Our analyses expand the list of candidate human PrLDs, quantitatively estimate the effects of sequence variation on the aggregation propensity of PrLDs, and suggest the involvement of prion-like mechanisms in additional human diseases.

Generalizable Compositional Features Influencing the Proteostatic Fates of Polar Low-Complexity Domains

“…Prion propensity predictions for the G-rich and Q/N-rich LCDs fused to Sup35 were performed using the downloadable modified prion aggregation prediction algorithm (mPAPA) Python script (https://github.com/RossLabCSU/mPAPA, accessed on 26 April 2021; [21,24]). By default, mPAPA uses FoldIndex to identify intrinsically disordered regions and only evaluates disordered domains for prion propensity.…”

“…Specifically, yeast prion domains (PrDs) are a subcategory of LCDs enriched in Q/N residues, with secondary biases for G, S, and/or Y [14][15][16][17]. These features have been incorporated into multiple prion prediction algorithms [16][17][18][19][20][21][22] and have aided in the identification of PrDs and prion-like domains (PrLDs) across a variety of organisms [16,17,[22][23][24][25][26][27][28][29][30].…”

Conserved functions of prion candidates suggest a primeval role of protein self‐templating

“…To broaden our understanding of the ancient functions of prions beyond this small group of confirmed prions, we include functions of proteins that are predicted to harbor prion‐like domains and, thus, may behave as true prions. The development of prion‐prediction algorithms has enabled proteome‐wide analysis of proteins revealing thousands of candidate prions 45,55‐63 . These efforts were recently reviewed by Batlle and Gil‐Garcia 64,65 .…”

Universal functions of prion candidates across all three domains of life suggest a primeval role of protein self-templating

“…To broaden our understanding of the ancient functions of prions beyond this small group of confirmed prions, we include functions of proteins that are predicted to behave as prions. Development of prion-prediction algorithms has enabled proteome-wide analysis of proteins revealing thousands of candidate prions (Cascarina and Ross 2020; Espinosa Angarica, Ventura, and Sancho 2013; Michelitsch and Weissman 2000; P. M. Harrison and Gerstein 2003; Alberti et al 2009; Afsar Minhas, Ross, and Ben-Hur 2017; Sabate et al 2015; Ross et al 2013; Lancaster et al 2014; Zambrano et al 2015). These efforts were recently reviewed by Batlle and Gil-Garcia (Gil-Garcia et al 2021; Batlle et al 2017).…”