The cellular prion protein, PrPC, is neuroprotective in a number of settings and in particular prevents cerebellar degeneration mediated by CNS‐expressed Doppel or internally deleted PrP (‘ΔPrP’). This paradigm has facilitated mapping of activity determinants in PrPC and implicated a cryptic PrPC‐like protein, ‘π’. Shadoo (Sho) is a hypothetical GPI‐anchored protein encoded by the Sprn gene, exhibiting homology and domain organization similar to the N‐terminus of PrP. Here we demonstrate Sprn expression and Sho protein in the adult CNS. Sho expression overlaps PrPC, but is low in cerebellar granular neurons (CGNs) containing PrPC and high in PrPC‐deficient dendritic processes. In Prnp0/0 CGNs, Sho transgenes were PrPC‐like in their ability to counteract neurotoxic effects of either Doppel or ΔPrP. Additionally, prion‐infected mice exhibit a dramatic reduction in endogenous Sho protein. Sho is a candidate for π, and since it engenders a PrPC‐like neuroprotective activity, compromised neuroprotective activity resulting from reduced levels may exacerbate damage in prion infections. Sho may prove useful in deciphering several unresolved facets of prion biology.

Intrinsically disordered regions make up a large part of the proteome, but the sequence-to-function relationship in these regions is poorly understood, in part because the primary amino acid sequences of these regions are poorly conserved in alignments. Here we use an evolutionary approach to detect molecular features that are preserved in the amino acid sequences of orthologous intrinsically disordered regions. We find that most disordered regions contain multiple molecular features that are preserved, and we define these as ‘evolutionary signatures’ of disordered regions. We demonstrate that intrinsically disordered regions with similar evolutionary signatures can rescue function in vivo, and that groups of intrinsically disordered regions with similar evolutionary signatures are strongly enriched for functional annotations and phenotypes. We propose that evolutionary signatures can be used to predict function for many disordered regions from their amino acid sequences.

In previous work, we showed that intrinsically disordered regions (IDRs) of proteins contain sequence-distributed molecular features that are conserved over evolution, despite little sequence similarity that can be detected in alignments (Zarin et al. 2019). Here, we aim to use these molecular features to predict specific biological functions for individual IDRs and identify the molecular features within them that are associated with these functions. We find that the predictable functions are diverse. Examining the associated molecular features, we note some that are consistent with previous reports, and identify others that were previously unknown. We experimentally confirm that elevated isoelectric point and hydrophobicity, features that are positively associated with mitochondrial localization, are necessary for mitochondrial targeting function. Remarkably, increasing isoelectric point in a synthetic IDR restores weak mitochondrial targeting. We believe feature analysis represents a new systematic approach to understand how biological functions of IDRs are specified by their protein sequences.

Intrinsically disordered regions make up a large part of the proteome, but the sequence-to-function relationship in these regions is poorly understood, in part because the primary amino acid sequences of these regions are poorly conserved in alignments. Here we use an evolutionary approach to detect molecular features that are preserved in the amino acid sequences of orthologous intrinsically disordered regions. We find that most disordered regions contain multiple molecular features that are preserved, and we define these as "evolutionary signatures" of disordered regions. We demonstrate that intrinsically disordered regions with similar evolutionary signatures can rescue function in vivo, and that groups of intrinsically disordered regions with similar evolutionary signatures are strongly enriched for functional annotations and phenotypes. We propose that evolutionary signatures can be used to predict function for many disordered regions from their amino acid sequences.