bioRxivBioinformaticsPreprint
Predicted condensation-related signatures localize to two C-terminal regions of the prion protein across cervid PRNP variants associated with chronic wasting disease
Guy W DayhoffiD, Lyudmyla DoroshiD, Holger WilleiD, Vladimir N UverskyiD, Maria StepanovaiD
Abstract
Genetic variation in cervid PRNP influences chronic wasting disease (CWD) susceptibility and progression, but it remains unclear whether disease-associated substitutions produce localized differences in sequence-derived properties relevant to prion protein (PrPC) condensation. We compared a white-tailed deer (WTD) reference sequence with 10 modeled variants on the same mature PrP scaffold (residues 24-233). For each sequence, we calculated intrinsic disorder, FuzDrop per-residue droplet-promoting propensity (pDP) and binding-mode entropy (Sbind), predicted residue-level pKa values, and a discrete-charge summary at pH 7.0. The overall profiles preserve a long N-terminal droplet-promoting region, whereas variant-associated differences are concentrated in selected intervals. In the FuzDrop outputs, residues 126-140 show allele-dependent changes in pDP near the droplet-promoting threshold, and residues 220-230 show variant-specific changes in Sbind, including lower Sbind for S225F/Y and higher Sbind for elk-like Q226E. These differences occur without large changes in overall intrinsic disorder. KaML pKa predictions identify near-neutral titratable sites that provide electrostatic context for these local predictor differences. Independent atomistic simulations of monomeric PrP further detected variant-sensitive backbone behavior and intramolecular contacts involving these intervals. Together, the analysis identifies residues 126-140 and 220-230 as computationally defined candidate intervals that overlap regions implicated independently in prion conversion or CWD fibril structure, motivating direct tests of cervid PrP condensation and maturation.
- organism: chicken / avian
- assay: protein sequence
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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