Search bioRxiv⌕ Search

bioRxiv · 10.1101/314625

Structure analysis of human Prion protein involved in Sporadic Fatal Insomnia

Abstract

Prion disorders are the root cause of Transmissible Spongiform Encephalopathies (TSE), a group of lethal diseases portrayed by progressive neurodegeneration and spongiosis. In recent years, researchers have come to understand that it is not the endogenous presence of Prions itself that causes neurodegeneration, but the amount of prion proteins that accumulates in the nervous tissue, leading them to exert neurotoxicity. More specifically, the cause of these disorders is mapped to several mutations that can bring the prion protein structure to a disordered permanent misfolded state. Our research is focused on Sporadic Fatal Insomnia (sFI), a rare TSE characterized by severe and chronic insomnia, leading to a life expectancy estimation of about two and a half years, from the onset of the first symptoms. The goal of this work was to analyze through computational studies the structure of the native human Prion Protein (PrPnat) and compare it with the toxic form (FI-Prion) which causes disease. Our findings show that the structure of the human mutant FI-Prion, responsible for Sporadic Fatal Insomnia is more flexible than the native human form PrPnat. Specific regions of the mutant seem to fluctuate more freely than the corresponding loops in the native form. We also identified amino acids Tyr128 and Met129 to be the key residues playing a major role in the manifestation of the disease. Therefore, weve learnt that the FI-Prion is more flexible than PrPnat. In addition, we also confirmed that sporadic fatal insomnia is undoubtedly an infectious disease.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Camp, P. J., Tabaee Damavandi, P., Pickersgill, R. W., Dove, M. T.. 2018-05-04. Structure analysis of human Prion protein involved in Sporadic Fatal Insomnia. https://doi.org/10.1101/314625

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

StableProt: Structure-Aware Deep Learning for Protein Thermostability (Tm) and Environmental Adaptation (OGT) Prediction

Predicting protein melting temperature (Tm) and organismal optimal growth temperature (OGT) is essential for enzyme engineering, biocatalyst design, and metagenomic bioprospecting, yet computational prediction faces several fundamental bottlenecks: sequence-only representations miss tertiary packing; dual-task coupling induces destructive gradient conflict; natural database skew (>87% mesophiles) drives mesophilic collapse; and existing tools produce uncalibrated point predictions. To address these challenges, we present StableProt, a structure-aware framework that couples amino acid sequences with Foldseek 3Di structural tokens via a frozen SaProt backbone to capture 3D contacts. Disjoint heads condition thermodynamic stability on ecological context without gradient conflict, while targeted 14% mesophilic subsampling expands thermophile representation to eliminate mesophilic collapse. Calibrated ensemble modeling reduces calibration error to 1.92%, providing dependable 95% confidence intervals. Evaluating models across 10{degrees}C temperature intervals reveals severe baseline collapse at thermal extremes, whereas StableProt maintains robust accuracy across regimes. On ProThermDB and zero-shot FireProtDB, StableProt achieves state-of-the-art probabilistic accuracy (CRPS = 4.52{degrees}C and 8.71{degrees}C) while recovering 92.0% of hyperthermophiles ([≥]80{degrees}C), and in prospective validation, its calibrated intervals capture both enzyme stability (75.3% coverage) and host environmental growth temperatures (78.1% coverage). StableProt is freely accessible as an interactive web server at https://stableprot.dixitlab.org/.

bioinformatics↗

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗