<b>Beta-Synuclein Degradome Foundation Atlas</b>

DOI

The β-Synuclein Degradome Foundation Atlas provides a comprehensive in silico reconstruction of the theoretical peptide landscape generated from proteolytic cleavage of β-synuclein. This dataset captures the full spectrum of β-synuclein fragments that may arise through normal protein turnover, regulated enzymatic cleavage, and stress-related degradation. This is a paradigm shift from treating β-synuclein as a single static molecule.Using predicted enzymatic and chemical cleavage sites, systematically all possible peptide fragments site are generated and annotated each with key physicochemical properties relevant for proteomics and biomarker discovery. These include molecular weight, mass-to-charge ratio (m/z), net charge, isoelectric point, hydrophobicity, Boman index, instability index, and aliphatic index.The resulting dataset provides a feature-rich reference for peptide-level analysis of β-synuclein biology and is designed to support a wide range of applications, including mass-spectrometry assay design, biomarker discovery in neurodegenerative diseases, degradome modelling, and machine-learning approaches in proteomics.The atlas includes wild-type β-synuclein and disease-associated variants (V70M and P123H) and is accompanied by fully reproducible Python scripts. Future releases will expand the resource with additional mutations and experimentally validated cleavage products.This dataset provides a foundation reference for degradome-centred biomarker research.

Identifier
DOI https://doi.org/10.5522/04/31915992.v1
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Metadata Access https://api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:figshare.com:article/31915992
Provenance
Creator Petzold, Axel ORCID logo
Publisher University College London UCL
Contributor Figshare
Publication Year 2026
Rights https://creativecommons.org/publicdomain/zero/1.0/; http://purl.org/coar/access_right/c_abf2
OpenAccess true
Contact researchdatarepository(at)ucl.ac.uk
Representation
Language English
Resource Type Dataset
Discipline Chemistry; Natural Sciences