2026-05-31T02:52:47Z https://api.figshare.com/v2/oai
oai:figshare.com:article/31346173 2026-02-16T09:36:21Z category_24208 portal_549 item_type_3 month_year_02_2026
10.5522/04/31346173.v1 https://figshare.com/articles/dataset/_b_TDP-43_Degradome_Foundation_Atlas_b_/31346173 https://ndownloader.figshare.com/files/61927663 https://ndownloader.figshare.com/files/61927666 https://ndownloader.figshare.com/files/61927771 https://ndownloader.figshare.com/files/61927801 Petzold, Axel Axel Petzold 0000-0002-0344-9749 <![CDATA[<b>TDP-43 Degradome Foundation Atlas</b>]]> Bioinformatics and computational biology not elsewhere classified TDP-43 filaments TDP -43 Amyloidogenic Region TAR DNA-binding protein-43 amyotrophic lateral sclerosis ALS protein aggregation 2026-02-16 2026-02-16 Dataset 2026 University College London This open-access repository presents the TDP-43 Degradome Foundation Atlas, a high-resolution, fully annotated dataset of proteolytic TDP-43 fragments with complete amino acid sequences and detailed biochemical metadata.

TDP-43 is a central molecular driver of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), with cytoplasmic aggregation and C- and N-terminal fragmentation representing pathological hallmarks of disease. The Atlas captures TDP-43 as a dynamic ensemble of peptide fragments generated through physiological turnover, stress-related processing, and disease-associated cleavage. Version 1 includes the wild-type protein and can be expanded to selected disease-relevant variants. This provides a structured peptide-level framework for mechanistic and translational research on TDP-43.

This dataset is suitable for classical biomarker discovery as well as for computational and AI-driven approaches in neurodegeneration. The structured, labelled format makes it particularly valuable for:

  • Training supervised models to detect or predict proteolytic cleavage sites
  • Feature extraction from intrinsically disordered and RNA-binding protein sequences
  • Modelling relationships between fragmentation, phase separation, and aggregation
  • Clustering fragment profiles by mutation or disease context
  • Integrating with transcriptomic, proteomic, or immunological datasets for multimodal analyses

    Dataset Features:
  • Systematically enumerated and annotated TDP-43 proteolytic fragments
  • Detailed biochemical and biophysical peptide descriptors
  • Tab-delimited ASCII / CSV format compatible with Python, R, SAS, and ML frameworks
  • Fully reproducible Python workflow for regeneration and expansion
  • Designed for iterative updates as new TDP-43 mutations and cleavage sites are identified

The dataset was generated using a transparent, reproducible computational pipeline based on open-source tools. All scripts are provided and documented, enabling full reproducibility and adaptation to future research needs.

This Atlas is particularly relevant for researchers in neurodegeneration, proteomics, RNA biology, bioinformatics, neuroinflammation, and artificial intelligence who are developing tools to understand, predict, and therapeutically target TDP-43 fragmentation and aggregation pathways in human disease.

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