Beyond its established role in synaptic physiology, neurogranin has attracted increasing attention as a candidate biomarker of neuronal injury and synaptic dysfunction. Elevated concentrations of neurogranin have been reported in several neurological disorders, where the protein has been investigated as a potential indicator of synaptic plasticity, degeneration and neurodegenerative disease processes.
Proteins undergoing physiological turnover, proteolytic regulation, and pathological modification generate complex populations of peptide fragments. Experimental studies have demonstrated that neurogranin can be proteolytically processed by endogenous proteases involved in neuronal signaling and protein turnover. However, the potential repertoire of neurogranin-derived peptide fragments has not previously been systematically characterized.
The Ng Degradome Foundation Atlas addresses this gap by enumerating the theoretical degradome that may arise from enzymatic or chemical cleavage of the Ng primary sequence. Each predicted fragment is annotated with a comprehensive panel of physicochemical properties relevant to proteomics, biomarker discovery, and computational peptide analysis.
The dataset comprises every predicted proteolytic fragment derived from the human Ng primary sequence based on defined cleavage boundaries.
The resulting fragment space includes overlapping peptides spanning the entire protein sequence.
For each peptide, the dataset provides:
These features provide a unified, feature-rich representation suitable for mass-spectrometry analysis, biomarker discovery, and computational proteomics workflows.
The Ng Degradome Atlas was generated using a reproducible Python workflow consisting of the following steps:
Definition of cleavage sites
Experimentally reported and computationally defined cleavage positions were specified along the neurogranin amino-acid sequence.
Fragment enumeration
All pairwise subsequences between cleavage boundaries were generated, producing the complete theoretical degradome.
Peptide property calculation
Physicochemical properties were calculated using the peptides Python library.
Structured data export
All peptide information was exported as structured CSV tables.
Dataset consolidation
Output files were merged and compressed into a single FAIR-compliant archive (TAR.XZ format) to facilitate efficient distribution and reproducibility.
The complete Python workflow is included in the repository, enabling transparent re-execution and extension to additional proteins, cleavage definitions, or sequence variants.
Primary file
Neurogranin_Degradome_Foundation_Atlas_v1.tar.xz
Contents
File Type
ASCII comma-separated values (CSV)
Compression
xz -9 -T0 (maximum parallel compression for efficient distribution)
The dataset can be used directly in:
It can also be integrated into proteomics workflows, including:
This dataset follows FAIR data principles.
Findable
Rich metadata, persistent DOI, and a search-optimized dataset description.
Accessible
Publicly available through the open-access Figshare repository.
Interoperable
Standard CSV format and widely used physicochemical descriptors enable integration with common computational and proteomics tools.
Reusable
The dataset includes a fully reproducible Python workflow and transparent data generation pipeline.
The Neurogranin Degradome Foundation Atlas enables research across several biomedical and computational domains:
The dataset may also serve as a reference framework for interpreting peptide-level signals detected in cerebrospinal fluid or blood proteomic studies of neurological disease.
This release represents Version 1 of the Neurogranin Degradome Foundation Atlas.
Future updates will incorporate:
These developments will progressively refine modelling of neurogranin proteolysis in health and neurodegenerative disease.
If you use this dataset, please cite this Figshare record:
DOI: 10.5522/04/32168850
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