The GABA Receptor Subunit Degradome Foundation Atlas (Version 1) is a comprehensive, open-access reference dataset and computational framework that systematically maps the complete theoretical degradome of human gamma-aminobutyric acid (GABA) receptor subunits. Developed as an in silico reconstruction, this foundation atlas represents GABA receptor subunits not as static entities, but as a dynamic ensemble of potential peptide fragments generated through regulated proteolytic cleavage and theoretical protein turnover.Biological and Clinical SignificanceGABA receptors are the primary inhibitory neurotransmitter receptors in the human central nervous system and are central to neurophysiology, pharmacology, and brain development. While the logical mathematical combination of human GABA receptor subunits exceeds 150,000 permutations, biological constraints restrict this to approximately 50 functional variations in humans. This atlas adopts a rigorous, purely mathematical and logical approach to catalogue every theoretically possible contiguous peptide fragment derived from wild-type human GABA receptor subunit sequences based on predicted enzymatic and chemical cleavage events.Comprehensive knowledge of the GABA receptor degradome is essential for:Biomarker Discovery: Interpreting proteomic measurements across neurological, psychiatric, and systemic human diseases.Systems Biology: Contextualising biomarker degradomes reliably alongside other neuro-proteomes.Peptidomics Workflow Optimization: Mapping mass spectrometry features to precise subunit origins.Included Fragment AnnotationsEvery peptide fragment generated within the atlas is extensively annotated with critical biochemical and biophysical characteristics required for downstream bioinformatics and statistical analysis:Sequence Identification: Unique peptide identifier (id) and exact amino acid sequence (peptide).Mass Spectrometry Metrics: Molecular weight in Daltons (Da) and mass-to-charge ratio (mz).Physicochemical Properties: Net electric charge (charge) and calculated isoelectric point (isoelectric_point / $pI$).Stability & Hydrophobicity Descriptors: Boman index (Boman), hydrophobicity index (hydrophobicity), predicted instability index (instability_index), and aliphatic index (aliphatic_index).Repository ContentsThis open-access repository contains the reproducible Python source code, pipeline dependencies, and resulting high-resolution datasets:Main Execution Scripts: Individual Python files mapped by UniProt accession numbers (e.g., P18505.py) containing explicitly defined cleavage site positions.Structured Datasets: Individual comma-separated (.csv) output files containing all computed peptide fragments and properties for each target subunit sequence.Environment Configuration: A requirements.txt file specifying software dependencies.Documentation: A technical README.txt detailing usage guidelines and decompression instructions.Version 1 GABA Subunit CoverageVersion 1 comprehensively catalogues the following 23 human wild-type GABA receptor subunit sequences and associated proteins:Subunit / Protein NameUniProt Accession / FASTA IDGamma-aminobutyric rho-3A8MPY1GABA receptor subunit piO00591GABA receptor subunit deltaO14764GABA receptor subunit type 2O75899GABA receptor subunit associated protein 1O95166GABA receptor subunit alpha-1P14867GABA receptor subunit beta-1P18505GABA receptor subunit gamma-2P18507GABA receptor subunit rho-1P24046GABA receptor subunit beta-3P28472GABA receptor subunit rho-2P28476GABA receptor subunit alpha-5P31644GABA receptor subunit alpha-3P34903GABA receptor subunit alpha-2P47869GABA receptor subunit beta-2P47870GABA receptor subunit alpha-4P48169GABA receptor subunit epsilonP78334GABA receptor subunit alpha-6Q16445GABA receptor subunit gamma-1Q8N1C3GABA receptor subunit gamma-3Q99928GABA receptor subunit 1Q9UBS5GABA receptor subunit thetaQ9UN88Gamma-aminobutyric acid receptor-associated protein 2P60520Reproducibility, Customisation, and UsageThe workflow utilises SQLite as an in-memory database to store intermediate peptide structures, guaranteeing deterministic behaviour and flawless reproducibility. Standardised features are computed using the open-source peptides Python library.Quick Start Pipeline:Dependencies: Create a virtual environment and run pip install pandas peptides.Execution: Execute the subunit-specific scripts (python P18505.py) to generate the corresponding structured datasets (p18505.csv).Customization: Users can modify the internal sites list within the scripts to adapt the workflow for alternative cleavage models, specialized proteases, or genetic variants.Citations and ReferencesIf you use this dataset, source code, or conceptual framework in your research, please cite both of the following references:Dataset DOI: doi:10.5522/04/32820329Methodological Framework: Petzold A. Proteolysis-based biomarker repertoire of the neurofilament proteome. Journal of Neurochemistry. 2025;169:e70023. doi:10.1111/jnc.70023