HypDB

HypDB curates and annotates proline hydroxylation (Hyp) sites in the human proteome to support proteome-wide profiling of Hyp structural and functional roles.

Key Features:

• Extensive data integration: Integration of liquid chromatography-tandem mass spectrometry (LC-MS) analysis and literature mining yields 15,319 non-redundant proline hydroxylation sites, including 8,226 sites identified with high confidence.
• Site-specific evidence: Provides detailed, site-level evidence and precise positional annotation of proline hydroxylation within protein sequences and structures.
• Functional domain enrichment: Annotation analyses reveal significant enrichment of proline hydroxylation on key functional domains implicated in protein structure, stability, and interactions.
• Tissue-specific distribution: Maps proline hydroxylation across 26 human organs and fluids and six cell lines to characterize tissue- and cell-type-specific patterns.
• Network connectivity analysis: Uses network connectivity analyses to link hydroxylated proteins to protein-protein interactions, cellular pathways, and disease-related mechanisms.
• Data-independent quantification (DIA) support: Includes a spectral library that supports data-independent acquisition (DIA) analyses for quantitative studies on clinical tissues and biomarker identification.
• Biomarker identification: Enables discovery of novel proline hydroxylation biomarkers reported for lung cancer and kidney cancer.

Scientific Applications:

• Proteome-wide Hyp profiling: Systematic identification and annotation of Hyp sites across the human proteome for large-scale PTM studies.
• Quantitative clinical proteomics: DIA-based quantification of Hyp in clinical tissue samples to detect differential modification levels and biomarkers.
• Tissue and cell-type studies: Comparative analysis of tissue- and cell-line-specific Hyp distributions to investigate physiological and pathological regulation.
• Functional annotation of substrates: Characterization of functional diversity of Hyp substrates to study effects on protein structure, stability, and interactions.
• Pathway and disease mechanism inference: Use of network connectivity and enrichment analyses to link Hyp-modified proteins to cellular pathways and disease processes.

Methodology:

Compilation and annotation of Hyp sites from LC-MS data and literature mining, site-level annotation and functional domain enrichment analyses, network connectivity analysis, and construction of a spectral library for data-independent acquisition (DIA) quantification.