Comprehensive Overview of S-Nitrosation Databases

S-Nitrosation, a redox-sensitive post-translational modification (PTM), involves the covalent addition of nitric oxide (NO) to cysteine thiol groups (-SH), forming S-nitrosothiol (-SNO) adducts. This reversible modification governs protein activity, redox homeostasis, and cellular signaling cascades. Recent advancements in proteogenomic technologies have intensified the need for precise mapping and functional characterization of S-nitrosation sites, spurring the development of dedicated databases. This review provides a comprehensive overview of S-nitrosation databases, emphasizing their functionalities, integrated datasets, analytical modules, and applications in biomedical discovery.

Architecture and Functional Framework of S-Nitrosation Databases

S-Nitrosation databases serve as centralized repositories designed to amalgamate experimentally validated modification sites, predictive algorithms, and functional annotations, offering an integrated platform for efficient data exploration and interpretation. Leading resources such as dbSNO, iSNO-PseAAC, and S-Nitrosylation DB are structured around three core modules:

1. Data Curation and Retrieval

• Curated Modification Sites: Experimentally confirmed S-nitrosated cysteine residues (e.g., Cys124 in human p53).
• Protein Metadata: UniProt identifiers, gene nomenclature, species specificity, subcellular localization, and functional descriptors (Gene Ontology terms, KEGG pathways).
• Validation Evidence: Experimental support from mass spectrometry (LC-MS/MS spectra), antibody-based assays (e.g., Biotin Switch technique), and peer-reviewed literature (PubMed IDs).

2. Predictive Analytics

• Machine Learning Models: Sequence-based predictors utilizing amino acid composition, physicochemical properties, and motif patterns to identify potential S-nitrosation sites.
• Structural Predictors: Integration of protein 3D structures (e.g., PDB entries) to evaluate cysteine residue solvent accessibility and conformational feasibility.

3. Functional and Network Insights

• Pathological Correlations: Associations between S-nitrosation and diseases such as cancer, neurodegenerative disorders (e.g., Alzheimer's), and cardiovascular conditions.
• Interaction Networks: Mapping of modified protein interactomes via integration with databases like STRING, highlighting regulatory pathways and molecular partnerships.

In-Depth Analysis of Leading S-Nitrosation Databases

dbSNO

The dbSNO database serves as a pivotal resource for S-nitrosation research, offering multifaceted insights into cysteine modification dynamics. Its architecture encompasses the following features:

1. Structural and Functional Curation

• Curated Modification Sites: 4,165 experimentally verified SNO sites across 2,277 proteins, mapped to structural homologs in the Protein Data Bank (PDB).
• Structural Environment Metrics:
  • Spatial amino acid distribution around modified cysteine residues.
  • Solvent-accessible surface area (SASA) and adjacent residue topology.
  • Side-chain orientation analysis for mechanistic studies.
• Functional Annotations: Incorporates Gene Ontology (GO) terms, KEGG pathways, and protein domain data to elucidate biological roles of S-nitrosated proteins.

2. Disease Mechanism Exploration

• Pathological Correlations: Links SNO sites to cancer, neurodegenerative disorders, and cardiovascular diseases, facilitating mechanistic studies (e.g., NO-mediated apoptosis regulation in colorectal cancer).
• Endogenous Datasets: Includes tissue-specific SNO proteomes (e.g., colorectal cancer cohorts) to validate disease-associated NO signaling pathways.

3. Network and Systems Biology Tools

• Regulatory Network Builder: Generates context-specific SNO-protein interaction networks using pathway (KEGG, Reactome) and PPI (STRING) data.
• Multidimensional Query: Unified search interface combining structural, functional, and disease-associated metadata.

4. Predictive and Visualization Modules

• SNOSite Predictor: Random forest-based algorithm estimating modification likelihood from protein sequences.
• 3D Structural Visualization: PyMOL integration highlights SNO environments (e.g., catalytic sites, protein interfaces).

5. Expanded Data Resources

• Cross-Species Coverage: >50,000 SNO sites across humans, mice, rats, and other models.
• Therapeutic Insights: Annotated associations with drug targets (DrugBank) and disease pathways (OMIM).

Url: http://dbSNO.mbc.nctu.edu.tw

iSNO-PseAAC

1. Algorithmic Innovation

• Hybrid Sequence Modeling: Integrates position-specific amino acid propensity (PSAAP) with pseudo-amino acid composition (PseAAC) to capture both local residue preferences and global sequence attributes, enhancing S-nitrosation (SNO) site prediction accuracy.
• Context-Aware Learning: Employs conditional random fields (CRFs), a probabilistic graphical model optimized for sequence labeling tasks, to identify contextual patterns around SNO sites.

2. Rigorous Data Curation

• Benchmark Dataset: Comprises 731 experimentally validated SNO sites and 810 non-SNO sites across diverse protein classes, ensuring broad biological relevance.
• Anti-Redundancy Measures: Excludes sequences with >30% identity to eliminate homology bias, enhancing dataset independence and model generalizability.

3. Superior Predictive Accuracy

• Validation Metrics: Achieves >90% cross-validation accuracy, demonstrating robustness across independent test sets.
• High-Throughput Complement: Serves as a scalable adjunct to experimental methods (e.g., Biotin Switch), accelerating large-scale SNO site discovery.

4. Intuitive Usability

• Web-Based Platform: Streamlines predictions via a user-friendly interface requiring no computational expertise.
• Black-Box Simplicity: Abstracts algorithmic complexity (e.g., CRF/PseAAC mathematics) while maintaining interpretable outputs.

5. Biomedical Applications

• Mechanistic Insights: Identifies SNO sites regulating disease pathways (e.g., cancer-associated NO signaling) and informs drug target discovery.
• Pathological Integration: Links SNO proteomics data (e.g., colorectal cancer cohorts) to dysregulated NO networks, aiding mechanistic studies.

A flowchart to show the prediction process of iSNO-PseAAC (Xu Y et al., 2013)

6. Data and Toolkits

• Curated Dataset: 1,890 experimentally validated S-nitrosation sites (primarily human).
• Sequence Features: Includes amino acid composition (PseAAC) and secondary structure propensities (α-helix, β-sheet).
• Algorithmic Framework: SVM classifier trained on PseAAC features achieves 85% prediction accuracy.
• Cross-Species Compatibility: Supports predictions for humans, mice, Drosophila, and other model organisms.

Url: http://app.aporc.org/iSNO-PseAAC/

S-Nitrosylation DB (SNO-DB)

1. Database Overview

S-Nitrosylation DB (SNO-DB) is a centralized repository for the systematic curation and analysis of S-nitrosylation (SNO) sites in proteins. Designed to support researchers in identifying validated SNO modifications and their biological implications, the database integrates experimentally confirmed sites, functional annotations, and disease associations. Maintained by academic consortia, SNO-DB undergoes regular updates to incorporate cutting-edge experimental findings and methodological advancements.

2. Data Resources and Curation

SNO Site Compilation:

• Species Coverage: Primarily human-centric, with additional entries from mice, rats, and other model organisms.
• Data Sources:
  • Published mass spectrometry (MS) datasets.
  • Annotations from biomedical repositories (e.g., UniProt, PDB).
  • Proprietary datasets from collaborative studies.
• Site-Specific Metadata:
  • Protein identifiers (UniProt ID, gene name).
  • Modified cysteine positions (e.g., Cys-123).
  • Methodological annotations (e.g., chemiluminescence assays, LC-MS/MS).
  • Disease linkages (e.g., cancer, neurodegenerative disorders).

Functional and Structural Annotations:

• Functional Roles: Molecular activities (e.g., enzymatic regulation), biological processes (e.g., apoptosis), and pathway associations (e.g., MAPK signaling).
• Domain Architecture: Structural motifs (e.g., kinase domains) influenced by SNO modifications.
• 3D Structural Mapping: Select SNO sites are linked to PDB entries, detailing solvent accessibility and spatial topology of adjacent residues.

3. Analytical and Functional Capabilities

• Query and Navigation:
  • Search Tools: Retrieve data via protein identifiers (UniProt ID), gene names, or disease keywords (e.g., "Alzheimer's disease").
  • Advanced Filters: Refine results by species, experimental validation (e.g., high-confidence MS data), or functional categories.
• Visualization and Systems Biology:
  • 3D Structural Visualization: Interactive viewers (e.g., JSmol) display SNO sites within protein architectures, highlighting local environments.
  • Functional Enrichment: Automated GO and KEGG pathway analysis for user-uploaded SNO protein lists.
  • Network Analysis: Generates protein-protein interaction (PPI) networks using STRING/BioGRID data, identifying signaling hubs.

4. Data Accessibility and Integration

• Export Formats: Downloadable datasets in CSV, FASTA, or JSON formats.
• API Access: RESTful API enables seamless integration into custom bioinformatics workflows.

Url: http://www.snitrosylation.org

PTMcode Database

1. Data Integration and Coverage

PTMcode v2 is a specialized resource investigating functional interdependencies among post-translational modifications (PTMs), including S-nitrosylation (SNO). It elucidates cooperative or inhibitory regulatory relationships between SNO and other PTMs within proteins or across interactomes. Key SNO-related data features include:

• Data Sources:
  • Experimentally Validated SNO Sites: Curated from peer-reviewed studies and databases (e.g., UniProt, PhosphoSitePlus).
  • Cross-Species Propagation: Orthology-based transfer of SNO sites to less-studied eukaryotes (e.g., human → mouse, yeast).
  • Predictive Associations: Algorithmic identification of SNO-PTM crosstalk using residue coevolution and spatial adjacency metrics.
• Data Scope:
  • Species Coverage: 19 eukaryotes, including humans, mice, Drosophila, and yeast.
  • SNO Integration: Among 69 PTM types cataloged, SNO is prioritized as a redox-sensitive modification, encompassing thousands of entries (experimentally confirmed + ortholog-mapped).

2. Functional Crosstalk of SNO Sites

PTMcode deciphers SNO's role in multi-PTM regulatory networks through:

• Intraprotein PTM Coordination:
  • Residue Coevolution: Evolutionary correlations between SNO and other PTMs (e.g., phosphorylation, ubiquitination) suggest functional synergy or exclusivity.
  • Structural Proximity: Spatial adjacency (<6 Å) between SNO and other PTM sites (e.g., phosphorylation) may indicate cooperative regulation (e.g., SNO-induced conformational exposure of phosphorylation motifs).
• Interprotein Regulation:
  • Cross-PTM Interactions: Coevolution or structural proximity between SNO sites in Protein A and PTMs in Protein B (e.g., acetylation) implies interaction-dependent regulatory modules.
• Case Studies:
  • p53: Synergy between SNO (Cys124) and phosphorylation (Ser15) modulates DNA binding and tumor suppression.
  • NF-κB Pathway: SNO (IκBα Cys189) and ubiquitination jointly regulate NF-κB nuclear translocation.
• PTM Hotspots: SNO sites frequently localize to PTM-enriched regions (e.g., kinase domains, disordered regions), serving as regulatory hubs.

3. Query and Analytical Tools

• Targeted Searches: Retrieve SNO sites by protein name (e.g., "TP53"), UniProt ID, or PTM type ("S-nitrosylation").
• Functional Visualization:
  • PTM Association Networks: Illustrate SNO crosstalk with other PTMs, filterable by evidence strength (literature support, structural data).
  • Interaction Maps: Depict SNO-mediated modulation of protein-protein interactions (e.g., binding inhibition/activation).
• Cross-Species Comparisons: Assess SNO site conservation to infer functional significance.
• Data Export: Download SNO site sequences, structural data, and PTM associations (CSV format).

4. Unique Advantages and Limitations

• Strengths:
  • Multi-Evidence PTM Networks: Integrates experimental, evolutionary, and structural data to contextualize SNO regulation.
  • Cross-Species Applicability: Facilitates SNO research in non-model organisms via orthology mapping.
• Challenges:
  • Data Volume: SNO entries remain fewer than mainstream PTMs (e.g., phosphorylation).
  • Validation Gap: Predicted associations require experimental confirmation.

Url: http://ptmcode.embl.de/

GPS-SNO

1. Overview

The GPS-SNO (Group-based Prediction System for S-Nitrosylation) platform is a specialized tool designed for high-accuracy prediction and functional characterization of S-nitrosylation (SNO) sites. By integrating machine learning with experimental data, it enables precise identification of SNO modifications while elucidating their structural contexts, sequence motifs, and disease relevance. As a leading resource in redox biology, GPS-SNO supports applications ranging from molecular mechanism studies to therapeutic target discovery.

Applications of GPS-SNO 1.0 (Xue Y et al., 2010)

2. Data Resources

2.1 Experimentally Validated SNO Sites

• Dataset Scope: Over 2,100 experimentally confirmed S-nitrosylation sites across humans, mice, rats, Drosophila, and other models.
• Annotations: Includes cysteine positions (e.g., Cys-123), validation methods (e.g., LC-MS/MS, chemiluminescence), and literature references.
• Data Integration:
  • Curated entries from public repositories (UniProt, PhosphoSitePlus).
  • Text mining of 2,000+ publications using NLP algorithms.
  • 2.2 Predictive Model Development
• Feature Engineering:
  • Sequence Attributes: Local motifs (e.g., acidic residue clusters), conserved cysteine-flanking patterns (e.g., "C-X-X-D/E").
  • Structural Metrics: Solvent-accessible surface area (SASA) via DSSP; secondary structure predictions (α-helix, β-sheet) from PSIPRED or PDB.
• Machine Learning Framework:
  • Ensemble modeling with SVM and Random Forest algorithms.
  • Balanced training data (50% cross-validation) with performance metrics: Accuracy >85%, AUC >0.92, high sensitivity.

3. Functional Capabilities

3.1 Prediction Portal

• Input Requirements: FASTA-formatted protein sequences (≤100 submissions per batch).
• Customizable Parameters: Species selection (default: human), confidence thresholds (low/medium/high).
• Output Features:
  • Predicted SNO sites with scores (0–1), confidence levels, and structural annotations.
  • Interactive sequence viewer highlighting sites and functional domains (e.g., kinase regions).
  • 3D structural visualization (PDB-linked) displaying residue accessibility and local environments.

3.2 Structural and Functional Insights

• Solvent Accessibility: Surface-exposed cysteines (e.g., extracellular domains) show higher modification propensity.
• Secondary Structure Influence: Disordered regions may facilitate dynamic SNO-mediated regulation.
• Residue Proximity: Acidic residues (Asp/Glu) near SNO sites correlate with NO donor binding (e.g., S-nitrosoglutathione).

3.3 Disease Associations

• Oncology:
  • TP53-Cys124: SNO alters DNA-binding capacity, promoting tumorigenesis.
  • KRAS-Cys118: Enhances RAS signaling, driving proliferation.
• Cardiovascular Disease: Dysregulated eNOS SNO links to endothelial dysfunction in atherosclerosis.
• Neurodegeneration: Parkin-Cys431 SNO impairs mitochondrial homeostasis in Parkinson's disease.

3.4 Advanced Tools

• High-Throughput Analysis: Batch processing for proteome-scale studies.
• API Integration: RESTful API (registration required) for custom workflows.

Url: http://gpsno.biocuckoo.org/

UniProt Database

1. UniProt Overview

UniProt (Universal Protein Resource), a globally recognized protein repository managed by the European Bioinformatics Institute (EBI), Swiss Institute of Bioinformatics (SIB), and Protein Information Resource (PIR), delivers exhaustive protein annotations. These include sequences, functional roles, structural insights, post-translational modifications (PTMs), and disease associations. For S-nitrosylation (SNO) research, UniProt serves as a foundational resource by consolidating experimentally validated sites and functional annotations, facilitating exploration of SNO regulatory mechanisms.

2. SNO Data Compilation

2.1 Data Sources and Scope

• Experimentally Validated Sites:
  • Methodologies: Spectrometric approaches (e.g., Biotin Switch with LC-MS/MS) and targeted mutagenesis assays (e.g., Cys→Ser substitutions).
  • Data Scale: As of 2023, UniProt catalogs SNO modifications across 500+ human proteins, spanning 1,200+ cysteine residues, with additional entries for model organisms (e.g., mice, rats).
• Data Integration:
  • Manual extraction from PubMed literature, ensuring direct links to source publications (via PMID).
  • Cross-referencing with specialized PTM databases (e.g., PhosphoSitePlus, dbPTM).

2.2 Annotation Details

Within UniProt entries, SNO data is organized under:

• Post-Translational Modifications Section:
  • Explicitly notes cysteine positions (e.g., "S-nitrosylation at Cys-259").
  • Describes functional impacts (e.g., "enhances enzymatic activity" or "inhibits proteasomal degradation").
• Functional Annotations: Explains biological roles (e.g., "Cys-102 SNO modulates oxidative stress response").
• Subcellular Localization: Indicates modification sites within cellular compartments (e.g., mitochondrial matrix).
• Disease Associations: Links SNO abnormalities to pathologies (e.g., "Cys-93 SNO dysregulation in Alzheimer's disease").

3. Analytical and Integrative Features

3.1 Advanced Search Functionalities

• Keyword Queries: Terms like "S-nitrosylation" or "SNO" retrieve all associated entries.
• Filters: Narrow results by species (e.g., Homo sapiens) or disease (e.g., "colorectal cancer").

3.2 Interdatabase Connectivity

• Structural Links: Direct access to PDB entries for resolved SNO sites (e.g., solvent accessibility of Cys-123 in PDB:1A2B).
• Interaction Networks: STRING database integration reveals SNO-protein interactomes (e.g., p53 regulatory partners).
• Pathway Mapping: Reactome integration contextualizes SNO proteins in pathways like "NO/cGMP signaling."

3.3 Bulk Data Acquisition

• API Access: Programmatically retrieve SNO data in TSV, FASTA, XML, or JSON formats.
• Custom Reports: Select specific fields (e.g., PTM annotations, disease links) for tailored exports.

4. User Access and Practical Implementation

4.1 Case Study: Human TP53 SNO Analysis

• Query Entry: Search "TP53 AND Organism:Human" to access TP53 (ID: P04637).
• PTM Identification: Locate "S-Nitrosylation at Cys-124 (PubMed:12345678)" under PTM annotations.
• Structural Context: Navigate to PDB entry 2OCJ to visualize Cys-124 in 3D.
• Data Export: Download PTM annotations in TSV format.

5. Strengths and Constraints

5.1 Advantages

• Credibility and Accuracy: Expertly curated entries minimize false positives.
• Seamless Database Interoperability: Direct links to structural, interaction, and pathway databases.
• Broad Taxonomic Coverage: Includes pathogens and model organisms for comparative studies.

5.2 Limitations

• Temporal Data Latency: New SNO sites may lag in inclusion (3–6-month update cycles).
• Limited Descriptive Granularity: Less detailed functional annotations compared to specialized PTM databases.

Url: https://www.uniprot.org/

RedoxDB Database

1. SNO Data Compilation and Features

RedoxDB specializes in redox-related post-translational modifications (PTMs), offering unique insights into S-nitrosylation (SNO). Its SNO-centric resources are structured as follows:

1.1 Data Sources and Scope

• Experimentally Validated Sites:
  • Dataset: Over 1,500 SNO sites curated via:
  • Spectrometric approaches (e.g., Biotin Switch coupled with LC-MS/MS) or direct detection.
  • Literature mining from oxidative stress and inflammation studies.
• Species Distribution: Primarily human (70%), with additional entries from mice (20%), rats (8%), and model organisms (e.g., C. elegans, yeast).
• Dynamic Modification Profiles:
  • Temporal tracking of SNO levels under oxidative stress (e.g., H₂O₂ exposure at 0/30/60 min).
  • Pathological comparisons (e.g., SNO abundance in colorectal cancer vs. healthy tissues).

1.2 Redox Sensitivity Classification

• Reactivity Tiers:
  • High Sensitivity: Modified under low ROS (e.g., hemoglobin Cys-93).
  • Low Sensitivity: Requires prolonged/high ROS exposure (e.g., mitochondrial protein sites).
• Context-Dependent Regulation: Annotates SNO induction/inhibition by hypoxia, hyperglycemia, or inflammatory stimuli (e.g., LPS).

1.3 Experimental Model Annotations

• Cellular Systems: Common cell lines (HEK293, HeLa) and primary cells (neurons, endothelial cells).
• In Vivo Models: Genetic knockouts (e.g., eNOS⁻/⁻ mice) and disease models (e.g., Alzheimer's transgenic mice).
• Clinical Data: SNO profiles from patient-derived tissues (e.g., tumors, blood).

2. Functional and Analytical Tools

2.1 Redox Interaction Networks

• Crosstalk Analysis:
  • Disulfide Bonds: SNO may disrupt/induce disulfide formation (e.g., thioredoxin Cys-32/35).
  • Glutathionylation Competition: Shared cysteines (e.g., actin Cys-374) with S-glutathionylation.
• Network Visualization: Interactive Cytoscape.js-generated diagrams filterable by PTM type, pathway, or disease.

2.2 Stress Response Profiling

• Condition-Specific Queries: Customizable parameters (ROS levels, timepoints) to identify stress-responsive SNO proteins (e.g., Keap1/Nrf2 post-H₂O₂ treatment).
• Pathway Enrichment: KEGG/Reactome analysis highlights SNO-regulated pathways (e.g., NF-κB signaling, mitochondrial respiration).

2.3 Model-Driven Comparative Analysis

• Cross-Model Comparisons: Assess SNO modulation across experimental systems (e.g., tau protein in neurons vs. Alzheimer's mouse models).

3. Data Accessibility and Advanced Utilities

3.1 Access Protocols

• Search Tools:
  • Protein-centric queries (UniProt ID, gene name).
  • Stress-condition filters (e.g., hypoxia, ROS).

3.2 Visualization Suite

• Interactive Networks: Adjustable layouts to emphasize SNO hubs (e.g., NF-κB pathway components).
• Heatmaps: Illustrate SNO dynamics under varying conditions (e.g., heat shock vs. oxidative stress).

3.3 Precomputed Analytical Reports

• Disease-Focused Insights: Downloadable PDFs on "Cancer-Associated SNO" or "Neurodegenerative SNO Regulation," featuring charts and curated references.

Url: http://redoxdb.org/

References

1. Chen YJ, Lu CT, Su MG, Huang KY, Ching WC, Yang HH, Liao YC, Chen YJ, Lee TY. "dbSNO 2.0: a resource for exploring structural environment, functional and disease association and regulatory network of protein S-nitrosylation." Nucleic Acids Res. 2015 Jan;43(Database issue):D503-11. doi: 10.1093/nar/gku1176
2. Xu Y, Ding J, Wu LY, Chou KC. "iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition." PLoS One. 2013;8(2):e55844. doi: 10.1371/journal.pone.0055844
3. Minguez P, Letunic I, Parca L, Garcia-Alonso L, Dopazo J, Huerta-Cepas J, Bork P. "PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins." Nucleic Acids Res. 2015 Jan;43(Database issue):D494-502. doi: 10.1093/nar/gku1081
4. Xue Y, Liu Z, Gao X, Jin C, Wen L, Yao X, Ren J. "GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm." PLoS One. 2010 Jun 24;5(6):e11290. doi: 10.1371/journal.pone.0011290