SNO

SNO performs structural analysis and characterization of S-nitrosylation (SNO) sites to identify proximal cysteine residues, assess their potential for disulfide formation, and evaluate functional impacts on protein conformation.

Key Features:

• High-Throughput Structural Analysis: Analysis of a dataset of 4,172 known S-nitrosylated proteins to identify proximal or vicinal cysteines that may form disulfide bridges with SNO sites.
• Enhanced Sampling Simulations: Use of enhanced sampling simulations to elucidate S-nitrosylation-induced conformational mechanisms, exemplified by studies on TRAP1.
• Coarse-Grained Modeling: Application of a coarse-grained model to 44 protein targets to account for protein flexibility and identify up to 1,248 proximal cysteines that can sense the redox state of SNO sites.
• Bioinformatic Workflows: Two bioinformatics pipelines (https://github.com/ELELAB/SNO_investigation_pipelines) for identifying proximal/vicinal cysteines and providing structural annotations.
• Variant Classification: Analysis and classification of mutations in tumor suppressors and oncogenes as neutral, stabilizing, or destabilizing with respect to propensity for S-nitrosylation and population-shift mechanisms.

Scientific Applications:

• Proteome-scale S-nitrosylation mapping: Identification and structural annotation of SNO sites across 4,172 proteins to prioritize candidates for molecular studies.
• Disulfide bridge prediction: Prediction of proximal cysteines likely to form disulfide bonds with SNO sites and affect protein conformation.
• Redox switch analysis: Detection of cysteines that act as sensors of SNO redox state to study redox-dependent conformational changes.
• Mechanistic studies of specific proteins: Elucidation of S-nitrosylation-induced conformational mechanisms in proteins such as TRAP1 using enhanced sampling simulations.
• Cancer variant interpretation: Classification of variants in tumor suppressors and oncogenes to assess effects on S-nitrosylation propensity and structural population shifts.

Methodology:

High-throughput structural analyses of 4,172 S-nitrosylated proteins, enhanced sampling simulations, coarse-grained modeling of 44 targets, bioinformatics pipelines (https://github.com/ELELAB/SNO_investigation_pipelines), and variant classification into neutral/stabilizing/destabilizing categories.