SCV

SCV visualizes sequence coverage on predicted protein structures to convert proteomics results into 3D structural context for analysis of post-translational modifications and isotope labeling.

Key Features:

• 3D sequence coverage visualization: Visualizes peptide- and protein-level sequence coverage on predicted 3D structures to localize identified regions of proteins.
• Integration with DeepMind and Baker lab predictions: Uses protein structure predictions from DeepMind and the Baker lab, including coverage for the human proteome.
• Post-translational modification and isotope label mapping: Maps post-translational modifications and isotope-labeled peptides onto predicted structures for structural interpretation.
• Limited proteolysis integration: Integrates limited proteolysis data and visualizes digestion over time on structural models.
• Comparative validation against PDB: Compares predicted structures with Protein Data Bank (PDB) entries to support validation and refinement of models.
• Conversion of proteomics results into structural insights: Transforms proteomics result lists into structural context to enable downstream structural analysis.

Scientific Applications:

• PTM and isotope-label localization: Localizes post-translational modifications and isotope-labeled peptides in three-dimensional structure to inform functional hypotheses.
• Validation and refinement of structural predictions: Uses proteolysis and proteomics data to compare and refine predicted models against PDB entries.
• Structural interpretation of proteomics experiments: Provides 3D context for interpreting protein identification, coverage, and digestion patterns from proteomics workflows.
• Integration with limited proteolysis studies: Enables comparison of time-resolved digestion data with structural models to study conformational accessibility and protease sensitivity.

Methodology:

Leverages protein structure predictions from DeepMind and the Baker lab; maps peptide sequence coverage, post-translational modifications, and isotope labels onto predicted 3D structures; visualizes digestion over time from limited proteolysis data; and compares predicted models against Protein Data Bank (PDB) entries for validation and refinement.