Mass Spec Studio

Mass Spec Studio performs vendor-agnostic extraction, processing, and integrative analysis of mass spectrometry data to support protein structural modeling and proteomics.

Key Features:

• Integration with Protein Modeling: Combines label-based MS and tandem MS (MS²) data with HADDOCK biomolecular docking to derive structural models from multiple biophysical datasets.
• Support for Diverse Labeling Chemistries: Supports hydrogen/deuterium exchange (HX), covalent labeling, targeted HX-MS², and data-independent HX-MS² acquisition strategies for modeling complex systems such as microtubule interactions.
• Crosslinking Detection: Provides a crosslink (XL) detection plug-in that uses peptide library reduction via tandem-MS data presearch to identify crosslinked peptides with high sensitivity and low false positive rates and to feed results into HADDOCK.
• Covalent Labeling Analysis: Implements the MSS-CLEAN plug-in to compute labeling yields, resolve amino-acid-level structural features, and address digestion-induced biases and site-localization ambiguities using redundant labeling from overlapping peptides.
• Normalization and Quantification: Implements CLEAN normalization routines to minimize over-reporting of labeling sites and to determine relative protection factors for quantitative structural interpretation.
• Extensible, vendor-agnostic processing: Offers an extensible framework supporting vendor-agnostic MS data formats and plug-ins for additional analytical techniques.

Scientific Applications:

• Structural biology: Generation of detailed structural models by integrating crosslinking, HX, covalent labeling, and MS² data with modeling tools like HADDOCK.
• Proteomics: Analysis of protein-protein interactions and large multiprotein systems, exemplified by studies of porcine transferrin–TbpB and microtubule interactions.
• Protein interaction modeling: Refinement of biomolecular docking and interaction interfaces through combined biophysical MS datasets.

Methodology:

Extraction of MS data followed by computational analysis linking label-based MS and tandem-MS (MS²) data to protein interaction models (HADDOCK); peptide library reduction via tandem-MS data presearch for crosslink identification; application of MSS-CLEAN and CLEAN normalization/quantification routines for covalent labeling analysis.