UniDec

UniDec performs automated deconvolution, peak detection, and batch processing of intact-protein mass spectrometry (native and denatured) data to quantify and characterize biotherapeutics such as bispecific antibodies and antibody–drug conjugates.


Key Features:

  • Fast processing: Handles large, batched intact mass spectrometry datasets to enable rapid analysis.
  • Deconvolution and peak detection: Implements deconvolution and peak detection algorithms for accurate mass assignment and quantitation of complex spectra.
  • Spreadsheet-driven batch processing: Iterates through a user-defined spreadsheet listing data files and deconvolution and quantitation parameters to perform systematic analysis across multiple datasets.
  • Automated reporting: Produces results in spreadsheet format and generates HTML reports summarizing deconvolution, peak detection, and quantitation outputs.
  • Customization and extensibility: Allows modification and extension of workflows and calculations to implement customized analyses.

Scientific Applications:

  • Bispecific antibody analysis: Measures correct pairing percentage in bispecific antibody datasets for assessment of product heterogeneity and assembly.
  • Antibody-drug conjugate (ADC) evaluation: Measures drug-to-antibody ratios (DAR) to quantify conjugation stoichiometry in ADCs.
  • Intact protein mass spectrometry characterization: Supports characterization and quantitation of intact proteins from native and denatured MS workflows.

Methodology:

Processes a user-defined spreadsheet listing data files and analysis parameters, and applies deconvolution and peak detection across datasets to generate quantitative outputs and reports.

Topics

Details

Cost:
Free of charge
Tool Type:
workflow
Programming Languages:
Python, C
Added:
1/10/2024
Last Updated:
1/10/2024

Operations

Data Inputs & Outputs

Publications

Phung W, Bakalarski CE, Hinkle TB, Sandoval W, Marty MT. UniDec Processing Pipeline for Rapid Analysis of Biotherapeutic Mass Spectrometry Data. Analytical Chemistry. 2023;95(30):11491-11498. doi:10.1021/acs.analchem.3c02010. PMID:37478487.

PMID: 37478487
Funding: - Division of Chemistry: CHE-1845230

Documentation

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