Automatic Protein Disulfide Bond Mapping of a Monoclonal Antibody Using the Agilent Accurate-Mass Q-TOF LC/MS Platform and BioConfirm Software Algorithm

Importance of the Topic

Monoclonal antibodies are among the most significant biopharmaceutical agents used in therapeutic and diagnostic applications. The correct pairing of disulfide bonds is essential for maintaining their higher‐order structure, stability, and biological activity. Inadequate or incorrect disulfide linkages can lead to misfolding and reduced efficacy, making comprehensive mapping of these bonds vital for drug quality assessment and development.

Study Objectives and Overview

This study aims to establish a robust, automated liquid chromatography–mass spectrometry (LC–MS/MS) workflow for the precise mapping of disulfide bonds in a monoclonal antibody (Herceptin). Key goals include confirmation of native disulfide pairings, detection of scrambled linkages under stress conditions, and generation of confidence metrics based on MS and MS/MS data.

Used Instrumentation

• Agilent 1290 Infinity II UHPLC system
• Agilent 6545 Accurate-Mass Q-TOF mass spectrometer
• Agilent MassHunter BioConfirm B.08.00 software

Methodology

Herceptin samples were desalted, denatured with 8 M urea, then divided into reduced (treated with DTT and iodoacetamide) and non-reduced fractions. Both fractions underwent proteolysis using trypsin/Lys-C. Peptide separation employed a reverse-phase AdvanceBio Peptide Mapping column (2.1 × 150 mm, 2.7 µm) with a 50-minute gradient from 0 to 100% acetonitrile containing 0.1% formic acid. MS data were acquired in positive ion mode, with MS1 scans from 100–1,700 m/z and MS/MS scans from 50–1,700 m/z. The BioConfirm software applied a Molecular Feature Extractor algorithm to detect peptide features, assign Quality Scores (0–100), and calculate Bio Scores based on MS and MS/MS matching metrics.

Main Results and Discussion

The automated workflow identified all 16 canonical disulfide bonds in the Herceptin heavy and light chains with high Quality and Bio Scores. Comparative analysis of reduced versus non-reduced digests revealed larger, intact disulfide-linked peptides exclusively in the non-reduced sample, as visualized by mirror plots of total ion chromatograms. The software algorithm generated theoretical peptide lists, matched observed features, and discriminated native versus scrambled linkages by score thresholds, streamlining a task traditionally performed manually.

Benefits and Practical Applications

• Rapid, high-confidence mapping of disulfide bonds without manual inspection
• Automated detection of mispaired or scrambled linkages
• Enhanced quality control processes for therapeutic antibody production
• Applicability to stress testing and formulation optimization

Future Trends and Potential Applications

Advances in high-resolution mass spectrometry and machine learning–driven data analysis will further improve disulfide mapping accuracy and throughput. Integration of automated LC–MS workflows into biomanufacturing quality control platforms will enable real-time monitoring of protein folding integrity. Expanding software capabilities to handle complex protein conjugates and multi-disulfide systems will broaden applications in biosimilar development and protein engineering.

Conclusions

The presented LC–MS/MS workflow, combining Agilent UHPLC-Q-TOF instrumentation with MassHunter BioConfirm software, provides a reliable, automated solution for comprehensive disulfide bond mapping in monoclonal antibodies. This approach enhances confidence in structural characterization and supports critical quality attributes during biopharmaceutical development.

References

1. Harris RJ. Heterogeneity of recombinant antibodies: linking structure to function. Dev Biol (Basel). 2005;122:117–127.
2. Liu H, May K. Disulfide bond structures of IgG molecules: structural variations, chemical modifications and possible impacts to stability and biological function. mAbs. 2012;4(1):17–23.