Fast LC-MS acquisition and automated data processing will help you speed up peptide mapping of your biotherapeutic, including critical disulfide bond and post-translational modification characterization. SCIEX helps you untangle the complexity of disulfide bonds, speeding up your characterization process.
- High-resolution, accurate mass MS, and MS/MS information from the SCIEX TripleTOF® 5600+ and 6600 Systems provide the data necessary to differentiate closely related species and confirm structural assignments.
- Confirm your molecule’s disulfide bond pattern more quickly using BioPharmaView™ software, which utilizes automated peak assignment and scoring.
- The combination of the TripleTOF Systems and BioPharmaView Software simplifies data processing and reporting, reducing overall processing time for accurate disulfide bond assignment.
Accurate disulfide bond mapping is essential for correctly establishing structure-function relationships as well as for monitoring the structural integrity of recombinant monoclonal antibodies (mAbs) throughout their production. Inappropriate disulfide bonds can affect a mAb’s stability, potency, aggregation, and may also signal errors in the cell culture or purification process. By following a biotherapeutic’s disulfide patterns over time, manufacturers can quickly detect production problems and then correct them as early as possible.
Correctly assigning disulfide bonds in a mAb, or ADC can be challenging and time-consuming due to the heterogeneity, large size, and multiple cysteine residues found in these biomolecules. Traditional approaches for disulfide mapping are based on fast liquid chromatography-mass spectrometry (LC-MS) analysis; however, these methods can be inefficient and usually involve digestion with multiple enzymes, tedious data processing, and intensive manual inspection of chromatograms for the identification of any possible disulfide linkages.
As the biotherapeutics industry develops and expands, there is an urgent need for software tools that can rapidly facilitate and accelerate the higher-order structural characterization of biopharmaceutical products. To meet these requirements, SCIEX has developed BioPharmaView™ Software, a data processing suite that can reduce the complexity of the massive data sets generated during biotherapeutic analysis. BioPharmaView Software uses rapid processing tools to accelerate critical characterization assays–such as peptide mapping and disulfide bond identification– by automating peak assignments, simplifying data processing, and streamlining the reporting process.
Peak Assignment Reduces Time for Peptide Mapping Experiments
To identify and match peptides, BioPharmaView Software automatically scores b- and y- ions from the high-resolution MS/MS spectra; and then the highest scoring experimental peaks are compared to a list of theoretical masses automatically generated by the software. The peak assignment process is further enhanced by predicting the theoretical fragment ion masses for non-reduced, disulfide-linked peptides before comparison with experimental data. Including other criteria in the ion selection process–such as MS/MS scoring, multiple charge states, and a retention time (RT) filter–can also help reduce the time needed for peptide mapping experiments. This enables manufacturers to meet regulatory requirements more quickly during the production and marketing of a new biotherapeutic product.
In this article, we successfully developed an efficient and automated workflow that comprehensively identified every disulfide linkage in the Fab region of an mAb. The use of the high-speed, TripleTOF® LC-MS System contributed to time-savings during disulfide analysis by permitting accurate mass MS and MS/MS information to be collected simultaneously, providing the high-resolution data necessary for differentiating closely related species and confirming structural assignments. And by using BioPharmaView Software to process the dataset, identifying the location of five disulfide linkages in the Fab region of an mAb was completed in a fast and automated fashion.