A handle on mass coincidence errors inde novosequencing of antibodies by bottom-up proteomics

Abstract

Antibody sequences can be determined at 99% accuracy directly from the polypeptide product using bottom-up proteomics techniques. This circumvents the need to isolate the antibody-producing B-cell clone and enables reverse engineering of monoclonal antibodies from lost hybridoma cell lines, as well as the secreted protein in bodily fluid. Sequencing accuracy at the peptide level is limited by common mass coincidences of isobaric residues like leucine/isoleucine, but also by incomplete fragmentation spectra in which the order of two or more residues remains ambiguous due to lacking fragment ions for the intermediate positions. Likewise, different combinations of amino acids, of potentially different length, can also coincide to the same mass (e.g. GG=N, GA=Qetc.). Here we present several updates to Stitch (v1.5), which performs template-based assembly ofde novopeptide reads to reconstruct antibody sequences. This version introduces a mass-based alignment algorithm that explicitly accounts for mass coincidence errors. In addition, it incorporates a postprocessing procedure to assign I/L residues based on secondary fragments (satellite ions,i.e. w-ions). Moreover, evidence for sequence assignments can now be directly evaluated with the addition of an integrated spectrum viewer. This version of Stitch also allows input data from a wider selection ofde novopeptide sequencing algorithms, now including Casanovo, PEAKS, Novor.Cloud, pNovo, and MaxNovo, in addition to flat text and FASTA. Combined, these changes make Stitch compatible with a larger range of data processing pipelines and improve its tolerance to peptide-level sequencing errors.

Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry – a perspective

“…Similarly, from the benchmark dataset analyzed here we might learn what sequencing mistakes are common in ModelAngelo data. This could be used to adjust the conventional Smith-Waterman Alignment in Stitch accordingly, analogous to recent improvements in the alignment algorithm we implemented for MS data 57 .…”

“…Similarly, from the benchmark dataset analyzed here we might learn what sequencing mistakes are common in ModelAngelo data. This could be used to adjust the conventional Smith-Waterman Alignment in Stitch accordingly, analogous to recent improvements in the alignment algorithm we implemented for MS data 57 . Finally, the template matching step in Stitch is now solely based on the sequence of the ModelAngelo models, but we may expand this to a structure-based alignment to better place the error-prone sequence reads in the correct framework of the Ig-domains.…”

“…Here we explore the use of ModelAngelo to derive de novo antibody sequences from experimental cryoEM density maps of antibody-antigen complexes. We have previously developed the software tool Stitch, which sorts and assembles MS-derived peptide sequences into full heavy/light chain sequences across complex repertoires 56,57 . We adapted Stitch to perform the same task on ModelAngelo-derived de novo models and test the accuracy of the approach on a benchmark of 164 publicly available cryoEM maps of monoclonal antibody-antigen pairs.…”