Template-Assisted De Novo Sequencing of SARS-CoV-2 and Influenza Monoclonal Antibodies by Mass Spectrometry.
Michelle V GadushGiuseppe A SauttoHamssika ChandrasekaranAlena BensussanTed M RossGregory C IppolitoMaria D PersonPublished in: Journal of proteome research (2022)
In this study, we used multiple enzyme digestions, coupled with higher-energy collisional dissociation (HCD) and electron-transfer/higher-energy collision dissociation (EThcD) fragmentation to develop a mass-spectrometric (MS) method for determining the complete protein sequence of monoclonal antibodies (mAbs). The method was refined on an mAb of a known sequence, a SARS-CoV-1 antireceptor binding domain (RBD) spike monoclonal antibody. The data were searched using Supernovo to generate a complete template-assisted de novo sequence for this and two SARS-CoV-2 mAbs of known sequences resulting in correct sequences for the variable regions and correct distinction of Ile and Leu residues. We then used the method on a set of 25 antihemagglutinin (HA) influenza antibodies of unknown sequences and determined high confidence sequences for >99% of the complementarity determining regions (CDRs). The heavy-chain and light-chain genes were cloned and transfected into cells for recombinant expression followed by affinity purification. The recombinant mAbs displayed binding curves matching the original mAbs with specificity to the HA influenza antigen. Our findings indicate that this methodology results in almost complete antibody sequence coverage with high confidence results for CDR regions on diverse mAb sequences.
Keyphrases
- sars cov
- monoclonal antibody
- electron transfer
- mass spectrometry
- respiratory syndrome coronavirus
- amino acid
- binding protein
- induced apoptosis
- genetic diversity
- poor prognosis
- healthcare
- capillary electrophoresis
- liquid chromatography
- multiple sclerosis
- high resolution
- cell cycle arrest
- gene expression
- single cell
- big data
- coronavirus disease
- oxidative stress
- machine learning
- high performance liquid chromatography
- endoplasmic reticulum stress
- bioinformatics analysis