Current state, existing challenges, and promising progress for de novo sequencing and assembly of monoclonal antibodies

Abstract

Monoclonal antibodies (mAbs) are biotechnologically produced proteins with various applications in research, therapeutics, and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody-antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a useful method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. Deep learning-based approaches have been developed and applied more frequently to increase the accuracy of de novo sequencing. In this study, we evaluated five recently developed de novo sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, and PointNovo) in their ability to identify and assemble antibody sequences. The deep learning-based tools PointNovo and SMSNet showed an increased peptide recall across different enzymes and datasets compared to spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra, and peptides of various lengths. We achieved a sequence coverage of 93.15% to 99.07% on the light chains of three different antibody datasets using the de Bruijn assembler ALPS and the predictions from PointNovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies, and hybrid approaches such as homology search to achieve high accuracy on long protein sequences.