Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly

peer-reviewed · Briefings in Bioinformatics · 2023

peer-reviewed · Briefings in Bioinformatics · 2023. Denis Beslic et al. Monoclonal antibodies are biotechnologically produced proteins with various applications in research…
Date 2023-01-19
Type peer-reviewed
Venue Briefings in Bioinformatics
Publisher Oxford University Press (OUP)
Contribution benchmark
DOI 10.1093/bib/bbac542
Citations (OpenAlex) 68
Venue 2-year citedness 7.18

Preprint version: Current state, existing challenges, and promising progress for de novo sequencing and assembly of monoclonal antibodies (2022-07-22, bioRxiv)

Abstract

Monoclonal antibodies 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 valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with 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 97.69-99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. 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.

Authors

  1. Denis Beslic · Robert Koch Institute
  2. Georg Tscheuschner · Federal Institute for Materials Research and Testing (BAM)
  3. Bernhard Y. Renard · Hasso Plattner Institute, Robert Koch Institute
  4. Michael G. Weller · Federal Institute for Materials Research and Testing (BAM)
  5. Thilo Muth · Federal Institute for Materials Research and Testing (BAM), Max Planck Institute for Dynamics of Complex Technical Systems, Robert Koch Institute, glyXera GmbH

Methods and tools

Data deposited

  • Supplementary Data for “Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly” (as deposited) · 10.6084/m9.figshare.21394143

Data used

  • Anti-FLAG-M2 antibody sequencing (as deposited) · PXD023419
  • Monoclonal antibody de novo assembly (as deposited) · MSV000079801

Cites (32)

Cited by (42)

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