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Bio-panning is a common process involved in recombinant antibody selection against defined targets. The biopanning process aims to isolate specific antibodies against an antigen via affinity selection from a phage display library. In general, antigens are immobilized on solid surfaces such as polystyrene plastic, magnetic beads, and nitrocellulose. For high-throughput selection, semi-automated panning selection allows simul-taneous panning against multiple target antigens adapting automated particle processing systems such as the KingFisher Flex. The system setup allows for minimal human intervention for pre-and post-panning steps such as antigen immobilization, phage rescue, and amplification. In addition, the platform is also adaptable to perform polyclonal and monoclonal ELISA for the evaluation process. This chapter will detail the protocols involved from the selection stage until the monoclonal ELISA evaluation with important notes attached at the end of this chapter for optimization and troubleshooting purposes.
Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly
(2023)
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.