Back

Comprehensive assembly of monoclonal and mixed antibody sequences

Jiang, W.; Xiong, Y.; Xiao, J.; Wang, J.; Jiang, Z.; Luo, L.; Yuan, Q.; Xia, N.; Yu, R.

2024-08-10 bioinformatics
10.1101/2024.08.09.607415 bioRxiv
Show abstract

The elucidation of antibody sequence information is crucial for understanding antigen binding and advancing therapeutic and research applications. However, complete de novo assembly of monoclonal antibody sequences remains challenging due to accuracy and robustness limitations. To address this issue, we introduce Fusion, an innovative de novo assembler that integrates overlapping peptides and template information into complete sequences using a beam search strategy. We demonstrate Fusions performance by reconstructing multiple human and murine antibodies with highest accuracy (100% and over 99%, respectively). Biological validation of the recombinantly expressed AFS98 antibody with unknown sequences further supports its effectiveness. Furthermore, current methods are applicable only to traditional monoclonal antibody sequencing assembly, presenting a significant bottleneck in achieving higher throughput. In contrast, Fusion can assemble peptide sequences from mixtures of two or three monoclonal antibodies into complete individual sequences with the same accuracy as traditional sequencing, significantly enhancing throughput. To our knowledge, this is the first study enabling high-throughput sequencing of multiple antibodies using only bottom-up mass spectrometry. The duration, expense, and reagent consumption of mass spectrometry detection are comparable to those required for sequencing a single monoclonal antibody. In summary, Fusions superior performance in handling the complex antibody sequencing represents a significant advancement in antibody research.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.