A Single Platform to Sequence All Monoclonal Antibody Proteins

With the sequence information, one can re-make the exact same antibody recombinantly, or perform additional engineering such as isotype switching, subtype switching, species switching and reformatting.

How big are the differences between different antibody protein species, subtypes and formats? What are the challenges to having a unified solution?
Antibody proteins are beautiful crafts from mother nature. Each and every antibody clone is unique, and therefore they all have their own unique sequences.

The differences between antibody protein sequences from difference species, even in those conserved framework regions, could be quite significant. Sequence motifs that frequently present in one species, may not be found in another species.

This difference will have a cascade effect on mass spec experiments. For example, an experiment protocol that works well for mouse antibodies may not work as well for hamster antibodies; or a protocol may work for one subtype but not the others, as some enzymes may not work as effectively.

This is one of the main challenges we have to overcome to design a unified solution.

What techniques have been proposed to address the antibody protein sequencing problem?
Over the years, several papers have been published to address the antibody protein sequencing problem. From the manual sequencing and assembly approach published 25 years ago, to the homology database assisted sequencing algorithms that can achieve over 90% accuracy, to the self-claimed automated full-length sequencing software released in recent years. But none of them have been widely adopted in the real world.

What challenges have these methodologies faced?
There are many challenges facing scientists when sequencing antibody proteins, both expected and unexpected.

One of the expected challenges here is the overfitting problem given the small and limited training dataset available publicly. All published works in the literature have trained their algorithms with only a few proteins. This overfits the algorithm on those few proteins, but the algorithm does not work well on new proteins. This is very likely the main reason why an algorithm works well on the original publication but works terribly in third party studies.

Some other expected challenges include, for example, heterogeneity, which increases the complexity of the sample. The experiments may be suboptimal, especially when the protocols are 'borrowed' from the general proteomics experiments. Some peptides just don't fly well and therefore not generating any signals.

In reality, there are also many unexpected challenges we have learned the hard way.
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