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bioRxiv · 10.1101/2025.05.28.656573

DoggifAI: a transformer based approach for antibodycaninisation

Grabarczyk, D.·Kocikowski, M.·Parys, M.·Houston, D.·Hupp, T.·Alfaro, J. A.·Cohen, S.
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Abstract

Antibody translation across species offers a compelling strategy to extend the vast and expensive investments in human therapeutic antibodies to veterinary oncology, with applications in both veterinary medicine and comparative oncology. While precise, low-immunogenic treatments are essential for canine cancer care, traditional species conversion methods rely on ad hoc bioinformatics modifications. These methods often implicitly decouple the framework (FR) and complementarity-determining regions (CDRs), ignoring how structural changes in FRs can affect the conformation and function of CDRs. This can compromise binding specificity and require costly high-throughput in vitro screening. To address this, we present DoggifAI, a transformer model that translates non-canine antibody sequences into canine ones by generating species-appropriate framework regions (FRs) based on desired CDRs. This allows the model to better preserve structural compatibility between FRs and CDRs. The model is pretrained in a T5-style text-to-text denoising task on a large multispecies antibody dataset, which allows further finetuning on a much smaller species-specific dataset. DoggifAI generates highly canine-like antibodies and shows promising results in preserving binding specificity. To support further progress in this field, we also release a curated dataset of over 430,000 unique canine antibody chain sequences, significantly expanding the public sequence repertoire. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/656573v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@13c10adorg.highwire.dtl.DTLVardef@6afca6org.highwire.dtl.DTLVardef@1f11961org.highwire.dtl.DTLVardef@1b9563a_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG HighlightsO_LIWe show that transformer models are capable of generating plausible antibody framework regions based on CDRs C_LIO_LIWe show that resulting framework regions are highly recognisable as coming from the desired species C_LIO_LIWe show promising results for the retention of binding specificity when translating antibody sequences in this way C_LIO_LIWe release a large, high-quality dataset of canine antibody sequences to support future research C_LI

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Grabarczyk, D., Kocikowski, M., Parys, M., Houston, D., Hupp, T., Alfaro, J. A., Cohen, S.. 2025-05-29. DoggifAI: a transformer based approach for antibodycaninisation. https://doi.org/10.1101/2025.05.28.656573

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