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Data mining patented antibody sequences

Konrad Krawczyk, Andrew Buchanan, Paolo Marcatili
doi: https://doi.org/10.1101/2020.11.26.389866
Konrad Krawczyk
1NaturalAntibody, Hamburg, Germany
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  • For correspondence: konrad@naturalantibody.com
Andrew Buchanan
2AstraZeneca, Cambridge, United Kingdom
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Paolo Marcatili
3Technical University of Denmark, Lyngby, Denmark
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Abstract

Patent literature should be a reflection of thirty years of engineering efforts in developing monoclonal antibody therapeutics. Such information is potentially valuable for rational antibody design. Patents however are not designed to convey scientific knowledge, but rather legal protection. It is unclear whether antibody information from patent documents, such as antibody sequences could be useful for the therapeutic antibody sphere in conveying engineering know-how rather than act as legal reference only. To assess the utility of patent data for therapeutic antibody engineering, we quantified the amount of antibody sequences in patents destined for medicinal purposes and how well they reflect the primary sequences of therapeutic antibodies in clinical use. We identified 16,526 patent families from major jurisdictions (e.g. USPTO and WIPO) that contained antibody sequences. These families held 245,109 unique antibody chains (135,397 heavy chains and 109,712 light chains) that we compiled in our Patented Antibody Database (PAD, http://naturalantibody.com/pad). We find that antibodies make up a non-trivial proportion of all patent amino acid sequence depositions (e.g. 10.95% of USPTO Full Text database). Our analysis of the 16,526 families demonstrates that the volume of patent documents with antibody sequences is growing with the majority of documents classified as containing antibodies for medicinal purposes. We further studied the 245,109 antibody chains from patent literature to reveal that they very well reflect the primary sequences of antibody therapeutics in clinical use. This suggests that patent literature could serve as a reference of previous engineering efforts to improve rational antibody design.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • http://naturalantibody.com/pad

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 03, 2020.
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Data mining patented antibody sequences
Konrad Krawczyk, Andrew Buchanan, Paolo Marcatili
bioRxiv 2020.11.26.389866; doi: https://doi.org/10.1101/2020.11.26.389866
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Data mining patented antibody sequences
Konrad Krawczyk, Andrew Buchanan, Paolo Marcatili
bioRxiv 2020.11.26.389866; doi: https://doi.org/10.1101/2020.11.26.389866

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