henhaobio
Sign in

mAbsBioinformaticsPeer-reviewed journal article

Data mining patented antibody sequences

Konrad KrawczykiD, Andrew G. BuchananiD, Paolo MarcatiliiD

PDFDOIPreprint

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.

Citations
25
FWCI
2.77
Field percentile
top 9%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISAbanades '23Krawczyk '19Młokosiewic '22Rawat '25Bashour '24Shuai '21
69% semantic matchBioinformatics
Brennan Abanades, Tobias Hegelund Olsen, Matthew I. J. Raybould+5 more·Nucleic Acids Research·Oct 28, 2023· 46 citations