ArticlemAbs
Data mining patented antibody sequences.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 34 citations in OpenAlex.
- ASD: antigen-specific antibody database.mAbs · 2026Article
- Exploring the nanobody patent landscape: a focus on BCMA sequences and structural analysis.mAbs · 2025Article
- Comparative assessment of different anti-CD147/Basigin 2 antibodies as a potential therapeutic anticancer target by molecular modeling and dynamic simulation.Molecular diversity · 2025Article
- RIOT-Rapid Immunoglobulin Overview Tool-annotation of nucleotide and amino acid immunoglobulin sequences using an open germline database.Briefings in bioinformatics · 2024Article
- Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability.Communications biology · 2024Article
- Humanization of Pan-HLA-DR mAb 44H10 Hinges on Critical Residues in the Antibody Framework.Antibodies (Basel, Switzerland) · 2024Article
- Antibody design using deep learning: from sequence and structure design to affinity maturation.Briefings in bioinformatics · 2024Review
- LAP: Liability Antibody Profiler by sequence & structural mapping of natural and therapeutic antibodies.PLoS computational biology · 2024Article
- Building Representation Learning Models for Antibody Comprehension.Cold Spring Harbor perspectives in biology · 2024Review
- The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures.Nucleic acids research · 2024Article
- nanoBERT: a deep learning model for gene agnostic navigation of the nanobody mutational space.Bioinformatics advances · 2024Article
- Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly.Briefings in bioinformatics · 2023Article
- An in silico method to assess antibody fragment polyreactivity.Nature communications · 2022Article
- Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.Briefings in bioinformatics · 2022Review
- INDI-integrated nanobody database for immunoinformatics.Nucleic acids research · 2022Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The patent literature should reflect the past 30 years of engineering efforts directed toward developing monoclonal antibody therapeutics. Such information is potentially valuable for rational antibody design. Patents, however, are designed not to convey scientific knowledge, but to provide legal protection. It is not obvious whether antibody information from patent documents, such as antibody sequences, is useful in conveying engineering know-how, rather than as a 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 covering major jurisdictions (e.g., US Patent and Trademark Office (USPTO) and World Intellectual Property Organization) 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., 11% 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 the patent literature could serve as a reference for previous engineering efforts to improve rational antibody design.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.