ArticlemAbs2016
ABodyBuilder: Automated antibody structure prediction with data-driven accuracy estimation.
Article in mAbs, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 153 papers.
What it found
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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
153 citing papers in PubMed.
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- A roadmap for systematic humanization of a chimeric antigen receptor: preclinical validation of a humanized CD22 scFv as a model.Experimental hematology · 2026Article
- Identification of a conformational epitope on the E antigen implicated in anti-E alloimmunization.Blood advances · 2026Article
- Paraplume: A fast and accurate antibody paratope prediction method provides insights into repertoire-scale binding dynamics.PLoS computational biology · 2026Article
- Molecular Modeling and Simulations of Biologics.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Benchmarking antibody modeling tools across structure prediction, docking, and paratope-epitope interface analysis.Bioinformatics advances · 2026Article
- Prediction of Protein Antigen Epitopes from Monoclonal Antibodies by In Silico Molecular Docking.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Human antibody targeting of coronavirus spike S2 subunit is associated with protection mediated by Fc effector functions.Journal of virology · 2025Article
- Explore antibody repertoire in the era of AI.Acta biochimica et biophysica Sinica · 2025Article
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- Article
- Review
- Technologies for Monoclonal Antibody Discovery and Development.International journal of molecular sciences · 2025Review
- Integrated antibody language model accelerates IgG screening and design for broad-spectrum antiviral therapy.iScience · 2025Article
- Assessing the structural boundaries of broadly reactive antibody interactions with diverse H3 influenza hemagglutinin proteins.Journal of virology · 2025Article
- High-throughput synthesis and specificity characterization of natively paired influenza hemagglutinin antibodies with oPoolScience translational medicine · 2025Article
- HIV broadly neutralizing antibody precursors to the Apex epitope induced in nonhuman primates.Science immunology · 2025Article
- Open-source antibodies as a path to enhanced research reproducibility and transparency.New biotechnology · 2025Article
- Optimization of synthetic human VProtein science : a publication of the Protein Society · 2025Article
- Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization.Journal of nanobiotechnology · 2025Review
93 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issuedCorrigendum.2018
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational modeling of antibody structures plays a critical role in therapeutic antibody design. Several antibody modeling pipelines exist, but no freely available methods currently model nanobodies, provide estimates of expected model accuracy, or highlight potential issues with the antibody's experimental development. Here, we describe our automated antibody modeling pipeline, ABodyBuilder, designed to overcome these issues. The algorithm itself follows the standard 4 steps of template selection, orientation prediction, complementarity-determining region (CDR) loop modeling, and side chain prediction. ABodyBuilder then annotates the 'confidence' of the model as a probability that a component of the antibody (e.g., CDRL3 loop) will be modeled within a root-mean square deviation threshold. It also flags structural motifs on the model that are known to cause issues during in vitro development. ABodyBuilder was tested on 4 separate datasets, including the 11 antibodies from the Antibody Modeling Assessment-II competition. ABodyBuilder builds models that are of similar quality to other methodologies, with sub-Angstrom predictions for the 'canonical' CDR loops. Its ability to model nanobodies, and rapidly generate models (∼30 seconds per model) widens its potential usage. ABodyBuilder can also help users in decision-making for the development of novel antibodies because it provides model confidence and potential sequence liabilities. ABodyBuilder is freely available at http://opig.stats.ox.ac.uk/webapps/abodybuilder .
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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.