ArticleScience advances2022
Fragment-based computational design of antibodies targeting structured epitopes.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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.
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Who cites it
35 citing papers in PubMed, 49 citations in OpenAlex.
- Article
- Humanized Anti-PD-1 Antibodies Generated Using The Conditional Kernel-Elastic Autoencoder.bioRxiv : the preprint server for biology · 2026Article
- Multi-objective antibody design and optimization using machine learning.Nature reviews bioengineering · 2026Article
- CD98hc-targeted antibody shuttles for central nervous system delivery with broad cross-species reactivity.Nature biomedical engineering · 2026Article
- From atoms to fragments: a coarse representation for efficient and functional protein design.Bioinformatics (Oxford, England) · 2026Article
- Reprogrammed SimCells for antimicrobial therapy.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Confidence scoring for deep learning-predicted antibody-antigen complexes: AntiConf as a precision-driven metric.Briefings in bioinformatics · 2026Article
- Ab-SELDON: Leveraging Diversity Data for an Efficient Automated Computational Pipeline for Antibody Design.Journal of chemical information and modeling · 2026Article
- The landscape of antibody production systems: recombinant expression for research, diagnostics and therapy.Frontiers in bioengineering and biotechnology · 2026Review
- Nanobodies in biomedicine: from molecular characteristics to fabrication and clinical translation.Military Medical Research · 2026Review
- SurfDiff: protein surface profiling for selective or broadly reactive epitope prioritisation in binder and immunogen design.bioRxiv : the preprint server for biology · 2025Article
- De novo design of epitope-specific antibodies via a structure-driven computational workflow.Nature communications · 2025Article
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- A Pharmacophore-Based Method for Rapid and Accurate Virtual Screening of Antibody Libraries against Antigens.Molecular pharmaceutics · 2025Article
- NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores.Journal of cheminformatics · 2025Article
- Design of nanobody targeting SARS-CoV-2 spike glycoprotein using CDR-grafting assisted by molecular simulation and machine learning.PLoS computational biology · 2025Article
- Nanobody engineering: computational modelling and design for biomedical and therapeutic applications.FEBS open bio · 2025Review
- Computational electrostatic engineering of nanobodies for enhanced SARS-CoV-2 receptor binding domain recognition.Frontiers in molecular biosciences · 2025Article
- Applying computational protein design to therapeutic antibody discovery - current state and perspectives.Frontiers in immunology · 2025Review
- A comprehensive overview of recent advances in generative models for antibodies.Computational and structural biotechnology journal · 2024Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
Abstract
De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes.
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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.