ArticleBioinformatics advances2024
nanoBERT: a deep learning model for gene agnostic navigation of the nanobody mutational space.
Article in Bioinformatics advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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
15 citing papers in PubMed.
- AVIDbase: A biologically accurate structural dataset of nanobody-antigen complexes.Protein science : a publication of the Protein Society · 2026Article
- Evidence-aware comparison of sequence-centric machine learning for antibody discovery and optimization.Briefings in bioinformatics · 2026Article
- Adaptive Disorder as the Hallmark of Nanobodies Antigen-Binding Loops.Journal of chemical information and modeling · 2026Article
- Targeting angiogenesis: advances in the design and engineered applications of nanobodies.Frontiers in immunology · 2026Review
- Targeting vascular inflammation and endothelial dysfunction with nanobodies: emerging therapeutic strategies for cardiovascular disease.Frontiers in immunology · 2026Review
- Review
- Article
- NanoDeNovo: De Novo Design of Anti-Poliovirus I Sabin Strain Nanobodies by Semi-Automated Computational Pipeline.International journal of molecular sciences · 2025Article
- Next-Generation Therapeutic Antibodies for Cancer Treatment: Advancements, Applications, and Challenges.Molecular biotechnology · 2025Review
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization.Journal of nanobiotechnology · 2025Review
- Nanobody engineering: computational modelling and design for biomedical and therapeutic applications.FEBS open bio · 2025Review
- Antibody design using deep learning: from sequence and structure design to affinity maturation.Briefings in bioinformatics · 2024Review
- Distinct types of VHHs in Alpaca.Frontiers in immunology · 2024Article
- Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Nanobodies are a subclass of immunoglobulins, whose binding site consists of only one peptide chain, bestowing favorable biophysical properties. Recently, the first nanobody therapy was approved, paving the way for further clinical applications of this antibody format. Further development of nanobody-based therapeutics could be streamlined by computational methods. One of such methods is infilling-positional prediction of biologically feasible mutations in nanobodies. Being able to identify possible positional substitutions based on sequence context, facilitates functional design of such molecules. Results: Here we present nanoBERT, a nanobody-specific transformer to predict amino acids in a given position in a query sequence. We demonstrate the need to develop such machine-learning based protocol as opposed to gene-specific positional statistics since appropriate genetic reference is not available. We benchmark nanoBERT with respect to human-based language models and ESM-2, demonstrating the benefit for domain-specific language models. We also demonstrate the benefit of employing nanobody-specific predictions for fine-tuning on experimentally measured thermostability dataset. We hope that nanoBERT will help engineers in a range of predictive tasks for designing therapeutic nanobodies. Availability and implementation: https://huggingface.co/NaturalAntibody/.
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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.