ReviewJournal of nanobiotechnology2025
Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization.
Review in Journal of nanobiotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Antigen-Detected NMR for Minimal Epitope Engineering and Structure-Guided Selection of a NaAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Hypervariable loop profiling decodes sequence determinants of antibody stability.Nature structural & molecular biology · 2026Article
- Camelid-Derived Nanobodies Targeting Human Epidermal Growth Factor Receptor: Screening, Expression, and Functional Validation.Antibodies (Basel, Switzerland) · 2026Article
- Nanobodies targeting the Epstein-Barr virus EBNA1 DNA binding domain inhibit tumor growth.Journal of nanobiotechnology · 2026Article
- Nanobodies in biomedicine: from molecular characteristics to fabrication and clinical translation.Military Medical Research · 2026Review
- Targeting angiogenesis: advances in the design and engineered applications of nanobodies.Frontiers in immunology · 2026Review
- Review
- Review
- Antibody-oligonucleotide conjugates for spatial proteomics: principles, applications, and challenges.Acta biochimica et biophysica Sinica · 2025Article
- NanoDeNovo: De Novo Design of Anti-Poliovirus I Sabin Strain Nanobodies by Semi-Automated Computational Pipeline.International journal of molecular sciences · 2025Article
- Targeting CD155 in lung adenocarcinoma: A5 nanobody-based therapeutics for precision treatment and enhanced drug delivery.Signal transduction and targeted therapy · 2025Article
- Bispecific Antibodies, Nanobodies and Extracellular Vesicles: Present and Future to Cancer Target Therapy.Biomolecules · 2025Review
- Isobutyrate Confers Resistance to Inflammatory Bowel Disease through Host-Microbiota Interactions in Pigs.Research (Washington, D.C.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Nanobodies (Nbs), miniature antibodies consisting solely of the variable region of heavy chains, exhibit unique properties such as small size, high stability, and strong specificity, making them highly promising for disease diagnosis and treatment. The engineering production of Nbs has evolved into a mature process, involving library construction, screening, and expression purification. Different library types, including immune, naïve, and synthetic/semi-synthetic libraries, offer diverse options for various applications, while display platforms like phage display, cell surface display, and non-surface display provide efficient screening of target Nbs. Recent advancements in artificial intelligence (AI) have opened new avenues in Nb engineering. AI's exceptional performance in protein structure prediction and molecular interaction simulation has introduced novel perspectives and tools for Nb design and optimization. Integrating AI with traditional experimental methods is anticipated to enhance the efficiency and precision of Nb development, expediting the transition from basic research to clinical applications. This review comprehensively examines the latest progress in Nb engineering, emphasizing library construction strategies, display platform technologies, and AI applications. It evaluates the strengths and weaknesses of various libraries and display platforms and explores the potential and challenges of AI in predicting Nb structure, antigen-antibody interactions, and optimizing physicochemical properties.
Indexed as
Identifiers
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.