ReviewCurrent research in pharmacology and drug discovery2024
Development of Recombinant Antibody by Yeast Surface Display Technology.
Review in Current research in pharmacology and drug discovery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- ADAPT-M: a workflow for rapid, quantitative in vitro measurements of enriched protein libraries.Nature communications · 2026Article
- The evolution of display technologies for antibody drug discovery.Trends in biotechnology · 2026Review
- The Production and Purification of Therapeutic Antibodies: A Comprehensive Analysis of Process- and Product-Related Contaminants.Biomolecules · 2026Review
- Antibody display technologies from phages to cells: translational bottlenecks and AI-enabled opportunities.Frontiers in bioengineering and biotechnology · 2026Review
- Protein Engineering and Drug Discovery: Importance, Methodologies, Challenges, and Prospects.Biomolecules · 2025Review
- ADAPT-M: A workflow for rapid, quantitativebioRxiv : the preprint server for biology · 2025Article
- Surface display of aspartate ammonia-lyase from Lactobacillus paracasei on Pichia pastoris.World journal of microbiology & biotechnology · 2025Article
- Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization.Journal of nanobiotechnology · 2025Review
- Optimized single-cell gates for yeast display screening.Protein engineering, design & selection : PEDS · 2025Article
- A yeast surface display platform for screening of non-enzymatic protein secretion in Kluyveromyces lactis.Applied microbiology and biotechnology · 2024Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recombinant antibodies have emerged as powerful tools in various fields, including therapeutics, diagnostics, and research applications. The selection of high-affinity antibodies with desired specificity is a crucial step in the development of recombinant antibody-based products. In recent years, yeast surface display technology has gained significant attention as a robust and versatile platform for antibody selection. This graphical review provides an overview of the yeast surface display technology and its applications in recombinant antibody selection. We discuss the key components involved in the construction of yeast surface display libraries, including the antibody gene libraries, yeast host strains, and display vectors. Furthermore, we highlight the strategies employed for affinity maturation and optimization of recombinant antibodies using yeast surface display. Finally, we discuss the advantages and limitations of this technology compared to other antibody selection methods. Overall, yeast surface display technology offers a powerful and efficient approach for the selection of recombinant antibodies, enabling the rapid generation of high-affinity antibodies for various applications.
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.