ArticleBio-protocol2022
Protocol for High Throughput Screening of Antibody Phage Libraries.
Article in Bio-protocol, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed, 5 citations in OpenAlex.
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Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phage display is a proven and widely used technology for selecting specific antibodies against desired targets. However, an immense amount of effort is required to identify and screen the desired positive clones from large and diverse combinatorial libraries. On the other hand, the selection of positive binding clones from synthetic and semi-synthetic libraries has an inherent bias toward clones with randomly produced amber stop codons, making it more difficult to identify desirable binding antibodies. To overcome the screening of desired clones with amber codons, we present a step-by-step approach for effective phage library screening to isolate useful antibodies. The procedure calls for creating a simple new vector system for soluble production of phage ELISA positive binding clones with one or more amber stop codons in their single-chain antibody fragment (scFv) gene sequences, which is otherwise difficult in standard screening. Graphical abstract.
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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.