Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Colin M Leaf, Pearl Qi, Richard W Roberts, Terry T Takahashi

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

4 authors.

Colin M Leaf *Department of Chemistry, University of Southern California, Los Angeles, CA, 90089, USA.

Pearl Qi *Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA.

Richard W RobertsDepartment of Chemistry, University of Southern California, Los Angeles, CA, 90089, USA. richrob@usc.edu.

Terry T TakahashiDepartment of Chemistry, University of Southern California, Los Angeles, CA, 90089, USA. tttakaha@usc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

mRNA display is a powerful in vitro selection technique that enables the discovery of peptide and protein ligands from libraries exceeding 10 trillion unique sequences. The technique allows for exquisite control over binding stringency and specificity, and it is highly amenable to high-throughput ligand profiling and integration with machine learning approaches. Here, we describe general methods for performing mRNA display selections.

Indexed as

Peptide LibraryPeptidesProteinsRNA, MessengerCombinatorial Chemistry TechniquesDirected Molecular EvolutionLigandsProtein EngineeringLigandsPeptide LibraryPeptidesProteinsRNA, MessengerCombinatorial chemistryDirected evolutionIn vitro selectionLigand discoverymRNA displayProtein engineeringRNA-protein fusion

Identifiers

PMID42400843

DOI10.1016/S0076-6879(83)96008-1

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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.