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• W4313644936 abstract "Advances in genetic code reprogramming have allowed the site-specific incorporation of noncanonical functionalities into polypeptides and proteins, providing access to wide swaths of chemical space via in vitro translation techniques like mRNA display. Prior efforts have established that the translation machinery can tolerate amino acids with modifications to both the peptide backbone and side chains, greatly broadening the chemical space that can be interrogated in ligand discovery efforts. However, existing methods for confirming the translation yield of new amino acid building blocks for these technologies necessitate multistep workups and, more importantly, are not relevant for measuring translation within the context of a combinatorial library consisting of multiple noncanonical amino acids. In this study, we developed a luminescence-based assay to rapidly assess the relative translation yield of any noncanonical amino acid in real time. Among the 59 amino acids tested here, we found that many translate with high efficiency, but translational yield is not necessarily correlated to whether the amino acid is proteinogenic or has high tRNA acylation efficiency. Interestingly, we found that single-template translation data can inform the library-scale translation yield and that shorter peptide libraries are more tolerant of lower-efficiency amino acid monomers. Together our data show that the luminescence-based assay described herein is an essential tool in evaluating new building blocks and codon table designs within mRNA display toward the goal of developing druglike peptide-based libraries for drug discovery campaigns." @default.
• W4313644936 created "2023-01-07" @default.
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• W4313644936 date "2023-01-06" @default.
• W4313644936 modified "2023-10-01" @default.
• W4313644936 title "Evaluating Translational Efficiency of Noncanonical Amino Acids to Inform the Design of Druglike Peptide Libraries" @default.
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• W4313644936 doi "https://doi.org/10.1021/acschembio.2c00712" @default.
• W4313644936 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36607609" @default.
• W4313644936 hasPublicationYear "2023" @default.
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