The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
In Vitro Selection of Highly Modified Cyclic Peptides That Act as Tight Binding Inhibitors
Open accessAbstract
There is a great demand for the discovery of new therapeutic molecules that combine the high specificity and affinity of biologic drugs with the bioavailability and lower cost of small molecules. Small, natural-product-like peptides hold great promise in bridging this gap; however, access to libraries of these compounds has been a limitation. Since ribosomal peptides may be subjected to in vitro selection techniques, the generation of extremely large libraries (>10(13)) of highly modified macrocyclic peptides may provide a powerful alternative for the generation and selection of new useful bioactive molecules. Moreover, the incorporation of many non-proteinogenic amino acids into ribosomal peptides in conjunction with macrocyclization should enhance the drug-like features of these libraries. Here we show that mRNA-display, a technique that allows the in vitro selection of peptides, can be applied to the evolution of macrocyclic peptides that contain a majority of unnatural amino acids. We describe the isolation and characterization of two such unnatural cyclic peptides that bind the protease thrombin with low nanomolar affinity, and we show that the unnatural residues in these peptides are essential for the observed high-affinity binding. We demonstrate that the selected peptides are tight-binding inhibitors of thrombin, with K(i)(app) values in the low nanomolar range. The ability to evolve highly modified macrocyclic peptides in the laboratory is the first crucial step toward the facile generation of useful molecular reagents and therapeutic lead molecules that combine the advantageous features of biologics with those of small-molecule drugs.
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Cites
Effect of pegylation on pharmaceuticals
Harris, J. M., & Chess, R. B. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1033 · Cited by 3,431
Hopkins, A. L., & Groom, C. R. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd892 · Cited by 3,305
How were new medicines discovered?
Swinney, D. C., & Anthony, J. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3480 · Cited by 1,812
Activation of Apoptosis in Vivo by a Hydrocarbon-Stapled BH3 Helix
Walensky, L. D., Kung, A. L., Escher, I., Malia, T. J., Barbuto, S., Wright, R. D., Wagner, G., Verdine, G. L., & Korsmeyer, S. J. (2004) · Science · doi:10.1126/science.1099191 · Cited by 1,349
Direct inhibition of the NOTCH transcription factor complex
Moellering, R. E., Cornejo, M., Davis, T. N., Bianco, C. D., Aster, J. C., Blacklow, S. C., Kung, A. L., Gilliland, D. G., Verdine, G. L., & Bradner, J. E. (2009) · Nature · doi:10.1038/nature08543 · Cited by 752
Encoding multiple unnatural amino acids via evolution of a quadruplet-decoding ribosome
Neumann, H., Wang, K., Davis, L., Garcia-Alai, M., & Chin, J. W. (2010) · Nature · doi:10.1038/nature08817 · Cited by 622
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Therapeutic Peptides: Current Applications and Future Directions
Wang, L., Wang, N., Zhang, W., Cheng, X., Yan, Z., Shao, G., Wang, X., Wang, R., & Fu, C. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00904-4 · Cited by 2,131
The Current State of Peptide Drug Discovery: Back to the Future?
Henninot, A., Collins, J. C., & Nuss, J. M. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00318 · Cited by 1,113
Prebiotic Systems Chemistry: New Perspectives for the Origins of Life
Ruiz-Mirazo, K., Briones, C., & de la Escosura, A. (2014) · Chemical Reviews · doi:10.1021/cr2004844 · Cited by 930
Macrocyclic Peptides as Drug Candidates: Recent Progress and Remaining Challenges
Vinogradov, A. A., Yin, Y., & Suga, H. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.8b13178 · Cited by 848
Nonproteinogenic Amino Acid Building Blocks for Nonribosomal Peptide and Hybrid Polyketide Scaffolds
Walsh, C. T., O'Brien, R. V., & Khosla, C. (2013) · Angewandte Chemie International Edition · doi:10.1002/anie.201208344 · Cited by 406
Metadata
- Authors
- Guillen Schlippe, Y. V., Hartman, M. C. T., Josephson, K., & Szostak, J. W.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-03-29
- Volume
- 134
- Issue
- 25
- Pages
- 10469-10477
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W1968375523
- Cited by
- 239
- References
- 6
- FWCI
- 9.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
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