De novo selection of oncogenes
- PMID: 24344264
- PMCID: PMC3890846
- DOI: 10.1073/pnas.1315298111
De novo selection of oncogenes
Abstract
All cellular proteins are derived from preexisting ones by natural selection. Because of the random nature of this process, many potentially useful protein structures never arose or were discarded during evolution. Here, we used a single round of genetic selection in mouse cells to isolate chemically simple, biologically active transmembrane proteins that do not contain any amino acid sequences from preexisting proteins. We screened a retroviral library expressing hundreds of thousands of proteins consisting of hydrophobic amino acids in random order to isolate four 29-aa proteins that induced focus formation in mouse and human fibroblasts and tumors in mice. These proteins share no amino acid sequences with known cellular or viral proteins, and the simplest of them contains only seven different amino acids. They transformed cells by forming a stable complex with the platelet-derived growth factor β receptor transmembrane domain and causing ligand-independent receptor activation. We term this approach de novo selection and suggest that it can be used to generate structures and activities not observed in nature, create prototypes for novel research reagents and therapeutics, and provide insight into cell biology, transmembrane protein-protein interactions, and possibly virus evolution and the origin of life.
Keywords: E5 protein; protein engineering; receptor tyrosine kinase; synthetic biology; traptamer.
Conflict of interest statement
Conflict of interest: Yale University has filed a provisional patent covering the construction and use of novel traptamers for various purposes.
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References
-
- Binz HK, Plückthun A. Engineered proteins as specific binding reagents. Curr Opin Biotechnol. 2005;16(4):459–469. - PubMed
-
- Hilvert D. Design of protein catalysts. Annu Rev Biochem. 2013;82:447–470. - PubMed
-
- Kries H, Blomberg R, Hilvert D. De novo enzymes by computational design. Curr Opin Chem Biol. 2013;17(2):221–228. - PubMed
-
- Yin H, et al. Computational design of peptides that target transmembrane helices. Science. 2007;315(5820):1817–1822. - PubMed
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