Title |
Accurate de novo design of hyperstable constrained peptides
|
---|---|
Published in |
Nature, September 2016
|
DOI | 10.1038/nature19791 |
Pubmed ID | |
Authors |
Gaurav Bhardwaj, Vikram Khipple Mulligan, Christopher D. Bahl, Jason M. Gilmore, Peta J. Harvey, Olivier Cheneval, Garry W. Buchko, Surya V. S. R. K. Pulavarti, Quentin Kaas, Alexander Eletsky, Po-Ssu Huang, William A. Johnsen, Per Jr Greisen, Gabriel J. Rocklin, Yifan Song, Thomas W. Linsky, Andrew Watkins, Stephen A. Rettie, Xianzhong Xu, Lauren P. Carter, Richard Bonneau, James M. Olson, Evangelos Coutsias, Colin E. Correnti, Thomas Szyperski, David J. Craik, David Baker |
Abstract |
Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes that have evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small-molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for accurate de novo design of conformationally restricted peptides, and the use of these methods to design 18-47 residue, disulfide-crosslinked peptides, a subset of which are heterochiral and/or N-C backbone-cyclized. Both genetically encodable and non-canonical peptides are exceptionally stable to thermal and chemical denaturation, and 12 experimentally determined X-ray and NMR structures are nearly identical to the computational design models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 25 | 25% |
United Kingdom | 9 | 9% |
Italy | 3 | 3% |
Spain | 3 | 3% |
India | 3 | 3% |
France | 3 | 3% |
Switzerland | 2 | 2% |
Australia | 1 | 1% |
Singapore | 1 | 1% |
Other | 15 | 15% |
Unknown | 35 | 35% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 69 | 69% |
Scientists | 27 | 27% |
Science communicators (journalists, bloggers, editors) | 3 | 3% |
Practitioners (doctors, other healthcare professionals) | 1 | 1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 1% |
United Kingdom | 5 | <1% |
Japan | 2 | <1% |
Italy | 1 | <1% |
Brazil | 1 | <1% |
France | 1 | <1% |
Switzerland | 1 | <1% |
Germany | 1 | <1% |
India | 1 | <1% |
Other | 1 | <1% |
Unknown | 803 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 237 | 29% |
Researcher | 161 | 19% |
Student > Bachelor | 82 | 10% |
Student > Master | 79 | 10% |
Student > Doctoral Student | 37 | 4% |
Other | 113 | 14% |
Unknown | 117 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 202 | 24% |
Chemistry | 187 | 23% |
Agricultural and Biological Sciences | 136 | 16% |
Computer Science | 33 | 4% |
Engineering | 30 | 4% |
Other | 99 | 12% |
Unknown | 139 | 17% |