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High thermodynamic stability of parametrically designed helical bundles

Journal Article · · Science
 [1];  [2];  [3];  [4];  [5];  [6];  [3];  [5];  [4];  [3]
  1. Univ. of Washington, Seattle, WA (United States); DOE Office of Scientific and Technical Information (OSTI)
  2. Univ. of Washington, Seattle, WA (United States); Univ. of Graz, Graz (Austria)
  3. Univ. of Washington, Seattle, WA (United States)
  4. Univ. of Cambridge, Cambridge (United Kingdom)
  5. Howard Hughes Medical Institute, Ashburn, VA (United States)
  6. Univ. of Cambridge, Cambridge (United Kingdom); Univ. of Tokyo, Tokyo (Japan)
Here we describe a procedure for designing proteins with backbones produced by varying the parameters in the Crick coiled coil–generating equations. Combinatorial design calculations identify low-energy sequences for alternative helix supercoil arrangements, and the helices in the lowest-energy arrangements are connected by loop building. We design an antiparallel monomeric untwisted three-helix bundle with 80-residue helices, an antiparallel monomeric right-handed four-helix bundle, and a pentameric parallel left-handed five-helix bundle. The designed proteins are extremely stable (extrapolated ΔGfold > 60 kilocalories per mole), and their crystal structures are close to those of the design models with nearly identical core packing between the helices. The approach enables the custom design of hyperstable proteins with fine-tuned geometries for a wide range of applications.
Research Organization:
Univ. of Washington, Seattle, WA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1344926
Journal Information:
Science, Journal Name: Science Journal Issue: 6208 Vol. 346; ISSN 0036-8075
Publisher:
AAASCopyright Statement
Country of Publication:
United States
Language:
English

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RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative adversarial neural network journal January 2020
Design of structurally distinct proteins using strategies inspired by evolution text January 2016
Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico text January 2019

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