De novo design of a non-local beta-sheet protein with high stability and accuracy

• Authors: MARCOS E.; CHIDYAUSIKU T.M.; MCSHAN A.C.; EVANGELIDIS Thomas; NERLI S.; CARTER L.; NIVON L.G.; DAVIS A.; OBERDORFER G.; TRIPSIANES Konstantinos; SGOURAKIS N.G.; BAKER D.
• Year of publication: 2018
• Type: Article in Periodical
• Magazine / Source: NATURE STRUCTURAL & MOLECULAR BIOLOGY
• MU Faculty or unit: Central European Institute of Technology
• Doi: http://dx.doi.org/10.1038/s41594-018-0141-6
• Keywords: STRUCTURE PREDICTION; COMPUTATIONAL DESIGN; SECONDARY STRUCTURE; SOLENOID PROTEINS; SANDWICH PROTEIN; NEGATIVE DESIGN; CHEMICAL-SHIFTS; NMR; ROSETTA; VALIDATION
• Description: beta-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein design. Despite this potential, de novo design of all-beta-sheet proteins from first principles lags far behind the design of all-alpha or mixed-alpha beta domains owing to their non-local nature and the tendency of exposed beta-strand edges to aggregate. Through study of loops connecting unpaired beta-strands (beta-arches), we have identified a series of structural relationships between loop geometry, side chain directionality and beta-strand length that arise from hydrogen bonding and packing constraints on regular beta-sheet structures. We use these rules to de novo design jellyroll structures with double-stranded beta-helices formed by eight antiparallel beta-strands. The nuclear magnetic resonance structure of a hyperthermostable design closely matched the computational model, demonstrating accurate control over the beta-sheet structure and loop geometry. Our results open the door to the design of a broad range of non-local beta-sheet protein structures.

Related projects

• CEITEC 2020
• Pushing the limits in automated NMR structure determination using a single 4D NOESY spectrum and machine learning methods