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

By Enrique Marcos, T. M. Chidyausiku, Andrew C. McShan, T. Evangelidis, Santrupti Nerli, Lauren P. Carter, L. Nivón, Audrey Davis, G. Oberdorfer, K. Tripsianes and 2 more. 2018

107 citations recorded by the source corpus.

β-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-β-sheet proteins from first principles lags far behind the design of all-α or mixed-αβ domains owing to their non-local nature and the tendency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have identified a series of structural relationships between loop geometry, side chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to de novo design jellyroll structures with double-stranded β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable design closely matched the computational model, demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the design of a broad range of non-local β-sheet protein structures. Baker, Marcos and colleagues analyze β-arches (loops connecting unpaired β-strands) and derive rules used for de novo design of a hyperthermostable jellyroll structure, with eight antiparallel β-strands forming double-stranded β-helices.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020, methodology): “…in de novo designed loops, the ConsensusLoopDesign task operation accessed through Rosetta Scripts allows a user to restrict the amino acid identities of loops based on sequence profiles of naturally occurring loops with the same region of backbone dihedral angle space (Ramachandran bins) 12,13 .”
De novo protein design, a retrospective (2020, methodology): “Moreover, repeat proteins (discussed below), mini-proteins, and cyclic peptides including ones with D-amino acids or unnatural crosslinks have been prepared and structurally verified (Bhardwaj et al., 2016; Chevalier et al., 2017; Dang et al., 2017; Marcos et al., 2018) (Fig.”
ProtGPT2 is a deep unsupervised language model for protein design (2022, background): “The network contains 59,612 vertices and 427,378 edges, comprising 1847 components or ‘ island-like ’ clusters (Fig.”
Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design (2021, background): “, 2017) performed as a part of evaluation, as is a staple of the most advanced current protein design approaches (Marcos et al., 2018; Vorobieva et al., 2021).”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “While robust protocols exist for designing helical bundles and small, idealized proteins with certain alpha-beta fold topologies (30, 58, 64), the success rates for other proteins such as beta barrels can be low (29, 31, 34).”
De novo protein design by inversion of the AlphaFold structure prediction network (2022, background): “…structural protein fragments into the desired fold which has proven successful in the design of de novo beta barrels (Dou et al., 2018), TIM bar-rels (Huang et al., 2016), jellyroll structures (Marcos et al., 2018), and various alpha-beta proteins (Correia et al., 2014; Koga et al., 2012).”
Fully differentiable full-atom protein backbone generation (2019, methodology): “In this application, the generator thus provides orderings of secondary structure elements encoded in local pairwise distance constraints – a non-trivial task in backbone design (26; 27) – while Rosetta is used to improve the rough global positioning of each secondary structure element.”