De novo design of a fluorescence-activating β-barrel

By Jiayi Dou, A. Vorobieva, W. Sheffler, Lindsey Doyle, Hahnbeom Park, M. J. Bick, Binchen Mao, G. W. Foight, Min Yen Lee, Lauren A. Gagnon and 8 more. 2018

312 citations recorded by the source corpus.

The regular arrangements of β-strands around a central axis in β-barrels and of α-helices in coiled coils contrast with the irregular tertiary structures of most globular proteins, and have fascinated structural biologists since they were first discovered. Simple parametric models have been used to design a wide range of α-helical coiled-coil structures, but to date there has been no success with β-barrels. Here we show that accurate de novo design of β-barrels requires considerable symmetry-breaking to achieve continuous hydrogen-bond connectivity and eliminate backbone strain. We then build ensembles of β-barrel backbone models with cavity shapes that match the fluorogenic compound DFHBI, and use a hierarchical grid-based search method to simultaneously optimize the rigid-body placement of DFHBI in these cavities and the identities of the surrounding amino acids to achieve high shape and chemical complementarity. The designs have high structural accuracy and bind and fluorescently activate DFHBI in vitro and in Escherichia coli, yeast and mammalian cells. This de novo design of small-molecule binding activity, using backbones custom-built to bind the ligand, should enable the design of increasingly sophisticated ligand-binding proteins, sensors and catalysts that are not limited by the backbone geometries available in known protein structures. The elucidation of general principles for designing β-barrels enables the de novo creation of fluorescent proteins.

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Improved protein structure prediction using predicted interresidue orientations (2019, positive): “We collected a set of 18 de novo-designed proteins of various topologies (24 – 26) ( α , β , and α / β ) with co-ordinates in the PDB and a set of 79 natural proteins of similar size selected from the CAMEO set and ran the trRosetta protocol to predict interresidue geometries (Fig.”
Protein sequence design by conformational landscape optimization (2021, methodology): “Backbones were constructed using blueprints (29) with distance and angle constraints to guide the formation of β-sheet backbone hydrogen bonds (30), followed by sequence design with Rosetta [FastDesign (28, 31) in conjunction with LayerDesign (21) and using the Rosetta all-atom energy function (32) ”
Identifying well-folded de novo proteins in the new era of accurate structure prediction (2022, methodology): “For the second dataset (“Monomer dataset”), we have curated a selection of 518 de novo proteins from several studies and spanning a wide range of folds (α, β, and mixed α/β) that were designed to be stable monomers without function (Kuhlman et al., 2003; Koga et al., 2012; Brunette et al., 2015; Doy”
Machine learning in protein engineering (2018, background): “This is an important advance, and useful when a single stable structure dictates function (Dou et al., 2018).”
Assessing and enhancing foldability in designed proteins (2021, background): “Indeed, the significance of misfolding has served as the primary motivation for studies of the design principles of protein folds [2,26,27] and for developing general methods to improve designed proteins’ foldability and stability [11,13,27,28].”
De novo protein design by inversion of the AlphaFold structure prediction network (2022, methodology): “This method assembles structural protein fragments into the desired fold which has proven successful in the design of de novo beta barrels (Dou et al., 2018), TIM barrels (Huang et al.”
De novo protein design, a retrospective (2020, background): “The design of uniquely folded β-proteins continued to be challenging, and accurate design of such tertiary structures was achieved only in the last two years (Dou et al., 2018).”