Principles for designing proteins with cavities formed by curved β-sheets

By Enrique Marcos, B. Basanta, T. M. Chidyausiku, Yuefeng Tang, G. Oberdorfer, Gaohua Liu, G. Swapna, R. Guan, Daniel‐Adriano Silva, Jiayi Dou and 6 more. 2017

130 citations recorded by the source corpus.

Designing proteins with cavities In de novo protein design, creating custom-tailored binding sites is a particular challenge because these sites often involve nonideal backbone structures. For example, curved b sheets are a common ligand binding motif. Marcos et al. investigated the principles that drive β-sheet curvature by studying the geometry of β sheets in natural proteins and folding simulations. In a step toward custom design of enzyme catalysts, they used these principles to control β-sheet geometry and design proteins with differently shaped cavities. Science, this issue p. 201 Understanding the principles that control β-sheet geometry allows design of proteins with cavities. Active sites and ligand-binding cavities in native proteins are often formed by curved β sheets, and the ability to control β-sheet curvature would allow design of binding proteins with cavities customized to specific ligands. Toward this end, we investigated the mechanisms controlling β-sheet curvature by studying the geometry of β sheets in naturally occurring protein structures and folding simulations. The principles emerging from this analysis were used to design, de novo, a series of proteins with curved β sheets topped with α helices. Nuclear magnetic resonance and crystal structures of the designs closely match the computational models, showing that β-sheet curvature can be controlled with atomic-level accuracy. Our approach enables the design of proteins with cavities and provides a route to custom design ligand-binding and catalytic sites.

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): “…design a variety of protein folds with curved sheets (Figure 3A), creating pockets suitable for tailoring ligand-binding and enzymatic active sites 12 . fold_from_loops This approach has been used for antibodies and for vaccine design 96 using the fold_from_loops application, where the functional…”
De novo protein design, a retrospective (2020, methodology): “Thus, in modern approaches to protein design, fragments from the PDB are clustered based on 3D similarity and used in assembly procedures for protein structure prediction and design (Leaver-Fay et al., 2011; Marcos et al., 2017).”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “Proteins with curved β -sheets need bulges and register shifts to enable de ﬁ ned β -sheet cur-vatures (62).”
De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN) (2019, methodology): “In particular, the (energy minimum) principle-driven Rosetta tools (Leaver-Fay et al. , 2011) have made great success for de novo protein design (Marcos et al. , 2017; Shen et al. , 2018; Dou et al. , 2018; Silva et al. , 2019).”
Essentials of de novo protein design: Methods and applications (2018, methodology): “can help to quickly and inexpensively predict those designs that are less likely to achieve near-native conformational sampling in standard ab initio folding simulations and therefore should be discarded.(37) Biased folding simulations differ from standard ab initio in that the fragments used are a ”
Protein sequence design with a learned potential (2020, background): “Computational protein design has emerged as powerful tool for studying the conformational space of proteins, and has allowed us to expand the space of known protein folds [1, 2, 3, 4, 5, 6], create variations on existing topologies [7, 8, 9, 10, 11], and access the expansive space of sequences yet t”