A 3.8 Å resolution cryo-EM structure of a small protein bound to an imaging scaffold

Liu, Yuxi; Huynh, Duc T.; Yeates, Todd O.

doi:10.1038/s41467-019-09836-0

Cited by 97 publications

(120 citation statements)

References 40 publications

(49 reference statements)

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“…An additional carbohydrate-binding module (CBM32, not resolved in the electron density (ED) map) is connected to EngBF via a helical bundle whose C-terminus faces the large solvent-filled channel 13 . We replaced the CBM32 domain, after residue 1520 of EngBF, with different target-binding domains through rigid shared-helix fusions, similar to the design of various crystallisation chaperones 14,15 and electron microscopy aids 16,17 .…”

Section: Resultsmentioning

confidence: 99%

Structural analysis of biological targets by host:guest crystal lattice engineering

Erñst

Plückthun

Mittl

2019

Sci Rep

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To overcome the laborious identification of crystallisation conditions for protein X-ray crystallography, we developed a method where the examined protein is immobilised as a guest molecule in a universal host lattice. We applied crystal engineering to create a generic crystalline host lattice under reproducible, predefined conditions and analysed the structures of target guest molecules of different size, namely two 15-mer peptides and green fluorescent protein (sfGFP). A fusion protein with an N-terminal endo-α-N-acetylgalactosaminidase (EngBF) domain and a C-terminal designed ankyrin repeat protein (DARPin) domain establishes the crystal lattice. The target is recruited into the host lattice, always in the same crystal form, through binding to the DARPin. The target structures can be determined rapidly from difference Fourier maps, whose quality depends on the size of the target and the orientation of the DARPin.

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Section: Resultsmentioning

confidence: 99%

Structural analysis of biological targets by host:guest crystal lattice engineering

Erñst

Plückthun

Mittl

2019

Sci Rep

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“…Importantly, only a single new interface needs to be designed to induce self-assembly into a cage. Exciting applications of this technology include de novo cages that package RNA, similar to viral capsids 139 , ordered presentation of small proteins for structure determination by cryo-EM 140,141 and display of viral antigens in nanoparticle vaccines 142 ( Fig. 4c).…”

Section: Nature Reviews | Molecular Cell Biologymentioning

confidence: 99%

Advances in protein structure prediction and design

Kuhlman

Bradley

2019

Nat Rev Mol Cell Biol

652

433

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The stunning diversity of molecular functions performed by naturally evolved proteins is made possible by their finely tuned three-dimensional structures, which are in turn determined by their genetically encoded amino acid sequences. A predictive understanding of the relationship between amino acid sequence and protein structure would therefore open up new avenues, both for the prediction of function from genome sequence data and also for the rational engineering of novel protein functions through the design of amino acid sequences with specific structures. The past decade has seen dramatic improvements in our ability to predict and design the three-dimensional structures of proteins, with potentially far-reaching implications for medicine and our understanding of biology. New machine-learning algorithms have been developed that analyse the patterns of correlated mutations in protein families, to predict structurally interacting residues from sequence information alone 1,2. Improved protein energy functions 3,4 have for the first time made it possible to start with an approximate structure prediction model and move it closer to the experimentally determined structure by an energy-guided refinement process 5,6. Advances in protein conformational sampling and sequence optimization have permitted the design of novel protein structures and complexes 7,8 , some of which show promise as therapeutics 9. These advances in protein structure prediction and design have been fuelled by technological breakthroughs as well as by a rapid growth in biological databases. Protein-modelling algorithms (Box 1) are computationally demanding both to develop and to apply. The rapid increase in computing power available to researchers (both CPU-based and, increasingly, GPU-based computing power) facilitates rapid benchmarking of new algorithms and enables their application to larger molecules and molecular assemblies. At the same time, next-generation sequencing has fuelled a dramatic increase in protein sequence databases as genomic and metagenomic sequencing efforts have expanded 10. Advances in software and automation have increased the pace of experimental structure determination, speeding the growth of the database of experimentally determined protein structures (the Protein Data Bank (PDB)) 11 , which now contains close to 150,000 macromolecular structures. Deep-learning algorithms 12 that have revolutionized image processing and speech recognition are now being adopted by protein modellers seeking to take advantage of these expanded sequence and structural databases. In this Review, we highlight a selection of recent breakthroughs that these technological advances have enabled. We describe current approaches to the prediction and design of protein structures, focusing primarily on template-free methods that do not require an

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“…Recent advances have been made by adapting an alpha helical fusion approach, developed earlier in the area of protein design 59 , 60 , to achieve more rigid connections between components 61 – 63 . The use of a modular adaptor system based on DARPins, as introduced by Liu et al 62 , has allowed the display and cryo-EM visualization of cargo proteins bound non-covalently to scaffolds built from symmetric protein assemblies 63 , 64 . Specific loop sequence mutations in the DARPin (or other adaptor protein) required to bind a given cargo protein can be identified experimentally based on separate laboratory evolution studies (see 65 for a recent review of DAPRin applications).…”

Section: Developments In Single-particle Cryo-electron Microscopymentioning

confidence: 99%

Advances in methods for atomic resolution macromolecular structure determination

2020

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Recent technical advances have dramatically increased the power and scope of structural biology. New developments in high-resolution cryo-electron microscopy, serial X-ray crystallography, and electron diffraction have been especially transformative. Here we highlight some of the latest advances and current challenges at the frontiers of atomic resolution methods for elucidating the structures and dynamical properties of macromolecules and their complexes.

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A 3.8 Å resolution cryo-EM structure of a small protein bound to an imaging scaffold

Cited by 97 publications

References 40 publications

Structural analysis of biological targets by host:guest crystal lattice engineering

Structural analysis of biological targets by host:guest crystal lattice engineering

Advances in protein structure prediction and design

Advances in methods for atomic resolution macromolecular structure determination

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