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Highlights of Coronavirus Structural Studies

9 Sep

Omicron SARS-CoV-2 mutations stabilize spike up-RBD conformation and lead to a non-RBM-binding monoclonal antibody escape (Nature Communications)

Omicron SARS-CoV-2 is rapidly spreading worldwide. To delineate the impact of emerging mutations on spike's properties, we performed systematic structural analyses on apo Omicron spike and its complexes with human ACE2 or S309 neutralizing antibody (NAb) by cryo-EM. The Omicron spike preferentially adopts the one-RBD-up conformation both before and after ACE2 binding, which is in sharp contrast to the orchestrated conformational changes to create more up-RBDs upon ACE2 binding as observed in the prototype and other four variants of concern (VOCs). Furthermore, we found that S371L, S373P and S375F substitutions enhance the stability of the one-RBD-up conformation to prevent exposing more up-RBDs triggered by ACE2 binding. The increased stability of the one-RBD-up conformation restricts the accessibility of S304 NAb, which targets a cryptic epitope in the closed conformation, thus facilitating the immune evasion by Omicron. These results expand our understanding of Omicron spike's conformation, receptor binding and antibody evasion mechanism.

The SARS-CoV-2 Omicron variant spreads rapidly. Here the authors show that Omicron S preferentially adopts the one-RBD-up conformation, which leads to a non-RBM-binding monoclonal antibody escape. Mutagenesis reveals that S371L, S373P and S375F substitutions enhance the conformational stability.

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Reader's Corner Archive

6 Apr

Imaging active site chemistry and protonation states: NMR crystallography of the tryptophan synthase alpha-aminoacrylate intermediate (PNAS)

NMR-assisted crystallography—the integrated application of solid-state NMR, X-ray crystallography, and first-principles computational chemistry—holds significant promise for mechanistic enzymology: by providing atomic-resolution characterization of stable intermediates in enzyme active sites, including hydrogen atom locations and tautomeric equilibria, NMR crystallography offers insight into both structure and chemical dynamics. Here, this integrated approach is used to characterize the tryptophan synthase α-aminoacrylate intermediate, a defining species for pyridoxal-5′-phosphate–dependent enzymes that catalyze β-elimination and replacement reactions. For this intermediate, NMR-assisted crystallography is able to identify the protonation states of the ionizable sites on the cofactor, substrate, and catalytic side chains as well as the location and orientation of crystallographic waters within the active site. Most notable is the water molecule immediately adjacent to the substrate β-carbon, which serves as a hydrogen bond donor to the ε-amino group of the acid–base catalytic residue βLys87. From this analysis, a detailed three-dimensional picture of structure and reactivity emerges, highlighting the fate of the L-serine hydroxyl leaving group and the reaction pathway back to the preceding transition state. Reaction of the α-aminoacrylate intermediate with benzimidazole, an isostere of the natural substrate indole, shows benzimidazole bound in the active site and poised for, but unable to initiate, the subsequent bond formation step. When modeled into the benzimidazole position, indole is positioned with C3 in contact with the α-aminoacrylate Cβ and aligned for nucleophilic attack. Here, the chemically detailed, three-dimensional structure from NMR-assisted crystallography is key to understanding why benzimidazole does not react, while indole does.

6 Apr

Structure determination of high-energy states in a dynamic protein ensemble (Nature)

Macromolecular function frequently requires that proteins change conformation into high-energy states. However, methods for solving the structures of these functionally essential, lowly populated states are lacking. Here Dorothee Kern at.al. develop a method for high-resolution structure determination of minorly populated states by coupling NMR spectroscopy-derived pseudocontact shifts (PCSs) with Carr–Purcell–Meiboom–Gill (CPMG) relaxation dispersion (PCS–CPMG). Their approach additionally defines the corresponding kinetics and thermodynamics of high-energy excursions, thereby characterizing the entire free-energy landscape. Using a large set of simulated data for adenylate kinase (Adk), calmodulin and Src kinase, they find that high-energy PCSs accurately determine high-energy structures (with a root mean squared deviation of less than 3.5 angström). Applying their methodology to Adk during catalysis, they find that the high-energy excursion involves surprisingly small openings of the AMP and ATP lids. This previously unresolved high-energy structure solves a longstanding controversy about conformational interconversions that are rate-limiting for catalysis. Primed for either substrate binding or product release, the high-energy structure of Adk suggests a two-step mechanism combining conformational selection to this state, followed by an induced-fit step into a fully closed state for catalysis of the phosphoryl-transfer reaction. Unlike other methods for resolving high-energy states, such as cryo-electron microscopy and X-ray crystallography, their solution PCS–CPMG approach excels in cases involving domain rearrangements of smaller systems (less than 60 kDa) and populations as low as 0.5%, and enables the simultaneous determination of protein structure, kinetics and thermodynamics while proteins perform their function.

6 Jan

De novo protein design by deep network hallucination (Nature)

There has been considerable recent progress in protein structure prediction using deep neural networks to predict inter-residue distances from amino acid sequences. Here we investigate whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occurring proteins used in training the models. We generate random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting residue–residue distance maps, which, as expected, are quite featureless. We then carry out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (Kullback–Leibler divergence) between the inter-residue distance distributions predicted by the network and background distributions averaged over all proteins. Optimization from different random starting points resulted in novel proteins spanning a wide range of sequences and predicted structures. We obtained synthetic genes encoding 129 of the network-‘hallucinated’ sequences, and expressed and purified the proteins in Escherichia coli; 27 of the proteins yielded monodisperse species with circular dichroism spectra consistent with the hallucinated structures. We determined the three-dimensional structures of three of the hallucinated proteins, two by X-ray crystallography and one by NMR, and these closely matched the hallucinated models. Thus, deep networks trained to predict native protein structures from their sequences can be inverted to design new proteins, and such networks and methods should contribute alongside traditional physics-based models to the de novo design of proteins with new functions.

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