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Ancylostoma caninum is an important zoonotic gastrointestinal nematode of dogs worldwide and a close relative of human hookworms. We recently reported that racing greyhound dogs in the USA are infected with A . caninum that are commonly resistant to multiple anthelmintics. Benzimidazole resistance in A . caninum in greyhounds was associated with a high frequency of the canonical F167Y(T T C>T A C) isotype-1 β-tubulin mutation. In this work, we show that benzimidazole resistance is remarkably widespread in A . caninum from domestic dogs across the USA. First, we identified and showed the functional significance of a novel benzimidazole isotype-1 β-tubulin resistance mutation, Q134H(CA A >CA T ). Several benzimidazole resistant A . caninum isolates from greyhounds with a low frequency of the F167Y(T T C>T A C) mutation had a high frequency of a Q134H(CA A >CA T ) mutation not previously reported from any eukaryotic pathogen in the field. Structural modeling predicted that the Q134 residue is directly involved in benzimidazole drug binding and that the 134H substitution would significantly reduce binding affinity. Introduction of the Q134H substitution into the C . elegans β-tubulin gene ben-1 , by CRISPR-Cas9 editing, conferred similar levels of resistance as a ben-1 null allele. Deep amplicon sequencing on A . caninum eggs from 685 hookworm positive pet dog fecal samples revealed that both mutations were widespread across the USA, with prevalences of 49.7% (overall mean frequency 54.0%) and 31.1% (overall mean frequency 16.4%) for F167Y(T T C>T A C) and Q134H(CA A >CA T ), respectively. Canonical codon 198 and 200 benzimidazole resistance mutations were absent. The F167Y(T T C>T A C) mutation had a significantly higher prevalence and frequency in Western USA than in other regions, which we hypothesize is due to differences in refugia. This work has important implications for companion animal parasite control and the potential emergence of drug resistance in human hookworms.

In this Perspective, the authors advance a view of macromolecules as collections ofinterchanging structural ensembles, and discuss how a synergistic combination of NMR,X-ray crystallography, and computational simulations can reveal the structural basis for conformational dynamics at atomic resolution. Biomolecules adopt a dynamic ensemble of conformations, each with the potential to interact with binding partners or perform the chemical reactions required for a multitude of cellular functions. Recent advances in X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy and other techniques are helping us realize the dream of seeing—in atomic detail—how different parts of biomolecules shift between functional substates using concerted motions. Integrative structural biology has advanced our understanding of the formation of large macromolecular complexes and how their components interact in assemblies by leveraging data from many low-resolution methods. Here, we review the growing opportunities for integrative, dynamic structural biology at the atomic scale, contending there is increasing synergistic potential between X-ray crystallography, NMR and computer simulations to reveal a structural basis for protein conformational dynamics at high resolution.

The fit of atomic models of protein structures to high-resolution cryo-electron microscopy maps can be assessed with a validation tool, EMRinger. Advances in high-resolution cryo-electron microscopy (cryo-EM) require the development of validation metrics to independently assess map quality and model geometry. We report EMRinger, a tool that assesses the precise fitting of an atomic model into the map during refinement and shows how radiation damage alters scattering from negatively charged amino acids. EMRinger ( https://github.com/fraser-lab/EMRinger ) will be useful for monitoring progress in resolving and modeling high-resolution features in cryo-EM.

The PLUMED consortium unifies developers and contributors to PLUMED, an open-source library for enhanced-sampling, free-energy calculations and the analysis of molecular dynamics simulations. Here, we outline our efforts to promote transparency and reproducibility by disseminating protocols for enhanced-sampling molecular simulations.

Insertions and deletions (indels) enable evolution and cause disease. Due to technical challenges, indels are left out of most mutational scans, limiting our understanding of them in disease, biology, and evolution. We develop a low cost and bias method, DIMPLE, for systematically generating deletions, insertions, and missense mutations in genes, which we test on a range of targets, including Kir2.1. We use DIMPLE to study how indels impact potassium channel structure, disease, and evolution. We find deletions are most disruptive overall, beta sheets are most sensitive to indels, and flexible loops are sensitive to deletions yet tolerate insertions.

Modern protein crystal structures are based nearly exclusively on X-ray data collected at cryogenic temperatures (generally 100 K). The cooling process is thought to introduce little bias in the functional interpretation of structural results, because cryogenic temperatures minimally perturb the overall protein backbone fold. In contrast, here we show that flash cooling biases previously hidden structural ensembles in protein crystals. By analyzing available data for 30 different proteins using new computational tools for electron-density sampling, model refinement, and molecular packing analysis, we found that crystal cryocooling remodels the conformational distributions of more than 35% of side chains and eliminates packing defects necessary for functional motions. In the signaling switch protein, H-Ras, an allosteric network consistent with fluctuations detected in solution by NMR was uncovered in the room-temperature, but not the cryogenic, electron-density maps. These results expose a bias in structural databases toward smaller, overpacked, and unrealistically unique models. Monitoring room-temperature conformational ensembles by X-ray crystallography can reveal motions crucial for catalysis, ligand binding, and allosteric regulation.

Cryo-EM has revealed the structures of many challenging yet exciting macromolecular assemblies at near-atomic resolution (3–4.5Å), providing biological phenomena with molecular descriptions. However, at these resolutions, accurately positioning individual atoms remains challenging and error-prone. Manually refining thousands of amino acids – typical in a macromolecular assembly – is tedious and time-consuming. We present an automated method that can improve the atomic details in models that are manually built in near-atomic-resolution cryo-EM maps. Applying the method to three systems recently solved by cryo-EM, we are able to improve model geometry while maintaining the fit-to-density. Backbone placement errors are automatically detected and corrected, and the refinement shows a large radius of convergence. The results demonstrate that the method is amenable to structures with symmetry, of very large size, and containing RNA as well as covalently bound ligands. The method should streamline the cryo-EM structure determination process, providing accurate and unbiased atomic structure interpretation of such maps.

The creation of artificial enzymes is a key objective of computational protein design. Although de novo enzymes have been successfully designed, these exhibit low catalytic efficiencies, requiring directed evolution to improve activity. Here, we use room-temperature X-ray crystallography to study changes in the conformational ensemble during evolution of the designed Kemp eliminase HG3 ( k cat / K M 146 M −1 s −1 ). We observe that catalytic residues are increasingly rigidified, the active site becomes better pre-organized, and its entrance is widened. Based on these observations, we engineer HG4, an efficient biocatalyst ( k cat / K M 103,000 M −1 s −1 ) containing key first and second-shell mutations found during evolution. HG4 structures reveal that its active site is pre-organized and rigidified for efficient catalysis. Our results show how directed evolution circumvents challenges inherent to enzyme design by shifting conformational ensembles to favor catalytically-productive sub-states, and suggest improvements to the design methodology that incorporate ensemble modeling of crystallographic data. Kemp eliminases are artificial enzymes that catalyze the concerted deprotonation and ring-opening of benzisoxazoles. Here, the authors use room-temperature X-ray crystallography to investigate changes to the conformational ensemble of the Kemp eliminase HG3 along a directed evolutionary trajectory, and develop an experimentally guided, ensemble-based computational enzyme design procedure.

Rational design and directed evolution have proved to be successful approaches to increase catalytic efficiencies of both natural and artificial enzymes. Protein dynamics is recognized as important, but due to the inherent flexibility of biological macromolecules it is often difficult to distinguish which conformational changes are directly related to function. Here, we use directed evolution on an impaired mutant of the proline isomerase CypA and identify two second-shell mutations that partially restore its catalytic activity. We show both kinetically, using NMR spectroscopy, and structurally, by room-temperature X-ray crystallography, how local perturbations propagate through a large allosteric network to facilitate conformational dynamics. The increased catalysis selected for in the evolutionary screen is correlated with an accelerated interconversion between the two catalytically essential conformational sub-states, which are both captured in the high-resolution X-ray ensembles. Our data provide a glimpse of an evolutionary trajectory and show how subtle changes can fine-tune enzyme function. A key challenge in the field of protein design and evolution is to understand the mechanisms by which directed evolution is improving enzymes. Here the authors combine different biophysical methods and give mechanistic insights into how directed evolution increases the catalytic efficiency of human peptidyl-prolyl cis / trans isomerase CypA.

While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent-exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decreases and relative hydrophobicity increases. Across a series of 13 inhibitor-bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from nuclear magnetic resonance studies suggesting that residual side-chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding. Proteins are the workhorses of our cells. They are large molecules that ‘fold’ into specific, often highly complex, three-dimensional configurations. These structures are not static, but rather dynamic and flexible. In other words, proteins can shift between different three-dimensional shapes to perform their tasks within the cell. To perform their roles, many proteins have to bind to small molecule ligands. Many ligands are drugs, which means that their effectiveness depends on their ability to bind to and impact the proteins involved in the disease they are treating. When a ligand binds to a protein, it can reshape the protein. For example, certain conformations of the protein, which were difficult for the protein to be in on its own, may become more stable when the ligand binds. Additionally, upon ligand binding, some parts of the protein may move relative to each other. Previous studies have shown that these movements can affect the interaction between ligand and protein. However, these studies only examined a small number of proteins. Therefore, Wankowicz et al. set out to determine, in greater detail, what happens to protein flexibility upon ligand binding. First, a pipeline was created to model alternative configurations of the protein both with and without ligands attached. These models measured flexibility within protein structures. The models revealed that when ligands bind to proteins, the flexibility of different regions of the protein changes – and does so in a consistent way. Proteins that become more rigid in the region interacting with their ligands become less rigid in other, distant regions, and vice versa. In other words, the rest of the protein is able to compensate for any changes in flexibility caused by ligand binding, which may contribute to how well a ligand binds to a protein. This study demonstrates the ability of ligands to affect the entire structure of the proteins they bind to, and therefore sheds new light on the role of proteins’ innate conformational flexibility during this process. These results will contribute to our understanding of how the ligands and proteins involved in different cellular processes interact with each other – and, potentially, how these interactions can be manipulated.