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To assess the potential benefits of molecular-dynamics (MD) simulations for macromolecular crystallography (MX), we performed room-temperature X-ray diffraction studies of the catalytic subunit of mouse protein kinase A (PKA-C). We then performed crystalline MD simulations of PKA-C, computed simulated electron densities from the water, protein, and ion components of the MD simulations, and carefully compared them to the initial crystal structure. The results led to the development of an MD-MX analysis procedure and several associated methods: 1) density comparison to evaluate consistency between the MD and the initial crystal structure model; 2) water building to generate alternative solvent models; and 3) protein remodeling to improve the crystal structure where interpretation of density is unclear. This procedure produced a revised structure of PKA with a new ordered water model and a modified protein structure. The revisions yield new insights into PKA mechanisms, including: a sensitivity of the His294 conformation to protonation state, with potential consequences for regulation of substrate binding; a remodeling of the Lys217 side chain along with a bound phosphate; an alternative conformation for Lys213 associated with binding to the regulatory subunit; and an alternative conformation for catalytic base Asp166 and nearby waters, suggesting a mechanism of progression of the phosphotransfer reaction via changes in Mg2+ coordination. Based on the benefits seen applying these methods to PKA, we recommend incorporating our MD-MX procedure into MX studies, to decide among ambiguous interpretations of electron density that occur, inevitably, as part of standard model refinement.

In their recent commentary in Protein Science, Jaskolski et al. analyzed three randomly picked diffraction data sets from fragment-screening group depositions from the PDB and, based on that, they claimed that such data are principally problematic. We demonstrate here that if such data are treated properly, none of the proclaimed criticisms persist.

Chemoenzymatic techniques have been applied extensively to pharmaceutical development, most effectively when routine synthetic methods fail. The regioselective and stereoselective construction of structurally complex glycans is an elegant application of this approach, that is seldom applied to positron emission tomography (PET) tracers. We sought a method to dimerize 2-deoxy-[18F]-fluoro-D-glucose ([18F]FDG), the most common tracer used in clinical imaging, to form [18F]-labeled disaccharides for detecting microorganismsin vivobased on their bacteria-specific glycan incorporation. When [18F]FDG was reacted with β-D-glucose-1-phosphate in the presence of maltose phosphorylase, both the α-1,4 and α-1,3-linked products 2-deoxy-[18F]-fluoro-maltose ([18F]FDM) and 2-deoxy-2-[18F]-fluoro-sakebiose ([18F]FSK) were obtained. This method was further extended with the use of trehalose (α,α-1,1), laminaribiose (β-1,3), and cellobiose (β-1,4) phosphorylases to synthesize 2-deoxy-2-[18F]fluoro-trehalose ([18F]FDT), 2-deoxy-2-[18F]fluoro-laminaribiose ([18F]FDL), and 2-deoxy-2-[18F]fluoro-cellobiose ([18F]FDC). We subsequently tested [18F]FDM and [18F]FSKin vitro,showing accumulation by several clinically relevant pathogens includingStaphylococcus aureusandAcinetobacter baumannii,and demonstrated their specific uptakein vivo.The lead sakebiose-derived tracer [18F]FSK was stable in human serum and showed high uptake in preclinical models of myositis and vertebral discitis-osteomyelitis. Both the synthetic ease, and high sensitivity of [18F]FSK toS. aureusincluding methicillin-resistant (MRSA) strains strongly justify clinical translation of this tracer to infected patients. Furthermore, this work suggests that chemoenzymatic radiosyntheses of complex [18F]FDG-derived oligomers will afford a wide array of PET radiotracers for infectious and oncologic applications.

Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.

Despite unprecedented efforts, our therapeutic arsenal against SARS-CoV-2 remains limited. The conserved macrodomain 1 (Mac1) in NSP3 is an enzyme exhibiting ADP-ribosylhydrolase activity and a possible drug target. To determine the therapeutic potential of Mac1 inhibition, we generated recombinant viruses and replicons encoding catalytically inactive NSP3 Mac1 domain by mutating a critical asparagine in the active site. While substitution to alanine (N40A) reduced activity by ∼10-fold, mutations to aspartic acid (N40D) reduced the catalytic activity by ∼100-fold relative to wildtype activity. Importantly, the N40A mutation rendered Mac1 unstable in vitro and lowered expression levels in bacterial and mammalian cells. When incorporated into SARS-CoV-2 molecular clones, the N40D mutant only modestly affected viral fitness in immortalized cell lines, but reduced viral replication in human airway organoids by 10-fold. In mice, N40D replicated at >1000-fold lower levels while inducing a robust interferon response, and all infected animals survived infection with the mutant, but not the wildtype virus. Our data validate SARS-CoV-2 NSP3 Mac1 domain as a critical viral pathogenesis factor and a reasonable target to develop antivirals, while emphasizing the importance of amino acid identity in viral mutagenesis studies and underscoring the limitations of solely relying on in vitro viral replication studies for target validation.

MET tyrosine kinase domain modeling location of the F1200 residue

Demographics of the METex14-mutated and EGFR-mutated cohorts.

Purpose:Although patients with advanced-stage non–small cell lung cancers (NSCLC) harboring MET exon 14 skipping mutations (METex14) often benefit from MET tyrosine kinase inhibitor (TKI) treatment, clinical benefit is limited by primary and acquired drug resistance. The molecular basis for this resistance remains incompletely understood.Experimental Design:Targeted sequencing analysis was performed on cell-free circulating tumor DNA obtained from 289 patients with advanced-stage METex14-mutated NSCLC.Results:Prominent co-occurring RAS–MAPK pathway gene alterations (e.g., in KRAS, NF1) were detected in NSCLCs with METex14 skipping alterations as compared with EGFR-mutated NSCLCs. There was an association between decreased MET TKI treatment response and RAS–MAPK pathway co-occurring alterations. In a preclinical model expressing a canonical METex14 mutation, KRAS overexpression or NF1 downregulation hyperactivated MAPK signaling to promote MET TKI resistance. This resistance was overcome by cotreatment with crizotinib and the MEK inhibitor trametinib.Conclusions:Our study provides a genomic landscape of co-occurring alterations in advanced-stage METex14-mutated NSCLC and suggests a potential combination therapy strategy targeting MAPK pathway signaling to enhance clinical outcomes.

The MET F1200 residue is conserved across multiple tyrosine kinases

Genes included in NGS Panels

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 152 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated protein dynamics within the active site, and key inhibitor motifs that will template future drug development against Mac1.Significance StatementSARS-CoV-2 encodes a viral macrodomain protein (Mac1) that hydrolyzes ribo-adenylate marks on viral proteins, disrupting the innate immune response to the virus. Catalytic mutations in the enzyme make the related SARS-1 virus less pathogenic and non-lethal in animals, suggesting that Mac1 will be a good antiviral target. However, no potent inhibitors of this protein class have been described, and pharmacologically the enzyme remains an orphan. Here, we computationally designed potent inhibitors of Mac1, determining 150 inhibitor-enzyme structures to ultra-high resolution by crystallography. In silico fragment linking and molecular docking of > 450 million virtual compounds led to inhibitors with submicromolar activity. These molecules may template future drug discovery efforts against this crucial but understudied viral target.

The process of finding molecules that bind to a target protein is a challenging first step in drug discovery. Crystallographic fragment screening is a strategy based on elucidating binding modes of small polar compounds and then building potency by expanding or merging them. Recent advances in high-throughput crystallography enable screening of large fragment libraries, reading out dense ensembles of fragments spanning the binding site. However, fragments typically have low affinity thus the road to potency is often long and fraught with false starts. Here, we take advantage of high-throughput crystallography to reframe fragment-based hit discovery as a denoising problem – identifying significant pharmacophore distributions from a fragment ensemble amid noise due to weak binders – and employ an unsupervised machine learning method to tackle this problem. Our method screens potential molecules by evaluating whether they recapitulate those fragment-derived pharmacophore distributions. We retrospectively validated our approach on an open science campaign against SARS-CoV-2 main protease (Mpro), showing that our method can distinguish active compounds from inactive ones using only structural data of fragment-protein complexes, without any activity data. Further, we prospectively found novel hits for Mpro and the Mac1 domain of SARS-CoV-2 non-structural protein 3. More broadly, our results demonstrate how unsupervised machine learning helps interpret high throughput crystallography data to rapidly discover of potent chemical modulators of protein function.

Protein structure prediction with a language model improves accuracy for orphan and designed proteins.

Decades of structure-function studies have established our current extensive understanding of enzymes. However, traditional structural models are snapshots of broader conformational ensembles of interchanging states. We demonstrate the need for conformational ensembles to understand function, using the enzyme ketosteroid isomerase (KSI) as an example. Comparison of prior KSI cryogenic x-ray structures suggested deleterious mutational effects from a misaligned oxyanion hole catalytic residue. However, ensemble information from room-temperature x-ray crystallography, combined with functional studies, excluded this model. Ensemble-function analyses can deconvolute effects from altering the probability of occupying a state ( P -effects) and changing the reactivity of each state ( k -effects); our ensemble-function analyses revealed functional effects arising from weakened oxyanion hole hydrogen bonding and substrate repositioning within the active site. Ensemble-function studies will have an integral role in understanding enzymes and in meeting the future goals of a predictive understanding of enzyme catalysis and engineering new enzymes.

Ancylostoma caninumis an important zoonotic gastrointestinal nematode of dogs worldwide and a close relative of human hookworms. We recently reported that racing greyhounds in the USA are infected withA. caninumthat are commonly resistant to multiple anthelmintics. Benzimidazole resistance inA. caninumin greyhounds was associated with a high frequency of the canonical F167Y(TTC>TAC) isotype-1 β-tubulin mutation. In this work, we show that benzimidazole resistance is remarkably widespread inA. caninumfrom domestic dogs across the USA. First, we identified and showed the functional significance of a novel benzimidazole isotype-1 β-tubulin resistance mutation, Q134H(CAA>CAT). Several benzimidazole resistantA. caninumisolates from greyhounds with a low frequency of the F167Y(TTC>TAC) mutation had a high frequency of a Q134H(CAA>CAT) 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 onA. caninumeggs from 685 hookworm positive pet dog fecal samples revealed that both mutations were widespread across the USA, often at high frequency, with prevalences of 49.7% (overall frequency 54.0%) and 31.1% (overall frequency 16.4%) for F167Y(TTC>TAC) and Q134H(CAA>CAT), respectively. Canonical codon 198 and 200 benzimidazole resistance mutations were absent. The F167Y(TTC>TAC) 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.Author SummaryAlthough increasingly common in livestock, no reports of widespread anthelmintic resistance are confirmed in any companion animal or human gastrointestinal nematode parasite to date. The canine hookworm is a common intestinal zoonotic parasite of dogs with severe clinical impacts in young dogs, and for which control is dependent on regular anthelmintic use. We recently reported multiple anthelmintic drug resistance inA. caninumisolates from greyhounds derived from multiple locations in the USA likely caused by long standing intensive treatment regimens in kennels. In this study, we investigated benzimidazole resistance inA. caninumin pet dogs across the USA. We also identified and showed the functional significance of a novel benzimidazole isotype-1 β-tubulin resistance mutation inA. caninumfrom greyhounds that has not been previously reported in the field for any organism. We then determined that this novel mutation, as well as a previously characterized resistance mutation, were present, often at high frequency, in manyA. caninumpopulations across the USA. This study reports the first evidence of widespread drug resistance for any parasitic nematode of companion animals and illustrates the power of molecular approaches to rapidly assess anthelmintic resistance in a region.

Insertions and deletions (indels) are a major source of genetic variation in evolution and the cause of nearly 30% of Mendelian disease. Despite their importance, indels are left out of nearly every systematic mutational scan to date due to technical challenges associated with making indel-containing libraries, limiting our understanding of indels in disease, biology, and evolution. Here we present a library generation method, DIMPLE, that generates deletions, insertions, and missense at similar frequencies within any gene. To benchmark DIMPLE, we generated libraries within four genes (Kir2.1, VatD, TRPV1, and OPRM1) of varying length and evolutionary origin. DIMPLE produces libraries that are near complete, low cost, and low bias. We measured how missense mutations and indels of varying length impact the potassium channel Kir2.1 surface expression. Across all Kir2.1’s secondary structure, deletions are more disruptive than insertions, beta sheets are extremely sensitive to large deletions, and flexible loops allow insertions far more frequently than deletions. DIMPLE’s low bias, ease of use, and low cost will enable high throughput probing of the importance of indels in disease and evolution.

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small molecule inhibitors of Mac1 have great therapeutic potential, few have been described. Here, we report the structure-based development of several chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high resolution X-ray protein crystallography, and binding evaluation with in-solution assays. Potent scaffolds were designed withComputational fragment-linking and ultra-large library docking identifies potent inhibitors of the SARS-CoV-2 macrodomain.

Understanding and controlling protein motion at atomic resolution is a hallmark challenge for structural biologists and protein engineers because conformational dynamics are essential for complex functions such as enzyme catalysis and allosteric regulation. Time-resolved crystallography offers a window into protein motions, yet without a universal perturbation to trigger conformational changes the method has been limited in scope. Here we couple a solvent-based temperature jump with time-resolved crystallography to visualize structural motions in lysozyme, a dynamic enzyme. We observed widespread atomic vibrations on the nanosecond timescale, which evolve on the sub-millisecond timescale into localized structural fluctuations that are coupled to the active site. An orthogonal perturbation to the enzyme, inhibitor binding, altered these dynamics by blocking key motions that allow energy to dissipate from vibrations into functional movements linked to the catalytic cycle. Because temperature-jump is a universal method for perturbing molecular motion, the method demonstrated here is broadly applicable for studying protein dynamics.

The MET receptor tyrosine kinase (RTK) and its cognate ligand hepatocyte growth factor (HGF) comprise a signaling axis essential for development, wound healing and tissue homeostasis. Aberrant HGF/MET signaling is a driver of many cancers and contributes to drug resistance to several approved therapeutics targeting other RTKs, making MET itself an important drug target. In RTKs, homeostatic receptor signaling is dependent on autoinhibition in the absence of ligand binding and orchestrated set of conformational changes induced by ligand-mediated receptor dimerization that result in activation of the intracellular kinase domains. A fundamental understanding of these mechanisms in the MET receptor remains incomplete, despite decades of research. This is due in part to the complex structure of the HGF ligand, which remains unknown in its full-length form, and a lack of high-resolution structures of the complete MET extracellular portion in an apo or ligand-bound state. A current view of HGF-dependent MET activation has evolved from biochemical and structural studies of HGF and MET fragments and here we review what these findings have thus far revealed.

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(TTC>TAC) 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(CAA>CAT). Several benzimidazole resistant A. caninum isolates from greyhounds with a low frequency of the F167Y(TTC>TAC) mutation had a high frequency of a Q134H(CAA>CAT) 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(TTC>TAC) and Q134H(CAA>CAT), respectively. Canonical codon 198 and 200 benzimidazole resistance mutations were absent. The F167Y(TTC>TAC) 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.

Demodex mites are commensal parasites of hair follicles (HFs). Normally asymptomatic, inflammatory outgrowth of mites can accompany malnutrition, immune dysfunction, and aging, but mechanisms restricting Demodex outgrowth are not defined. Here, we show that control of mite HF colonization in mice required group 2 innate lymphoid cells (ILC2s), interleukin-13 (IL-13), and its receptor, IL-4Ra-IL-13Ra1. HF-associated ILC2s elaborated IL-13 that attenuated HFs and epithelial proliferation at anagen onset; in their absence, Demodex colonization led to increased epithelial proliferation and replacement of gene programs for repair by aberrant inflammation, leading to the loss of barrier function and HF exhaustion. Humans with rhinophymatous acne rosacea, an inflammatory condition associated with Demodex, had increased HF inflammation with decreased type 2 cytokines, consistent with the inverse relationship seen in mice. Our studies uncover a key role for skin ILC2s and IL-13, which comprise an immune checkpoint that sustains cutaneous integrity and restricts pathologic infestation by colonizing HF mites.

Significance Computational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein’s preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.

The nonstructural protein 3 (NSP3) macrodomain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Mac1) removes adenosine diphosphate (ADP) ribosylation posttranslational modifications, playing a key role in the immune evasion capabilities of the virus responsible for the coronavirus disease 2019 pandemic. Here, we determined neutron and x-ray crystal structures of the SARS-CoV-2 NSP3 macrodomain using multiple crystal forms, temperatures, and pHs, across the apo and ADP-ribose–bound states. We characterize extensive solvation in the Mac1 active site and visualize how water networks reorganize upon binding of ADP-ribose and non-native ligands, inspiring strategies for displacing waters to increase the potency of Mac1 inhibitors. Determining the precise orientations of active site water molecules and the protonation states of key catalytic site residues by neutron crystallography suggests a catalytic mechanism for coronavirus macrodomains distinct from the substrate-assisted mechanism proposed for human MacroD2. These data provoke a reevaluation of macrodomain catalytic mechanisms and will guide the optimization of Mac1 inhibitors.

Alteration of antibiotic binding sites through modification of ribosomal RNA (rRNA) is a common form of resistance to ribosome-targeting antibiotics. The rRNA-modifying enzyme Cfr methylates an adenosine nucleotide within the peptidyl transferase center, resulting in the C-8 methylation of A2503 (m8A2503). Acquisition of cfr results in resistance to eight classes of ribosome-targeting antibiotics. Despite the prevalence of this resistance mechanism, it is poorly understood whether and how bacteria modulate Cfr methylation to adapt to antibiotic pressure. Moreover, direct evidence for how m8A2503 alters antibiotic binding sites within the ribosome is lacking. In this study, we performed directed evolution of Cfr under antibiotic selection to generate Cfr variants that confer increased resistance by enhancing methylation of A2503 in cells. Increased rRNA methylation is achieved by improved expression and stability of Cfr through transcriptional and post-transcriptional mechanisms, which may be exploited by pathogens under antibiotic stress as suggested by natural isolates. Using a variant that achieves near-stoichiometric methylation of rRNA, we determined a 2.2 Å cryo-electron microscopy structure of the Cfr-modified ribosome. Our structure reveals the molecular basis for broad resistance to antibiotics and will inform the design of new antibiotics that overcome resistance mediated by Cfr., eLife digest Antibiotics treat or prevent infections by killing bacteria or slowing down their growth. A large proportion of these drugs do this by disrupting an essential piece of cellular machinery called the ribosome which the bacteria need to make proteins. However, over the course of the treatment, some bacteria may gain genetic alterations that allow them to resist the effects of the antibiotic. Antibiotic resistance is a major threat to global health, and understanding how it emerges and spreads is an important area of research. Recent studies have discovered populations of resistant bacteria carrying a gene for a protein named chloramphenicol-florfenicol resistance, or Cfr for short. Cfr inserts a small modification in to the ribosome that prevents antibiotics from inhibiting the production of proteins, making them ineffective against the infection. To date, Cfr has been found to cause resistance to eight different classes of antibiotics. Identifying which mutations enhance its activity and protect bacteria is vital for designing strategies that fight antibiotic resistance. To investigate how the gene for Cfr could mutate and make bacteria more resistant, Tsai et al. performed a laboratory technique called directed evolution, a cyclic process which mimics natural selection. Genetic changes were randomly introduced in the gene for the Cfr protein and bacteria carrying these mutations were treated with tiamulin, an antibiotic rendered ineffective by the modification Cfr introduces into the ribosome. Bacteria that survived were then selected and had more mutations inserted. By repeating this process several times, Tsai et al. identified ‘super’ variants of the Cfr protein that lead to greater resistance. The experiments showed that these variants boosted resistance by increasing the proportion of ribosomes that contained the protective modification. This process was facilitated by mutations that enabled higher levels of Cfr protein to accumulate in the cell. In addition, the current study allowed, for the first time, direct visualization of how the Cfr modification disrupts the effect antibiotics have on the ribosome. These findings will make it easier for clinics to look out for bacteria that carry these ‘super’ resistant mutations. They could also help researchers design a new generation of antibiotics that can overcome resistance caused by the Cfr protein.

Summary ParagraphProtein dynamics play an important role in enzyme catalysis1-4. Many enzymes form covalent catalytic intermediates that can alter enzyme structure and conformational dynamics5,6. How these changes in enzyme structure and dynamics facilitate passage along the reaction coordinate is a fundamental unanswered question in structural enzymology. Here, we use Mix-and-Inject Serial Femtosecond X-ray Crystallography (MISC) at an X-ray Free Electron Laser (XFEL)7-10, ambient temperature X-ray crystallography, computer simulations, and enzyme kinetics to characterize how covalent modification of the active site cysteine residue in isocyanide hydratase (ICH) alters the enzyme’s conformational ensemble throughout the catalytic cycle. With MISC, we directly observe formation of a thioimidate covalent intermediate during ICH catalysis. The intermediate exhibits changes in the active site electrostatic environment, disrupting a hydrogen bond and triggering a cascade of conformational changes in ICH. X-ray-induced formation of a cysteine-sulfenic acid at the catalytic nucleophile (Cys101-SOH) with conventional crystallography at ambient temperature induces similar conformational shifts, demonstrating that these enzyme motions result from cysteine modification. Computer simulations show how cysteine modification-gated structural changes allosterically propagate through the ICH dimer. Mutations at Gly150 that modulate helical mobility reduce ICH catalytic turnover and alter its pre-steady state kinetic behavior, establishing that helical mobility is important for ICH catalytic efficiency. Taken together, our results demonstrate the potential of mix-and-inject XFEL crystallography to capture otherwise elusive mechanistic details of enzyme catalysis and dynamics from microcrystalline samples7,11. This approach can connect conformational dynamics to function for the large class of systems that rely on covalently modified cysteine residues for catalysis or regulation, resolving long-standing questions about enzyme mechanism and functionally relevant non-equilibrium enzyme motions.

Following decades of insights from structure–function studies, there is now a need to progress from a static to dynamic view of enzymes. Comparison of prior cryo X-ray structures suggested that deleterious effects from ketosteroid isomerase (KSI) mutants arise from misalignment of the oxyanion hole catalytic residue, Y16. However, multi-conformer models from room temperature X-ray diffraction revealed an ensemble of Y16 conformers indistinguishable from WT for Y32F/Y57F KSI and a distinct, non-native ensemble for Y16 in Y57F KSI. Functional analyses suggested rate effects arise from weakened hydrogen bonding, due to disruption of the Y16/Y57/Y32 hydrogen bond network, and repositioning of the general base. In general, catalytic changes can be deconvoluted into effects on the probability of occupying a state (P-effects) and the reactivity of each state (k-effects). Our results underscore the need for ensemble–function analysis to decipher enzyme function and ultimately manipulate their extraordinary capabilities.

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.

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 decrease 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 NMR 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.

The antibiotic linezolid, the first clinically approved member of the oxazolidinone class, inhibits translation of bacterial ribosomes by binding to the peptidyl transferase center. Recent work has demonstrated that linezolid does not inhibit peptide bond formation at all sequences but rather acts in a context-specific manner, namely when alanine occupies the penultimate position of the nascent chain. In this study, we determined that the second-generation oxazolidinone radezolid also induces stalling with alanine at the penultimate position. However, the molecular basis for context-specificity of these inhibitors has not been elucidated. In this study, we determined high-resolution cryo-EM structures of both linezolid and radezolid-stalled ribosome complexes. These structures reveal that the alanine side chain fits within a small hydrophobic crevice created by oxazolidinone, resulting in improved ribosome binding. Modification of the ribosome by the antibiotic resistance enzyme Cfr disrupts stalling by forcing the antibiotic to adopt a conformation that narrows the hydrophobic alanine pocket. Together, the structural and biochemical findings presented in this work provide molecular understanding of context-specific inhibition of translation by clinically important oxazolidinone antibiotics.

The antibiotic linezolid, the first clinically approved member of the oxazolidinone class, inhibits translation of bacterial ribosomes by binding to the peptidyl transferase center. Recent work has demonstrated that linezolid does not inhibit peptide bond formation at all sequences but rather acts in a context-specific manner, namely when alanine occupies the penultimate position of the nascent chain. However, the molecular basis for context-specificity has not been elucidated. Here we show that the second-generation oxazolidinone radezolid also induces stalling with a penultimate alanine, and we determine high-resolution cryo-EM structures of linezolid- and radezolid-stalled ribosome complexes to explain their mechanism of action. These structures reveal that the alanine side chain fits within a small hydrophobic crevice created by oxazolidinone, resulting in improved ribosome binding. Modification of the ribosome by the antibiotic resistance enzyme Cfr disrupts stalling due to repositioning of the modified nucleotide. Together, our findings provide molecular understanding for the context-specificity of oxazolidinones.

Bypassing nature’s evolutionary trajectory,de novoprotein generation—defined as creating artificial protein sequences from scratch—could enable breakthrough solutions for biomedical and environmental challenges. Viewing amino acid sequences as a language, we demonstrate that a deep learning-based language model can generate functional artificial protein sequences across families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. Our protein language model is trained by simply learning to predict the next amino acid for over 280 million protein sequences from thousands of protein families, without biophysical or coevolutionary modeling. We experimentally evaluate model-generated artificial proteins on five distinct antibacterial lysozyme families. Artificial proteins show similar activities and catalytic efficiencies as representative natural lysozymes, including hen egg white lysozyme, while reaching as low as 44% identity to any known naturally-evolved protein. The X-ray crystal structure of an enzymatically active artificial protein recapitulates the conserved fold and positioning of active site residues found in natural proteins. We demonstrate our language model’s ability to be adapted to different protein families by accurately predicting the functionality of artificial chorismate mutase and malate dehydrogenase proteins. These results indicate that neural language models successfully performde novoprotein generation across protein families and may prove to be a tool to shortcut evolution.