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The antibiotics chloramphenicol (CHL) and oxazolidinones including linezolid (LZD) are known to inhibit mitochondrial translation. This can result in serious, potentially deadly, side effects when used therapeutically. Although the mechanism by which CHL and LZD inhibit bacterial ribosomes has been elucidated in detail, their mechanism of action against mitochondrial ribosomes has yet to be explored. CHL and oxazolidinones bind to the ribosomal peptidyl transfer center (PTC) of the bacterial ribosome and prevent incorporation of incoming amino acids under specific sequence contexts, causing ribosomes to stall only at certain sequences. Through mitoribosome profiling, we show that inhibition of mitochondrial ribosomes is similarly context-specific - CHL and LZD lead to mitoribosome stalling primarily when there is an alanine, serine, or threonine in the penultimate position of the nascent peptide chain. We further validate context-specific stalling through in vitro translation assays. A high resolution cryo-EM structure of LZD bound to the PTC of the human mitoribosome shows extensive similarity to the mode of bacterial inhibition and also suggests potential avenues for altering selectivity. Our findings could help inform the rational development of future, less mitotoxic, antibiotics, which are critically needed in the current era of increasing antimicrobial resistance.

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Tuberculosis remains the deadliest infectious disease in the world and requires novel therapeutic targets. The ESX-3 secretion system, which is essential for iron and zinc homeostasis and thus M. tuberculosis survival, is a promising target. In this study, we perform a deep mutational scan on the ESX-3 core protein EccD3 in the model organism M. smegmatis. We systematically investigated the functional roles of 145 residues across the soluble ubiquitin-like domain, the conformationally distinct flexible linker, and selected transmembrane helices of EccD3. Our data combined with structural comparisons to ESX-5 complexes support a model where EccD3 stabilizes the complex, with the hinge motif within the linker being particularly sensitive to disruption. Our study is the first deep mutational scan in mycobacteria, which could help guide drug development toward novel treatment of tuberculosis. This study underscores the importance of context-specific mutational analyses for discovering essential protein interactions within mycobacterial systems.

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MET is a receptor tyrosine kinase (RTK) responsible for initiating signaling pathways involved in development and wound repair. MET activation relies on ligand binding to the extracellular receptor, which prompts dimerization, intracellular phosphorylation, and recruitment of associated signaling proteins. Mutations, which are predominantly observed clinically in the intracellular juxtamembrane and kinase domains, can disrupt typical MET regulatory mechanisms. Understanding how juxtamembrane variants, such as exon 14 skipping (METΔEx14), and rare kinase domain mutations can increase signaling, often leading to cancer, remains a challenge. Here, we perform a parallel deep mutational scan (DMS) of the MET intracellular kinase domain in two fusion protein backgrounds: wild-type and METΔEx14. Our comparative approach has revealed a critical hydrophobic interaction between a juxtamembrane segment and the kinase ⍺C-helix, pointing to potential differences in regulatory mechanisms between MET and other RTKs. Additionally, we have uncovered a ß5 motif that acts as a structural pivot for the kinase domain in MET and other TAM family of kinases. We also describe a number of previously unknown activating mutations, aiding the effort to annotate driver, passenger, and drug resistance mutations in the MET kinase domain.

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Phage-encoded anti-CRISPR (Acr) proteins inhibit CRISPR-Cas systems to allow phage replication and lysogeny maintenance. Most of the Acrs characterized to date are stable stoichiometric inhibitors, and while enzymatic Acrs have been characterized biochemically, little is known about their potency, specificity, and reversibility. Here, we examine AcrIF11, a widespread phage and plasmid-encoded ADP-ribosyltransferase (ART) that inhibits the Type I-F CRISPR-Cas system. We present an NMR structure of an AcrIF11 homolog that reveals chemical shift perturbations consistent with NAD (cofactor) binding. In experiments that model both lytic phage replication and MGE/lysogen stability under high targeting pressure, AcrIF11 is a highly potent CRISPR-Cas inhibitor and more robust to Cas protein level fluctuations than stoichiometric inhibitors. Furthermore, we demonstrate that AcrIF11 is remarkably specific, predominantly ADP-ribosylating Csy1 when expressed in P. aeruginosa. Given the reversible nature of ADP-ribosylation, we hypothesized that ADPr eraser enzymes (macrodomains) could remove ADPr from Csy1, a potential limitation of PTM-based CRISPR inhibition. We demonstrate that diverse macrodomains can indeed remove the modification from Csy1 in P. aeruginosa lysate. Together, these experiments connect the in vitro observations of AcrIF11's enzymatic activity to its potent and specific effects in vivo, clarifying the advantages and drawbacks of enzymatic Acrs in the evolutionary arms race between phages and bacteria.

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The macrodomain contained in the SARS-CoV-2 non-structural protein 3 (NSP3) is required for viral pathogenesis and lethality. Inhibitors that block the macrodomain could be a new therapeutic strategy for viral suppression. We previously performed a large-scale X-ray crystallography-based fragment screen and discovered a sub-micromolar inhibitor by fragment linking. However, this carboxylic acid-containing lead had poor membrane permeability and other liabilities that made optimization difficult. Here, we developed a shape-based virtual screening pipeline - FrankenROCS - to identify new macrodomain inhibitors using fragment X-ray crystal structures. We used FrankenROCS to exhaustively screen the Enamine high-throughput screening (HTS) collection of 2.1 million compounds and selected 39 compounds for testing, with the most potent compound having an IC50 value equal to 130 µM. We then paired FrankenROCS with an active learning algorithm (Thompson sampling) to efficiently search the Enamine REAL database of 22 billion molecules, testing 32 compounds with the most potent having an IC50 equal to 220 µM. Further optimization led to analogs with IC50 values better than 10 µM, with X-ray crystal structures revealing diverse binding modes despite conserved chemical features. These analogs represent a new lead series with improved membrane permeability that is poised for optimization. In addition, the collection of 137 X-ray crystal structures with associated binding data will serve as a resource for the development of structure-based drug discovery methods. FrankenROCS may be a scalable method for fragment linking to exploit ever-growing synthesis-on-demand libraries.

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SARS-CoV-2 continues to pose a threat to public health. Current therapeutics remain limited to direct acting antivirals that lack distinct mechanisms of action and are already showing signs of viral resistance. The virus encodes an ADP-ribosylhydrolase macrodomain (Mac1) that plays an important role in the coronaviral lifecycle by suppressing host innate immune responses. Genetic inactivation of Mac1 abrogates viral replication in vivo by potentiating host innate immune responses. However, it is unknown whether this can be achieved by pharmacologic inhibition and can therefore be exploited therapeutically. Here we report a potent and selective lead small molecule, AVI-4206, that is effective in an in vivo model of SARS-CoV-2 infection. Cellular models indicate that AVI-4206 has high target engagement and can weakly inhibit viral replication in a gamma interferon- and Mac1 catalytic activity-dependent manner; a stronger antiviral effect for AVI-4206 is observed in human airway organoids. In an animal model of severe SARS-CoV-2 infection, AVI-4206 reduces viral replication, potentiates innate immune responses, and leads to a survival benefit. Our results provide pharmacological proof of concept that Mac1 is a valid therapeutic target via a novel immune-restoring mechanism that could potentially synergize with existing therapies targeting distinct, essential aspects of the coronaviral life cycle. This approach could be more widely used to target other viral macrodomains to develop antiviral therapeutics beyond COVID-19.

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In the folded state, biomolecules exchange between multiple conformational states crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo-electron microscopy. Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the macromolecular crystallographic information file (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable the capture of macromolecular ensembles in a human and machine-interpretable way, potentially catalyzing breakthroughs for ensemble-function predictions, analogous to the achievements of AlphaFold with single-structure prediction.

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Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5,764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We identified common resistance sites across type I, type II, and type I ½ inhibitors, unveiled unique resistance and sensitizing mutations for each inhibitor, and validated non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.

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Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC ß-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on reanalysis of large-scale results against the σ2 and dopamine D4 receptors. It may be that as the scale of both the virtual libraries and their testing grows, not only are better ligands found but so too does our ability to rank them.

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In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

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Chitin is an abundant biopolymer and pathogen-associated molecular pattern that stimulates a host innate immune response. Mammals express chitin-binding and chitin-degrading proteins to remove chitin from the body. One of these proteins, Acidic Mammalian Chitinase (AMCase), is an enzyme known for its ability to function under acidic conditions in the stomach but is also active in tissues with more neutral pHs, such as the lung. Here, we used a combination of biochemical, structural, and computational modeling approaches to examine how the mouse homolog (mAMCase) can act in both acidic and neutral environments. We measured kinetic properties of mAMCase activity across a broad pH range, quantifying its unusual dual activity optima at pH 2 and 7. We also solved high-resolution crystal structures of mAMCase in complex with oligomeric GlcNAcn, the building block of chitin, where we identified extensive conformational ligand heterogeneity. Leveraging these data, we conducted molecular dynamics simulations that suggest how a key catalytic residue could be protonated via distinct mechanisms in each of the two environmental pH ranges. These results integrate structural, biochemical, and computational approaches to deliver a more complete understanding of the catalytic mechanism governing mAMCase activity at different pH. Engineering proteins with tunable pH optima may provide new opportunities to develop improved enzyme variants, including AMCase, for therapeutic purposes in chitin degradation.

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Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.

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Peer review is an important part of the scientific process, but traditional peer review at journals is coming under increased scrutiny for its inefficiency and lack of transparency. As preprints become more widely used and accepted, they raise the possibility of rethinking the peer-review process. Preprints are enabling new forms of peer review that have the potential to be more thorough, inclusive, and collegial than traditional journal peer review, and to thus fundamentally shift the culture of peer review toward constructive collaboration. In this Consensus View, we make a call to action to stakeholders in the community to accelerate the growing momentum of preprint sharing and provide recommendations to empower researchers to provide open and constructive peer review for preprints.

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Structural biology, as powerful as it is, can be misleading. We highlight four fundamental challenges: interpreting raw experimental data; accounting for motion; addressing the misleading nature of in vitro structures; and unraveling interactions between drugs and "anti-targets." Overcoming these challenges will amplify the impact of structural biology on drug discovery.

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PIK3CA (PI3Kα) is a lipid kinase commonly mutated in cancer, including ∼40% of hormone receptor-positive breast cancer. The most frequently observed mutants occur in the kinase and helical domains. Orthosteric PI3Kα inhibitors suffer from poor selectivity leading to undesirable side effects, most prominently hyperglycemia due to inhibition of wild-type (WT) PI3Kα. Here, we used molecular dynamics simulations and cryo-electron microscopy to identify an allosteric network that provides an explanation for how mutations favor PI3Kα activation. A DNA-encoded library screen leveraging electron microscopy-optimized constructs, differential enrichment, and an orthosteric-blocking compound led to the identification of RLY-2608, a first-in-class allosteric mutant-selective inhibitor of PI3Kα. RLY-2608 inhibited tumor growth in PIK3CA-mutant xenograft models with minimal impact on insulin, a marker of dysregulated glucose homeostasis. RLY-2608 elicited objective tumor responses in two patients diagnosed with advanced hormone receptor-positive breast cancer with kinase or helical domain PIK3CA mutations, with no observed WT PI3Kα-related toxicities. SIGNIFICANCE: Treatments for PIK3CA-mutant cancers are limited by toxicities associated with the inhibition of WT PI3Kα. Molecular dynamics, cryo-electron microscopy, and DNA-encoded libraries were used to develop RLY-2608, a first-in-class inhibitor that demonstrates mutant selectivity in patients. This marks the advance of clinical mutant-selective inhibition that overcomes limitations of orthosteric PI3Kα inhibitors. See related commentary by Gong and Vanhaesebroeck, p. 204 . See related article by Varkaris et al., p. 227 . This article is featured in Selected Articles from This Issue, p. 201.

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In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

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Chitin is an abundant biopolymer and pathogen-associated molecular pattern that stimulates a host innate immune response. Mammals express chitin-binding and chitin-degrading proteins to remove chitin from the body. One of these proteins, Acidic Mammalian Chitinase (AMCase), is an enzyme known for its ability to function under acidic conditions in the stomach but is also active in tissues with more neutral pHs, such as the lung. Here, we used a combination of biochemical, structural, and computational modeling approaches to examine how the mouse homolog (mAMCase) can act in both acidic and neutral environments. We measured kinetic properties of mAMCase activity across a broad pH range, quantifying its unusual dual activity optima at pH 2 and 7. We also solved high resolution crystal structures of mAMCase in complex with oligomeric GlcNAcn, the building block of chitin, where we identified extensive conformational ligand heterogeneity. Leveraging these data, we conducted molecular dynamics simulations that suggest how a key catalytic residue could be protonated via distinct mechanisms in each of the two environmental pH ranges. These results integrate structural, biochemical, and computational approaches to deliver a more complete understanding of the catalytic mechanism governing mAMCase activity at different pH. Engineering proteins with tunable pH optima may provide new opportunities to develop improved enzyme variants, including AMCase, for therapeutic purposes in chitin degradation.

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In hereditary papillary renal cell carcinoma (HPRCC), the MET receptor tyrosine kinase (RTK) mutations recorded to date are located in the kinase domain and lead to constitutive MET activation. This contrasts with MET mutations recently identified in non-small cell lung cancer (NSCLC), which lead to exon 14 skipping and deletion of a regulatory domain: in this latter case, the mutated receptor still requires ligand stimulation. Sequencing of MET in samples from 158 HPRCC and 2808 NSCLC patients revealed ten uncharacterized mutations. Four of these, all found in HPRCC and leading to amino acid substitutions in the N-lobe of the MET kinase, proved able to induce cell transformation, further enhanced by HGF stimulation: His1086Leu, Ile1102Thr, Leu1130Ser, and Cis1125Gly. Similar to the variant resulting in MET exon14 skipping, the two N-lobe MET variants His1086Leu, Ile1102Thr further characterized were found to require stimulation by HGF in order to strongly activate downstream signaling pathways and epithelial cell motility. The Ile1102Thr mutation displayed also transforming potential, promoting tumor growth in a xenograft model. In addition, the N-lobe-mutated MET variants were found to trigger a common HGF-stimulation-dependent transcriptional program, consistent with an observed increase in cell motility and invasion. Altogether, this functional characterization revealed that N-lobe variants still require ligand stimulation, in contrast to other RTK variants. This suggests that HGF expression in the tumor microenvironment is important for tumor growth. The sensitivity of these variants to MET TKIs opens the way for use of targeted therapies for patients harboring the corresponding mutations.

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Dietary fiber improves metabolic health, but host-encoded mechanisms for digesting fibrous polysaccharides are unclear. In this work, we describe a mammalian adaptation to dietary chitin that is coordinated by gastric innate immune activation and acidic mammalian chitinase (AMCase). Chitin consumption causes gastric distension and cytokine production by stomach tuft cells and group 2 innate lymphoid cells (ILC2s) in mice, which drives the expansion of AMCase-expressing zymogenic chief cells that facilitate chitin digestion. Although chitin influences gut microbial composition, ILC2-mediated tissue adaptation and gastrointestinal responses are preserved in germ-free mice. In the absence of AMCase, sustained chitin intake leads to heightened basal type 2 immunity, reduced adiposity, and resistance to obesity. These data define an endogenous metabolic circuit that enables nutrient extraction from an insoluble dietary constituent by enhancing digestive function.

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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 initiate 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 submillisecond 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.

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