Five Hypotheses on the Origins of Temperature Dependence of 77Se NMR Shifts in Diselenides.

Notable thermal shifts in diselenides have been documented in 77Se NMR for more than 50 years, but no satisfactory explanation has been found. Here, five hypotheses are considered as possible explanations for the large temperature dependence of the 77Se chemical shifts of diaryl and dialkyl diselenides compared to monoselenides and selenols. Density functional theory calculations are provided to bolster hypotheses and better understand the effects of barrier height and dipole energies. It is proposed that the temperature dependence of diselenide 77Se NMR chemical shifts is due to rotation around the Se-Se bond and sampling of twisted conformers at higher temperatures. The molecular twisting is solvent dependent; here, DMSO-d6 and toluene-d8 were evaluated. No correlation was established between para-substituents on diaryl diselenides and the magnitude of the change in the 77Se NMR shift (Δδ) with temperature.

Ligand efficacy shifts a nuclear receptor conformational ensemble between transcriptionally active and repressive states.

Nuclear receptors (NRs) are thought to dynamically alternate between transcriptionally active and repressive conformations, which are stabilized upon ligand binding. Most NR ligand series exhibit limited bias, primarily consisting of transcriptionally active agonists or neutral antagonists, but not repressive inverse agonists-a limitation that restricts understanding of the functional NR conformational ensemble. Here, we report a NR ligand series for peroxisome proliferator-activated receptor gamma (PPARγ) that spans a pharmacological spectrum from repression (inverse agonism) to activation (agonism) where subtle structural modifications switch compound activity. While crystal structures provide snapshots of the fully repressive state, NMR spectroscopy and conformation-activity relationship analysis reveals that compounds within the series shift the PPARγ conformational ensemble between transcriptionally active and repressive conformations that are populated in the apo/ligand-free ensemble. Our findings reveal a molecular framework for minimal chemical modifications that enhance PPARγ inverse agonism and elucidate their influence on the dynamic PPARγ conformational ensemble.

Antimicrobial Lasso Peptide Cloacaenodin Utilizes a Unique TonB-Dependent Transporter to Access Susceptible Bacteria.

The development of new antimicrobial agents effective against Gram-negative bacteria remains a major challenge in drug discovery. The lasso peptide cloacaenodin has potent antimicrobial activity against multiple strains in the Enterobacter genus, one of the ESKAPE pathogens. Here, we show that cloacaenodin uses a previously uncharacterized TonB-dependent transporter, which we name CloU, to cross the outer membrane (OM) of susceptible bacteria. Inner membrane transport is mediated by the protein SbmA. CloU is distinct from the known OM transporters (FhuA and PupB) utilized by other antimicrobial lasso peptides and thus offers important insight into the spectrum of activity of cloacaenodin. Using knowledge of the transport pathway to predict other cloacaenodin-susceptible strains, we demonstrate the activity of cloacaenodin against clinical isolates of Enterobacter and of a Kluyvera strain. Further, we use molecular dynamics simulations and mutagenesis of CloU to explain the variation in cloacaenodin susceptibility observed across different strains of Enterobacter. This work expands the currently limited understanding of lasso peptide uptake and advances the potential of cloacaenodin as an antibiotic.

Substrate Positioning Dynamics Involves a Non-Electrostatic Component to Mediate Catalysis.

Substrate positioning dynamics (SPD) orients the substrate in the active site, thereby influencing catalytic efficiency. However, it remains unknown whether SPD effects originate primarily from electrostatic perturbation inside the enzyme or can independently mediate catalysis with a significant non-electrostatic component. In this work, we investigated how the non-electrostatic component of SPD affects transition state (TS) stabilization. Using high-throughput enzyme modeling, we selected Kemp eliminase variants with similar electrostatics inside the enzyme but significantly different SPD. The kinetic parameters of these mutants were experimentally characterized. We observed a valley-shaped, two-segment linear correlation between the TS stabilization free energy (converted from kinetic parameters) and substrate positioning index (a metric to quantify SPD). The energy varies by approximately 2 kcal/mol. Favorable SPD was observed for the distal mutant R154W, increasing the proportion of reactive conformations and leading to the lowest activation free energy. These results indicate the substantial contribution of the non-electrostatic component of SPD to enzyme catalytic efficiency.

EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution.

Hydrolase-catalyzed kinetic resolution is a well-established biocatalytic process. However, the computational tools that predict favorable enzyme scaffolds for separating a racemic substrate mixture are underdeveloped. To address this challenge, we trained a deep learning framework, EnzyKR, to automate the selection of hydrolases for stereoselective biocatalysis. EnzyKR adopts a classifier-regressor architecture that first identifies the reactive binding conformer of a substrate-hydrolase complex, and then predicts its activation free energy. A structure-based encoding strategy was used to depict the chiral interactions between hydrolases and enantiomers. Different from existing models trained on protein sequences and substrate SMILES strings, EnzyKR was trained using 204 substrate-hydrolase complexes, which were constructed by docking. EnzyKR was tested using a held-out dataset of 20 complexes on the task of predicting activation free energy. EnzyKR achieved a Pearson correlation coefficient (R) of 0.72, a Spearman rank correlation coefficient (Spearman R) of 0.72, and a mean absolute error (MAE) of 1.54 kcal mol-1 in this task. Furthermore, EnzyKR was tested on the task of predicting enantiomeric excess ratios for 28 hydrolytic kinetic resolution reactions catalyzed by fluoroacetate dehalogenase RPA1163, halohydrin HheC, A. mediolanus epoxide hydrolase, and P. fluorescens esterase. The performance of EnzyKR was compared against that of a recently developed kinetic predictor, DLKcat. EnzyKR correctly predicts the favored enantiomer and outperforms DLKcat in 18 out of 28 reactions, occupying 64% of the test cases. These results demonstrate EnzyKR to be a new approach for prediction of enantiomeric outcomes in hydrolase-catalyzed kinetic resolution reactions.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Iron acquisition by a commensal bacterium modifies host nutritional immunity during Salmonella infection.

During intestinal inflammation, host nutritional immunity starves microbes of essential micronutrients, such as iron. Pathogens scavenge iron using siderophores, including enterobactin; however, this strategy is counteracted by host protein lipocalin-2, which sequesters iron-laden enterobactin. Although this iron competition occurs in the presence of gut bacteria, the roles of commensals in nutritional immunity involving iron remain unexplored. Here, we report that the gut commensal Bacteroides thetaiotaomicron acquires iron and sustains its resilience in the inflamed gut by utilizing siderophores produced by other bacteria, including Salmonella, via a secreted siderophore-binding lipoprotein XusB. Notably, XusB-bound enterobactin is less accessible to host sequestration by lipocalin-2 but can be "re-acquired" by Salmonella, allowing the pathogen to evade nutritional immunity. Because the host and pathogen have been the focus of studies of nutritional immunity, this work adds commensal iron metabolism as a previously unrecognized mechanism modulating the host-pathogen interactions and nutritional immunity.

EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation.

Directed evolution facilitates enzyme engineering via iterative rounds of mutagenesis. Despite the wide applications of high-throughput screening, building "smart libraries" to effectively identify beneficial variants remains a major challenge in the community. Here, we developed a new computational directed evolution protocol based on EnzyHTP, a software that we have previously reported to automate enzyme modeling. To enhance the throughput efficiency, we implemented an adaptive resource allocation strategy that dynamically allocates different types of computing resources (e.g., GPU/CPU) based on the specific need of an enzyme modeling subtask in the workflow. We implemented the strategy as a Python library and tested the library using fluoroacetate dehalogenase as a model enzyme. The results show that compared to fixed resource allocation where both CPU and GPU are on-call for use during the entire workflow, applying adaptive resource allocation can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed a computational directed evolution protocol under the framework of adaptive resource allocation. The workflow was tested against two rounds of mutational screening in the directed evolution experiments of Kemp eliminase (KE07) with a total of 184 mutants. Using folding stability and electrostatic stabilization energy as computational readout, we identified all four experimentally observed target variants. Enabled by the workflow, the entire computation task (i.e., 18.4 µs MD and 18,400 QM single-point calculations) completes in 3 days of wall-clock time using ∼30 GPUs and ∼1000 CPUs.

Iron acquisition by a commensal bacterium modifies host nutritional immunity during Salmonella infection.

During intestinal inflammation, host nutritional immunity starves microbes of essential micronutrients such as iron. Pathogens scavenge iron using siderophores, which is counteracted by the host using lipocalin-2, a protein that sequesters iron-laden siderophores, including enterobactin. Although the host and pathogens compete for iron in the presence of gut commensal bacteria, the roles of commensals in nutritional immunity involving iron remain unexplored. Here, we report that the gut commensal Bacteroides thetaiotaomicron acquires iron in the inflamed gut by utilizing siderophores produced by other bacteria including Salmonella, via a secreted siderophore-binding lipoprotein termed XusB. Notably, XusB-bound siderophores are less accessible to host sequestration by lipocalin-2 but can be "re-acquired" by Salmonella , allowing the pathogen to evade nutritional immunity. As the host and pathogen have been the focus of studies of nutritional immunity, this work adds commensal iron metabolism as a previously unrecognized mechanism modulating the interactions between pathogen and host nutritional immunity.

High-throughput computational investigation of protein electrostatics and cavity for SAM-dependent methyltransferases.

S-adenosyl methionine (SAM)-dependent methyl transferases (MTases) are a ubiquitous class of enzymes catalyzing dozens of essential life processes. Despite targeting a large space of substrates with diverse intrinsic reactivity, SAM MTases have similar catalytic efficiency. While understanding of MTase mechanism has grown tremendously through the integration of structural characterization, kinetic assays, and multiscale simulations, it remains elusive how these enzymes have evolved to fit the diverse chemical needs of their respective substrates. In this work, we performed a high-throughput molecular modeling analysis of 91 SAM MTases to better understand how their properties (i.e., electric field [EF] strength and active site volumes) help achieve similar catalytic efficiency toward substrates of different reactivity. We found that EF strengths have largely adjusted to make the target atom a better methyl acceptor. For MTases that target RNA/DNA and histone proteins, our results suggest that EF strength accommodates formal hybridization state and variation in cavity volume trends with diversity of substrate classes. Metal ions in SAM MTases contribute negatively to EF strength for methyl donation and enzyme scaffolds tend to offset these contributions.

Accelerated Entropic Path Sampling with a Bidirectional Generative Adversarial Network.

The role of entropy in mediating the dynamic outcomes of chemical reactions remains largely unknown. To evaluate the change of entropy along post-transition state paths, we have previously developed entropic path sampling that computes configurational entropy from an ensemble of reaction trajectories. However, one major caveat of this approach lies in its high computational demand: about 2000 trajectories are needed to converge the computation of an entropic profile. Here, by leveraging a deep generative model, we developed an accelerated entropic path sampling approach that evaluates entropic profiles using merely a few hundred reaction dynamic trajectories. The new method, called bidirectional generative adversarial network-entropic path sampling, can enhance the estimation of probability density functions of molecular configurations by generating pseudo-molecular configurations that are statistically indistinguishable from the true data. The method was established using cyclopentadiene dimerization, in which we reproduced the reference entropic profiles (derived from 2480 trajectories) using merely 124 trajectories. The method was further benchmarked using three reactions with symmetric post-transition-state bifurcation, including endo-butadiene dimerization, 5-fluoro-1,3-cyclopentadiene dimerization, and 5-methyl-1,3-cyclopentadiene dimerization. The results indicate the existence of a "hidden entropic intermediate", which is a dynamic species that binds to a local entropic maximum where no free energy minimum is formed.

Computational Strategies for Entropy Modeling in Chemical Processes.

Computational simulations of entropy are important in understanding the thermodynamic forces that drive chemical reactions on a molecular scale. In recent years, various algorithms have been developed and applied in conjunction with molecular modeling techniques to evaluate the change of entropy in solvation, hydrophobic interactions, and chemical reactions. The aim of this review is to highlight four specific computational entropy calculation methods: normal mode analysis, free volume theory, two-phase thermodynamics, and configurational entropy modeling. The technical aspects, applications, and limitations of each method will be discussed in detail.

High-Throughput Screen Reveals the Structure-Activity Relationship of the Antimicrobial Lasso Peptide Ubonodin.

The Burkholderia cepacia complex (Bcc) is a group of bacteria including opportunistic human pathogens. Immunocompromised individuals and cystic fibrosis patients are especially vulnerable to serious infections by these bacteria, motivating the search for compounds with antimicrobial activity against the Bcc. Ubonodin is a lasso peptide with promising activity against Bcc species, working by inhibiting RNA polymerase in susceptible bacteria. We constructed a library of over 90 000 ubonodin variants with 2 amino acid substitutions and used a high-throughput screen and next-generation sequencing to examine the fitness of the entire library, generating the most comprehensive data set on lasso peptide activity so far. This screen revealed information regarding the structure-activity relationship of ubonodin over a large sequence space. Remarkably, the screen identified one variant with not only improved activity compared to wild-type ubonodin but also a submicromolar minimum inhibitory concentration (MIC) against a clinical isolate of the Bcc member Burkholderia cenocepacia. Ubonodin and several of the variants identified in this study had lower MICs against certain Bcc strains than those of many clinically approved antibiotics. Finally, the large library size enabled us to develop DeepLasso, a deep learning model that can predict the RNAP inhibitory activity of an ubonodin variant.

Effects of a genetic variant rs13266634 in the zinc transporter 8 gene (SLC30A8) on insulin and lipid levels before and after a high-fat mixed macronutrient tolerance test in U.S. adults.

BACKGROUND: The common C-allele of rs13266634 (c.973C>T or p.Arg325Trp) in SLC30A8 (ZNT8) is associated with increased risk of type 2 diabetes. While previous studies have examined the correlation of the variant with insulin and glucose metabolism, the effects of this variant on insulin and lipid responses after a lipid challenge in humans remain elusive. The goal of this study was to determine whether the C-allele had an impact on an individual's risk to metabolic syndromes in U.S. adults. METHOD: We studied the genotypes of rs13266634 in 349 individuals aged between 18 and 65 y with BMI ranging from 18.5 to 45 kg/m2. The subjects were evaluated for insulin, glucose, HbA1c, ghrelin, and lipid profiles before and after a high-fat mixed macronutrient tolerance test (MMTT). RESULTS: We found that the effects of variants rs13266634 on glucose and lipid metabolism were sex-dimorphic, greater impact on males than on females. Insulin incremental area under the curve (AUC) after MMTT was significantly decreased in men with the CC genotype (p < 0.05). Men with the CC genotype also had the lowest fasting non-esterified fatty acid (NEFA) concentrations. On the other hand, the TT genotype was associated with a slower triglyceride removal from the circulation in men after MMTT. The reduced triglyceride removal was also observed in subjects with BMI ≥ 30 carrying either the heterozygous or homozygous T-allele. Nevertheless, the SNP had little effect on fasting or postprandial blood glucose and cholesterol concentrations. CONCLUSION: We conclude that the CC genotype negatively affects insulin response after MMTT while the T-allele may negatively influence lipolysis during fasting and postprandial blood triglyceride removal in men and obese subjects, a novel finding in this study.

Identification of novel compound heterozygous variants in the SLC30A7 (ZNT7) gene in two French brothers with stunted growth, testicular hypoplasia and bone marrow failure.

Zinc is an essential trace mineral. Dietary zinc deficiency results in stunted growth, skin lesions, hypogonadism and frequent infections in humans. Mice genetically lacking Slc30a7 suffer from mild zinc deficiency and are prone to development of prostate cancer and insulin resistance. Disease-causing variants or mutations in the human SLC30A7 (ZNT7) gene have not been previously reported. Here, we describe two-boy siblings from a French family with stunted growth, testicular hypoplasia and bone marrow failure. Exome sequencing revealed compound heterozygous variants in ZNT7 consisting of NM_133496.5:c.21dup; p.Asp8ArgfsTer3 and c.842 + 15 T > C inherited from their unaffected mother and father, respectively. The c.21dup variant led to a premature stop codon generated in exon 1 of the ZNT7 coding sequence. RNA-seq analysis demonstrated that the c.842 + 15 T > C variant resulted in a leaky mRNA splicing event generating a premature stop codon right after the splicing donor site of exon 8. Moreover, the expression of ZNT7 protein was remarkably reduced by 80-96% in the affected brothers compared to the control cells. These findings strongly suggest that biallelic variants in SLC30A7 should be considered as a cause of growth retardation, testicular hypoplasia and syndromic bone marrow failure.

Properties of Configurationally Stable Atropoenantiomers in Macrocyclic Natural Products and the Chrysophaentin Family.

development of antibiotics, antineoplastics, and therapeutics for other diseases. Natural products are unique among all other small molecules in that they are produced by dedicated enzymatic assembly lines that are the protein products of biosynthetic gene clusters. As the products of chiral macromolecules, natural products have distinct three-dimensional shapes and stereochemistry is often encoded in their structures through the presence of stereocenters, or in the case of molecules that lack a stereocenter, the presence of an axis or plane of chirality. In the latter forms of chirality, if the barrier to rotation about the chiral axis or chiral plane is sufficiently high, stable conformers may exist allowing for isolation of discrete conformers, also known as atropisomers. Importantly, the diverse functions and biological activities of natural products are contingent upon their structures, stereochemistry and molecular shape. With continued innovation in methods for natural products discovery, synthetic chemistry, and analytical and computational tools, new insights into atropisomerism in natural products and related scaffolds are being made. As molecular complexity increases, more than one form of stereoisomerism may exist in a single compound (for example, point chirality, chiral axes, and chiral planes), sometimes creating atypical or noncanonical atropisomers, a term used to distinguish physically noninterconvertable atropisomers from typical atropisomers.Here we provide an account of the discovery and unusual structural and stereochemical features of the chrysophaentins, algal derived inhibitors of the bacterial cytoskeletal protein FtsZ and its associated protein partners. Eleven members of the chrysophaentin family have been discovered to date; seven of these are macrocyclic bis-bibenzyl ethers wherein the site of the ether linkage yields either a symmetrical or asymmetrical macrocyclic ring system. The asymmetrical ring system is highly strained and corresponds to the compounds having the most potent antimicrobial activity among the family. We review the structure elucidation and NMR properties that indicate restricted rotation between axes of two biaryl ethers, and the plane represented by the substituted 2-Z-butene bridge common to all of the macrocycles. Computational studies that corroborate high barriers to rotation about one representative plane, on the order of 20+ kcal/mol are presented. These barriers to rotation fix the conformation of the macrocycle into a bowl-like structure and suggest that an atropisomer should exist. Experimental evidence for atropisomerism is presented, consistent with computational predictions. These properties are discussed in the context of the total synthesis of 9-dechlorochrysophaenin A and its ring C isomers. Last, we discuss the implications for the presence of enantiomers in the biological activity and macrocyclization of the natural product.

LassoHTP: A High-Throughput Computational Tool for Lasso Peptide Structure Construction and Modeling.

Lasso peptides are a subclass of ribosomally synthesized and post-translationally modified peptides with a slipknot conformation. With superior thermal stability, protease resistance, and antimicrobial activity, lasso peptides are promising candidates for bioengineering and pharmaceutical applications. To enable high-throughput computational prediction and design of lasso peptides, we developed a software, LassoHTP, for automatic lasso peptide structure construction and modeling. LassoHTP consists of three modules, including the scaffold constructor, mutant generator, and molecular dynamics (MD) simulator. With a user-provided sequence and conformational annotation, LassoHTP can either generate the structure and conformational ensemble as is or conduct random mutagenesis. We used LassoHTP to construct eight known lasso peptide structures de novo and to simulate their conformational ensembles for 100 ns MD simulations. For benchmarking, we calculated the root mean square deviation (RMSD) of these ensembles with reference to their experimental crystal or NMR PDB structures; we also compared these RMSD values against those of the MD ensembles that are initiated from the PDB structures. Dihedral principal component analysis was also conducted. The results show that the LassoHTP-initiated ensembles are similar to those of the PDB-initiated ensembles. LassoHTP offers a computational platform to develop strategies for lasso peptide prediction and design.

Data-driven enzyme engineering to identify function-enhancing enzymes.

Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence-structure-function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.

IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database.

Data-driven modeling has emerged as a new paradigm for biocatalyst design and discovery. Biocatalytic databases that integrate enzyme structure and function data are in urgent need. Here we describe IntEnzyDB as an integrated structure-kinetics database for facile statistical modeling and machine learning. IntEnzyDB employs a relational database architecture with a flattened data structure, which allows rapid data operation. This architecture also makes it easy for IntEnzyDB to incorporate more types of enzyme function data. IntEnzyDB contains enzyme kinetics and structure data from six enzyme commission classes. Using 1050 enzyme structure-kinetics pairs, we investigated the efficiency-perturbing propensities of mutations that are close or distal to the active site. The statistical results show that efficiency-enhancing mutations are globally encoded and that deleterious mutations are much more likely to occur in close mutations than in distal mutations. Finally, we describe a web interface that allows public users to access enzymology data stored in IntEnzyDB. IntEnzyDB will provide a computational facility for data-driven modeling in biocatalysis and molecular evolution.

Mechanistic Studies of a Skatole-Forming Glycyl Radical Enzyme Suggest Reaction Initiation via Hydrogen Atom Transfer.

Gut microbial decarboxylation of amino acid-derived arylacetates is a chemically challenging enzymatic transformation which generates small molecules that impact host physiology. The glycyl radical enzyme (GRE) indoleacetate decarboxylase from Olsenella uli (Ou IAD) performs the non-oxidative radical decarboxylation of indole-3-acetate (I3A) to yield skatole, a disease-associated metabolite produced in the guts of swine and ruminants. Despite the importance of IAD, our understanding of its mechanism is limited. Here, we characterize the mechanism of Ou IAD, evaluating previously proposed hypotheses of: (1) a Kolbe-type decarboxylation reaction involving an initial 1-e- oxidation of the carboxylate of I3A or (2) a hydrogen atom abstraction from the α-carbon of I3A to generate an initial carbon-centered radical. Site-directed mutagenesis, kinetic isotope effect experiments, analysis of reactions performed in D2O, and computational modeling are consistent with a mechanism involving initial hydrogen atom transfer. This finding expands the types of radical mechanisms employed by GRE decarboxylases and non-oxidative decarboxylases, more broadly. Elucidating the mechanism of IAD decarboxylation enhances our understanding of radical enzymes and may inform downstream efforts to modulate this disease-associated metabolism.