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Glycosylation is a predominant strategy plants use to fine-tune the properties of small molecule metabolites to affect their bioactivity, transport, and storage. It is also important in biotechnology and medicine as many glycosides are utilized in human health. Small molecule glycosylation is largely carried out by family 1 glycosyltransferases. Here, we report a structural and biochemical investigation of UGT95A1, a family 1 GT enzyme from Pilosella officinarum that exhibits a strong, unusual regiospecificity for the 3'-O position of flavonoid acceptor substrate luteolin. We obtained an apo crystal structure to help drive the analyses of a series of binding site mutants, revealing that while most residues are tolerant to mutations, key residues M145 and D464 are important for overall glycosylation activity. Interestingly, E347 is crucial for maintaining the strong preference for 3'-O glycosylation, while R462 can be mutated to increase regioselectivity. The structural determinants of regioselectivity were further confirmed in homologous enzymes. Our study also suggests that the enzyme contains large, highly dynamic, disordered regions. We showed that while most disordered regions of the protein have little to no implication in catalysis, the disordered regions conserved among investigated homologs are important to both the overall efficiency and regiospecificity of the enzyme. This report represents a comprehensive in-depth analysis of a family 1 GT enzyme with a unique substrate regiospecificity and may provide a basis for enzyme functional prediction and engineering.

Sunscreen has been used for thousands of years to protect skin from ultraviolet radiation. However, the use of modern commercial sunscreen containing oxybenzone, ZnO, and TiO2 has raised concerns due to their negative effects on human health and the environment. In this study, we aim to establish an efficient microbial platform for production of shinorine, a UV light absorbing compound with anti-aging properties. First, we methodically selected an appropriate host for shinorine production by analyzing central carbon flux distribution data from prior studies alongside predictions from genome-scale metabolic models (GEMs). We enhanced shinorine productivity through CRISPRi-mediated downregulation and utilized shotgun proteomics to pinpoint potential competing pathways. Simultaneously, we improved the shinorine biosynthetic pathway by refining its design, optimizing promoter usage, and altering the strength of ribosome binding sites. Finally, we conducted amino acid feeding experiments under various conditions to identify the key limiting factors in shinorine production. The study combines meta-analysis of 13C-metabolic flux analysis, GEMs, synthetic biology, CRISPRi-mediated gene downregulation, and omics analysis to improve shinorine production, demonstrating the potential of Pseudomonas putida KT2440 as platform for shinorine production.

Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo-electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model-building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix-AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1.21 release. This webservice will be improved based on how it is used by the research community and the future research directions for Phenix.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane) and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the "IPP-bypass" pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.

Neutron diffraction is one of the three crystallographic techniques (X-ray, neutron and electron diffraction) used to determine the atomic structures of molecules. Its particular strengths derive from the fact that H (and D) atoms are strong neutron scatterers, meaning that their positions, and thus protonation states, can be derived from crystallographic maps. However, because of technical limitations and experimental obstacles, the quality of neutron diffraction data is typically much poorer (completeness, resolution and signal to noise) than that of X-ray diffraction data for the same sample. Further, refinement is more complex as it usually requires additional parameters to describe the H (and D) atoms. The increase in the number of parameters may be mitigated by using the `riding hydrogen' refinement strategy, in which the positions of H atoms without a rotational degree of freedom are inferred from their neighboring heavy atoms. However, this does not address the issues related to poor data quality. Therefore, neutron structure determination often relies on the presence of an X-ray data set for joint X-ray and neutron (XN) refinement. In this approach, the X-ray data serve to compensate for the deficiencies of the neutron diffraction data by refining one model simultaneously against the X-ray and neutron data sets. To be applicable, it is assumed that both data sets are highly isomorphous, and preferably collected from the same crystals and at the same temperature. However, the approach has a number of limitations that are discussed in this work by comparing four separately re-refined neutron models. To address the limitations, a new method for joint XN refinement is introduced that optimizes two different models against the different data sets. This approach is tested using neutron models and data deposited in the Protein Data Bank. The efficacy of refining models with H atoms as riding or as individual atoms is also investigated.

Pseudomonas putida have emerged as promising biocatalysts for the conversion of sugars and aromatic compounds obtained from lignocellulosic biomass. Understanding the role of carbon catabolite repression (CCR) in these strains is critical to optimize biomass conversion to fuels and chemicals. The CCR functioning in P. putida M2, a strain capable of consuming both hexose and pentose sugars as well as aromatic compounds, was investigated by cultivation experiments, proteomics, and CRISPRi-based gene repression. Strain M2 co-utilized sugars and aromatic compounds simultaneously; however, during cultivation with glucose and aromatic compounds (p-coumarate and ferulate) mixture, intermediates (4-hydroxybenzoate and vanillate) accumulated, and substrate consumption was incomplete. In contrast, xylose-aromatic consumption resulted in transient intermediate accumulation and complete aromatic consumption, while xylose was incompletely consumed. Proteomics analysis revealed that glucose exerted stronger repression than xylose on the aromatic catabolic proteins. Key glucose (Eda) and xylose (XylX) catabolic proteins were also identified at lower abundance during cultivation with aromatic compounds implying simultaneous catabolite repression by sugars and aromatic compounds. Reduction of crc expression via CRISPRi led to faster growth and glucose and p-coumarate uptake in the CRISPRi strains compared to the control, while no difference was observed on xylose+p-coumarate. The increased abundances of Eda and amino acid biosynthesis proteins in the CRISPRi strain further supported these observations. Lastly, small RNAs (sRNAs) sequencing results showed that CrcY and CrcZ homologues levels in M2, previously identified in P. putida strains, were lower under strong CCR (glucose+p-coumarate) condition compared to when repression was absent (p-coumarate or glucose only).IMPORTANCEA newly isolated Pseudomonas putida strain, P. putida M2, can utilize both hexose and pentose sugars as well as aromatic compounds making it a promising host for the valorization of lignocellulosic biomass. Pseudomonads have developed a regulatory strategy, carbon catabolite repression, to control the assimilation of carbon sources in the environment. Carbon catabolite repression may impede the simultaneous and complete metabolism of sugars and aromatic compounds present in lignocellulosic biomass and hinder the development of an efficient industrial biocatalyst. This study provides insight into the cellular physiology and proteome during mixed-substrate utilization in P. putida M2. The phenotypic and proteomics results demonstrated simultaneous catabolite repression in the sugar-aromatic mixtures, while the CRISPRi and sRNA sequencing demonstrated the potential role of the crc gene and small RNAs in carbon catabolite repression.

Mass spectrometry (MS) enables detection of different chemical species with a very high specificity; however, it can be limited by its throughput. Integrating MS with microfluidics has a tremendous potential to improve throughput and accelerate biochemical research. In this work, we introduce Drop-NIMS, a combination of a passive droplet loading microfluidic device and a matrix-free MS laser desorption ionization technique called nanostructure-initiator mass spectrometry (NIMS). This platform combines different droplets at random to generate a combinatorial library of enzymatic reactions that are deposited directly on the NIMS surface without requiring additional sample handling. The enzyme reaction products are then detected with MS. Drop-NIMS was used to rapidly screen enzymatic reactions containing low (on the order of nL) volumes of glycoside reactants and glycoside hydrolase enzymes per reaction. MS "barcodes" (small compounds with unique masses) were added to the droplets to identify different combinations of substrates and enzymes created by the device. We assigned xylanase activities to several putative glycoside hydrolases, making them relevant to food and biofuel industrial applications. Overall, Drop-NIMS is simple to fabricate, assemble, and operate and it has potential to be used with many other small molecule metabolites.

Diffraction intensities from a crystallographic experiment include contributions from the entire unit cell of the crystal: the macromolecule, the solvent around it and eventually other compounds. These contributions cannot typically be well described by an atomic model alone, i.e. using point scatterers. Indeed, entities such as disordered (bulk) solvent, semi-ordered solvent (e.g. lipid belts in membrane proteins, ligands, ion channels) and disordered polymer loops require other types of modeling than a collection of individual atoms. This results in the model structure factors containing multiple contributions. Most macromolecular applications assume two-component structure factors: one component arising from the atomic model and the second one describing the bulk solvent. A more accurate and detailed modeling of the disordered regions of the crystal will naturally require more than two components in the structure factors, which presents algorithmic and computational challenges. Here an efficient solution of this problem is proposed. All algorithms described in this work have been implemented in the computational crystallography toolbox (CCTBX) and are also available within Phenix software. These algorithms are rather general and do not use any assumptions about molecule type or size nor about those of its components.

In natural photosynthesis, the light-driven splitting of water into electrons, protons and molecular oxygen forms the first step of the solar-to-chemical energy conversion process. The reaction takes place in photosystem II, where the Mn4CaO5 cluster first stores four oxidizing equivalents, the S0 to S4 intermediate states in the Kok cycle, sequentially generated by photochemical charge separations in the reaction center and then catalyzes the O-O bond formation chemistry1-3. Here, we report room temperature snapshots by serial femtosecond X-ray crystallography to provide structural insights into the final reaction step of Kok's photosynthetic water oxidation cycle, the S3→[S4]→S0 transition where O2 is formed and Kok's water oxidation clock is reset. Our data reveal a complex sequence of events, which occur over micro- to milliseconds, comprising changes at the Mn4CaO5 cluster, its ligands and water pathways as well as controlled proton release through the hydrogen-bonding network of the Cl1 channel. Importantly, the extra O atom Ox, which was introduced as a bridging ligand between Ca and Mn1 during the S2→S3 transition4-6, disappears or relocates in parallel with Yz reduction starting at approximately 700 μs after the third flash. The onset of O2 evolution, as indicated by the shortening of the Mn1-Mn4 distance, occurs at around 1,200 μs, signifying the presence of a reduced intermediate, possibly a bound peroxide.

Photosynthetic light harvesting requires efficient energy transfer within dynamic networks of light-harvesting complexes embedded within phospholipid membranes. Artificial light-harvesting models are valuable tools for understanding the structural features underpinning energy absorption and transfer within chromophore arrays. Here, a method for attaching a protein-based light-harvesting model to a planar, fluid supported lipid bilayer (SLB) is developed.  The protein model consists of the tobacco mosaic viral capsid proteins that are gene-doubled to create a tandem dimer (dTMV). Assemblies of dTMV break the facial symmetry of the double disk to allow for differentiation between the disk faces. A single reactive lysine residue is incorporated into the dTMV assemblies for the site-selective attachment of chromophores for light absorption. On the opposing dTMV face, a cysteine residue is incorporated for the bioconjugation of a peptide containing a polyhistidine tag for association with SLBs. The dual-modified dTMV complexes show significant association with SLBs and exhibit mobility on the bilayer. The techniques used herein offer a new method for protein-surface attachment and provide a platform for evaluating excited state energy transfer events in a dynamic, fully synthetic artificial light-harvesting system.

Modular polyketide synthases (PKSs) are polymerases that employ α-carboxyacyl-CoAs as extender substrates. This enzyme family contains several catalytic modules, where each module is responsible for a single round of polyketide chain extension. Although PKS modules typically use malonyl-CoA or methylmalonyl-CoA for chain elongation, many other malonyl-CoA analogues are used to diversify polyketide structures in nature. Previously, we developed a method to alter an extension substrate of a given module by exchanging an acyltransferase (AT) domain while maintaining protein folding. Here, we report in vitro polyketide biosynthesis by 13 PKSs (the wild-type PKS and 12 AT-exchanged PKSs with unusual ATs) and 14 extender substrates. Our ∼200 in vitro reactions resulted in 13 structurally different polyketides, including several polyketides that have not been reported. In some cases, AT-exchanged PKSs produced target polyketides by >100-fold compared to the wild-type PKS. These data also indicate that most unusual AT domains do not incorporate malonyl-CoA and methylmalonyl-CoA but incorporate various rare extender substrates that are equal to in size or slightly larger than natural substrates. We developed a computational workflow to predict the approximate AT substrate range based on active site volumes to support the selection of ATs. These results greatly enhance our understanding of rare AT domains and demonstrate the benefit of using the proposed PKS engineering strategy to produce novel chemicals in vitro.

Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of Cα atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.

Rhamnogalacturonan I (RGI) is a structurally complex pectic polysaccharide with a backbone of alternating rhamnose and galacturonic acid residues substituted with arabinan and galactan side chains. Galactan synthase 1 (GalS1) transfers galactose and arabinose to either extend or cap the β-1,4-galactan side chains of RGI, respectively. Here we report the structure of GalS1 from Populus trichocarpa, showing a modular protein consisting of an N-terminal domain that represents the founding member of a new family of carbohydrate-binding module, CBM95, and a C-terminal glycosyltransferase family 92 (GT92) catalytic domain that adopts a GT-A fold. GalS1 exists as a dimer in vitro, with stem domains interacting across the chains in a 'handshake' orientation that is essential for maintaining stability and activity. In addition to understanding the enzymatic mechanism of GalS1, we gained insight into the donor and acceptor substrate binding sites using deep evolutionary analysis, molecular simulations and biochemical studies. Combining all the results, a mechanism for GalS1 catalysis and a new model for pectic galactan side-chain addition are proposed.

In macromolecular crystallographic structure refinement, ligands present challenges for the generation of geometric restraints due to their large chemical variability, their possible novel nature and their specific interaction with the binding pocket of the protein. Quantum-mechanical approaches are useful for providing accurate ligand geometries, but can be plagued by the number of minima in flexible molecules. In an effort to avoid these issues, the Quantum Mechanical Restraints (QMR) procedure optimizes the ligand geometry in situ, thus accounting for the influence of the macromolecule on the local energy minima of the ligand. The optimized ligand geometry is used to generate target values for geometric restraints during the crystallographic refinement. As demonstrated using a sample of >2330 ligand instances in >1700 protein-ligand models, QMR restraints generally result in lower deviations from the target stereochemistry compared with conventionally generated restraints. In particular, the QMR approach provides accurate torsion restraints for ligands and other entities.

We described a mass spectrometry-based assay to rapidly quantify the production of primary alcohols directly from cell cultures. This novel assay used the combination of TEMPO-based oxidation chemistry and oxime ligation, followed by product analysis based on Nanostructure-Initiator Mass Spectrometry. This assay enables quantitative monitor both C5 to C18 alcohols as well as glucose and gluconate in the growth medium to support strain characterization and optimization. We find that this assay yields similar results to gas chromatography for isoprenol production but required much less acquisition time per sample. We applied this assay to gain new insights into P. Putida's utilization of alcohols and find that this strain largely could not grow on heptanol and octanol.

Plate-based proteomic sample preparation offers a solution to the large sample throughput demands in the biotechnology field where hundreds or thousands of engineered microbes are constructed for testing is routine. Meanwhile, sample preparation methods that work efficiently on broader microbial groups are desirable for new applications of proteomics in other fields, such as microbial communities. Here, we detail a step-by-step protocol that consists of cell lysis in an alkaline chemical buffer (NaOH/SDS) followed by protein precipitation with high-ionic strength acetone in 96-well format. The protocol works for a broad range of microbes (e.g., Gram-negative bacteria, Gram-positive bacteria, non-filamentous fungi) and the resulting proteins are ready for tryptic digestion for bottom-up quantitative proteomic analysis without the need for desalting column cleanup. The yield of protein using this protocol increases linearly with respect to the amount of starting biomass from 0.5-2.0 OD*mL of cells. By using a bench-top automated liquid dispenser, a cost-effective and environmentally-friendly option to eliminating pipette tips and reducing reagent waste, the protocol takes approximately 30 minutes to extract protein from 96 samples. Tests on mock mixtures showed expected results that the biomass composition structure is in close agreement with the experimental design. Lastly, we applied the protocol for the composition analysis of a synthetic community of environmental isolates grown on two different media. This protocol has been developed to facilitate rapid, low-variance sample preparation of hundreds of samples and allow flexibility for future protocol development.

Direct-acting antivirals are needed to combat coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The papain-like protease (PLpro) domain of Nsp3 from SARS-CoV-2 is essential for viral replication. In addition, PLpro dysregulates the host immune response by cleaving ubiquitin and interferon-stimulated gene 15 protein from host proteins. As a result, PLpro is a promising target for inhibition by small-molecule therapeutics. Here we design a series of covalent inhibitors by introducing a peptidomimetic linker and reactive electrophile onto analogs of the noncovalent PLpro inhibitor GRL0617. The most potent compound inhibits PLpro with kinact/KI = 9,600 M-1 s-1, achieves sub-μM EC50 values against three SARS-CoV-2 variants in mammalian cell lines, and does not inhibit a panel of human deubiquitinases (DUBs) at >30 μM concentrations of inhibitor. An X-ray co-crystal structure of the compound bound to PLpro validates our design strategy and establishes the molecular basis for covalent inhibition and selectivity against structurally similar human DUBs. These findings present an opportunity for further development of covalent PLpro inhibitors.

Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps.

Oligomerization is a core structural feature that defines the form and function of many proteins. Most proteins form molecular complexes; however, there remains a dearth of diversity-driven structural studies investigating the evolutionary trajectory of these assemblies. Ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO) is one such enzyme that adopts multiple assemblies, although the origins and distribution of its different oligomeric states remain cryptic. Here, we retrace the evolution of ancestral and extant form II RuBisCOs, revealing a complex and diverse history of oligomerization. We structurally characterize a newly discovered tetrameric RuBisCO, elucidating how solvent-exposed surfaces can readily adopt new interactions to interconvert or give rise to new oligomeric states. We further use these principles to engineer and demonstrate how changes in oligomerization can be mediated by relatively few mutations. Our findings yield insight into how structural plasticity may give rise to new oligomeric states.

PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.

The considerable utility of glycoside phosphorylases (GPs) has led to substantial efforts over the past two decades to expand the breadth of known GP activities. Driven largely by the increase of available genomic DNA sequence data, the gap between the number of sequences in the carbohydrate active enzyme database (CAZy DB) and its functionally characterized members continues to grow. This wealth of sequence data presented an exciting opportunity to explore the ever-expanding CAZy DB to discover new GPs with never-before-described functionalities. Utilizing an in silico sequence analysis of CAZy family GH94, we discovered and then functionally and structurally characterized the new GP β-1,3-N-acetylglucosaminide phosphorylase. This new GP was sourced from the genome of the cell-wall-less Mollicute bacterium, Acholeplasma laidlawii and was found to synthesize β-1,3-linked N-acetylglucosaminide linkages. The resulting poly-β-1,3-N-acetylglucosamine represents a new, previously undescribed biopolymer that completes the set of possible β-linked GlcNAc homopolysaccharides together with chitin (β-1,4) and PNAG (poly-β-1,6-N-acetylglucosamine). The new biopolymer was denoted acholetin, a combination of the genus Acholeplasma and the polysaccharide chitin, and the new GP was thus denoted acholetin phosphorylase (AchP). Use of the reverse phosphorolysis action of AchP provides an efficient method to enzymatically synthesize acholetin, which is a new biodegradable polymeric material.

BackgroundPlant cell walls are interwoven structures recalcitrant to degradation. Native and adapted microbiomes can be particularly effective at plant cell wall deconstruction. Although most understanding of biological cell wall deconstruction has been obtained from isolates, cultivated microbiomes that break down cell walls have emerged as new sources for biotechnologically relevant microbes and enzymes. These microbiomes provide a unique resource to identify key interacting functional microbial groups and to guide the design of specialized synthetic microbial communities.ResultsTo establish a system assessing comparative microbiome performance, parallel microbiomes were cultivated on sorghum (Sorghum bicolor L. Moench) from compost inocula. Biomass loss and biochemical assays indicated that these microbiomes diverged in their ability to deconstruct biomass. Network reconstructions from gene expression dynamics identified key groups and potential interactions within the adapted sorghum-degrading communities, including Actinotalea, Filomicrobium, and Gemmatimonadetes populations. Functional analysis demonstrated that the microbiomes proceeded through successive stages that are linked to enzymes that deconstruct plant cell wall polymers. The combination of network and functional analysis highlighted the importance of cellulose-degrading Actinobacteria in differentiating the performance of these microbiomes.ConclusionsThe two-tier cultivation of compost-derived microbiomes on sorghum led to the establishment of microbiomes for which community structure and performance could be assessed. The work reinforces the observation that subtle differences in community composition and the genomic content of strains may lead to significant differences in community performance. Video Abstract.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), threatens global public health. The world needs rapid development of new antivirals and vaccines to control the current pandemic and to control the spread of the variants. Among the proteins synthesized by the SARS-CoV-2 genome, main protease (Mpro also known as 3CLpro) is a primary drug target, due to its essential role in maturation of the viral polyproteins. In this study, we provide crystallographic evidence, along with some binding assay data, that three clinically approved anti hepatitis C virus drugs and two other drug-like compounds covalently bind to the Mpro Cys145 catalytic residue in the active site. Also, molecular docking studies can provide additional insight for the design of new antiviral inhibitors for SARS-CoV-2 using these drugs as lead compounds. One might consider derivatives of these lead compounds with higher affinity to the Mpro as potential COVID-19 therapeutics for further testing and possibly clinical trials.

Manual proteomic sample preparation methods limit sample throughput and often lead to poor data quality when thousands of samples must be analyzed. Automated liquid handler systems are increasingly used to overcome these issues for many of the sample preparation steps. Here, we detail a step-by-step protocol to prepare samples for bottom-up proteomic analysis for Gram-negative bacterial and fungal cells. The full modular protocol consists of three optimized protocols to: (A) lyse Gram-negative bacteria and fungal cells; (B) quantify the amount of protein extracted; and (C) normalize the amount of protein and set up tryptic digestion. These protocols have been developed to facilitate rapid, low variance sample preparation of hundreds of samples, be easily implemented on widely-available Beckman-Coulter Biomek automated liquid handlers, and allow flexibility for future protocol development. By using this workflow 50 micrograms of protein from 96 samples can be prepared for tryptic digestion in under an hour. We validate these protocols by analyzing 47 Pseudomonas putida and Rhodosporidium toruloides samples and show that this modular workflow provides robust, reproducible proteomic samples for high-throughput applications. The expected results from these protocols are 94 peptide samples from Gram-negative bacterial and fungal cells prepared for bottom-up quantitative proteomic analysis without the need for desalting column cleanup and with protein relative quantity variance (CV%) below 15%.

We present a droplet-based microfluidic system that enables CRISPR-based gene editing and high-throughput screening on a chip. The microfluidic device contains a 10 × 10 element array, and each element contains sets of electrodes for two electric field-actuated operations: electrowetting for merging droplets to mix reagents and electroporation for transformation. This device can perform up to 100 genetic modification reactions in parallel, providing a scalable platform for generating the large number of engineered strains required for the combinatorial optimization of genetic pathways and predictable bioengineering. We demonstrate the system's capabilities through the CRISPR-based engineering of two test cases: (1) disruption of the function of the enzyme galactokinase (galK) in E. coli and (2) targeted engineering of the glutamine synthetase gene (glnA) and the blue-pigment synthetase gene (bpsA) to improve indigoidine production in E. coli.

BackgroundLignin peroxidases catalyze a variety of reactions, resulting in cleavage of both β-O-4' ether bonds and C-C bonds in lignin, both of which are essential for depolymerizing lignin into fragments amendable to biological or chemical upgrading to valuable products. Studies of the specificity of lignin peroxidases to catalyze these various reactions and the role reaction conditions such as pH play have been limited by the lack of assays that allow quantification of specific bond-breaking events. The subsequent theoretical understanding of the underlying mechanisms by which pH modulates the activity of lignin peroxidases remains nascent. Here, we report on combined experimental and theoretical studies of the effect of pH on the enzyme-catalyzed cleavage of β-O-4' ether bonds and of C-C bonds by a lignin peroxidase isozyme H8 from Phanerochaete chrysosporium and an acid stabilized variant of the same enzyme.ResultsUsing a nanostructure initiator mass spectrometry assay that provides quantification of bond breaking in a phenolic model lignin dimer we found that catalysis of degradation of the dimer to products by an acid-stabilized variant of lignin peroxidase isozyme H8 increased from 38.4% at pH 5 to 92.5% at pH 2.6. At pH 2.6, the observed product distribution resulted from 65.5% β-O-4' ether bond cleavage, 27.0% Cα-C1 carbon bond cleavage, and 3.6% Cα-oxidation as by-product. Using ab initio molecular dynamic simulations and climbing-image Nudge Elastic Band based transition state searches, we suggest the effect of lower pH is via protonation of aliphatic hydroxyl groups under which extremely acidic conditions resulted in lower energetic barriers for bond-cleavages, particularly β-O-4' bonds.ConclusionThese coupled experimental results and theoretical explanations suggest pH is a key driving force for selective and efficient lignin peroxidase isozyme H8 catalyzed depolymerization of the phenolic lignin dimer and further suggest that engineering of lignin peroxidase isozyme H8 and other enzymes involved in lignin depolymerization should include targeting stability at low pH.

With the advent of the resolution revolution in cryoelectron microscopy (cryo-EM), low-resolution refinement is common, and likewise increases the need for a reliable force field. Here, we report on the incorporation of the OPLS3e force field with the VSGB2.1 solvation model in the structure determination package Phenix. Our results show significantly improved structure quality and reduced ligand strain at lower resolution for X-ray refinement. For refinement of cryo-EM-based structures, we find comparable quality structures, goodness-of-fit, and reduced ligand strain. We also show how structure quality and ligand strain are related to the map-model cross-correlation as a function of data weight, and how that can detect overfitting. Signs of overfitting are found in over half of our cryo-EM dataset, which can be remedied by a re-refinement at a lower data weight. Finally, a start-to-end script for refining structures with Phenix/OPLS3e is available in the Schrödinger 2020-3 distribution.

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.

Lignocellulosic biomass is composed of three major biopolymers: cellulose, hemicellulose and lignin. Analytical tools capable of quickly detecting both glycan and lignin deconstruction are needed to support the development and characterization of efficient enzymes/enzyme cocktails. Previously we have described nanostructure-initiator mass spectrometry-based assays for the analysis of glycosyl hydrolase and most recently an assay for lignin modifying enzymes. Here we integrate these two assays into a single multiplexed assay against both classes of enzymes and use it to characterize crude commercial enzyme mixtures. Application of our multiplexed platform based on nanostructure-initiator mass spectrometry enabled us to characterize crude mixtures of laccase enzymes from fungi Agaricus bisporus (Ab) and Myceliopthora thermophila (Mt) revealing activity on both carbohydrate and aromatic substrates. Using time-series analysis we determined that crude laccase from Ab has the higher GH activity and that laccase from Mt has the higher activity against our lignin model compound. Inhibitor studies showed a significant reduction in Mt GH activity under low oxygen conditions and increased activities in the presence of vanillin (common GH inhibitor). Ultimately, this assay can help to discover mixtures of enzymes that could be incorporated into biomass pretreatments to deconstruct diverse components of lignocellulosic biomass.

Using single-particle electron cryo-microscopy (cryo-EM), it is possible to obtain multiple reconstructions showing the 3D structures of proteins imaged as a mixture. Here, it is shown that automatic map interpretation based on such reconstructions can be used to create atomic models of proteins as well as to match the proteins to the correct sequences and thereby to identify them. This procedure was tested using two proteins previously identified from a mixture at resolutions of 3.2 Å, as well as using 91 deposited maps with resolutions between 2 and 4.5 Å. The approach is found to be highly effective for maps obtained at resolutions of 3.5 Å and better, and to have some utility at resolutions as low as 4 Å.

The functionalization of terpenes using cytochrome P450 enzymes is a versatile route to the production of useful derivatives that can be further converted to value-added products. Many terpenes are hydrophobic and volatile making their availability as a substrate for P450 enzymes significantly limited during microbial production. In this study, we developed a strategy to improve the accessibility of terpene molecules for the P450 reaction by linking terpene synthase and P450 together. As a model system, fusion proteins of 1,8-cineole synthase (CS) and P450cin were investigated and it showed an improved hydroxylation of the monoterpenoid 1,8-cineole up to 5.4-fold. Structural analysis of the CS-P450cin fusion proteins by SEC-SAXS indicated a dimer formation with preferred orientations of the active sites of the two domains. We also applied the enzyme fusion strategy to the oxidation of a sesquiterpene epi-isozizaene and the fusion enzymes significantly improved albaflavenol production in engineered E. coli. From the analysis of positive and negative examples of the fusion strategy, we proposed key factors in structure-based prediction and evaluation of fusion enzymes. Developing fusion enzymes for terpene synthase and P450 presents an efficient strategy toward oxidation of hydrophobic terpene compounds. This strategy could be widely applicable to improve the biosynthetic titer of the functionalized products from hydrophobic terpene intermediates.

This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.

The field of electron cryomicroscopy (cryo-EM) has advanced quickly in recent years as the result of numerous technological and methodological developments. This has led to an increase in the number of atomic structures determined using this method. Recently, several tools for the analysis of cryo-EM data and models have been developed within the Phenix software package, such as phenix.real_space_refine for the refinement of atomic models against real-space maps. Also, new validation metrics have been developed for low-resolution cryo-EM models. To understand the quality of deposited cryo-EM structures and how they might be improved, models deposited in the Protein Data Bank that have map resolutions of better than 5 Å were automatically re-refined using current versions of Phenix tools. The results are available on a publicly accessible web page (https://cci.lbl.gov/ceres). The implementation of a Cryo-EM Re-refinement System (CERES) for the improvement of models deposited in the wwPDB, and the results of the re-refinements, are described. Based on these results, contents are proposed for a `cryo-EM Table 1', which summarizes experimental details and validation metrics in a similar way to `Table 1' in crystallography. The consistent use of robust metrics for the evaluation of cryo-EM models and data should accompany every structure deposition and be reported in scientific publications.

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

Light-driven oxidation of water to molecular oxygen is catalyzed by the oxygen-evolving complex (OEC) in Photosystem II (PS II). This multi-electron, multi-proton catalysis requires the transport of two water molecules to and four protons from the OEC. A high-resolution 1.89 Å structure obtained by averaging all the S states and refining the data of various time points during the S2 to S3 transition has provided better visualization of the potential pathways for substrate water insertion and proton release. Our results indicate that the O1 channel is the likely water intake pathway, and the Cl1 channel is the likely proton release pathway based on the structural rearrangements of water molecules and amino acid side chains along these channels. In particular in the Cl1 channel, we suggest that residue D1-E65 serves as a gate for proton transport by minimizing the back reaction. The results show that the water oxidation reaction at the OEC is well coordinated with the amino acid side chains and the H-bonding network over the entire length of the channels, which is essential in shuttling substrate waters and protons.

Crystallographic refinement of macromolecular structures relies on stereochemical restraints to mitigate the typically poor data-to-parameter ratio. For proteins, each amino acid has a unique set of geometry restraints which represent stereochemical information such as bond lengths, valence angles, torsion angles, dihedrals and planes. It has been shown that the geometry in refined structures can differ significantly from that present in libraries; for example, it was recently reported that the guanidinium moiety in arginine is not symmetric. In this work, the asymmetry of the Nϵ-Cζ-Nη1 and Nϵ-Cζ-Nη2 valence angles in the guanidinium moiety is confirmed. In addition, it was found that the Cδ atom can deviate significantly (more than 20°) from the guanidinium plane. This requires the relaxation of the planar restraint for the Cδ atom, as it otherwise causes the other atoms in the group to compensate by distorting the guanidinium core plane. A new set of restraints for the arginine side chain have therefore been formulated, and are available in the software package Phenix, that take into account the asymmetry of the group and the planar deviation of the Cδ atom. This is an example of the need to regularly revisit the geometric restraint libraries used in macromolecular refinement so that they reflect the best knowledge of the structural chemistry of their components available at the time.

Ramachandran plots report the distribution of the (ϕ, ψ) torsion angles of the protein backbone and are one of the best quality metrics of experimental structure models. Typically, validation software reports the number of residues belonging to "outlier," "allowed," and "favored" regions. While "zero unexplained outliers" can be considered the current "gold standard," this can be misleading if deviations from expected distributions are not considered. We revisited the Ramachandran Z score (Rama-Z), a quality metric introduced more than two decades ago but underutilized. We describe a reimplementation of the Rama-Z score in the Computational Crystallography Toolbox along with an algorithm to estimate its uncertainty for individual models; final implementations are available in Phenix and PDB-REDO. We discuss the interpretation of the Rama-Z score and advocate including it in the validation reports provided by the Protein Data Bank. We also advocate reporting it alongside the outlier/allowed/favored counts in structural publications.

Bacillus sp. strain EB106-08-02-XG196 was isolated from a high-nitrate- and heavy metal-contaminated site at the Oak Ridge Reservation in Tennessee. We report the draft genome sequence of this strain to provide insights into the genomic basis for surviving in this unique environment.