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

We report a fully automated procedure for the optimization and interpretation of reconstructions from cryo-electron microscopy (cryo-EM) data, available in Phenix as phenix.map_to_model. We applied our approach to 476 datasets with resolution of 4.5 Å or better, including reconstructions of 47 ribosomes and 32 other protein–RNA complexes. The median fraction of residues in the deposited structures reproduced automatically was 71% for reconstructions determined at resolutions of 3 Å or better and 47% for those at resolutions worse than 3 Å.

Indigo is an ancient dye uniquely capable of producing the signature tones in blue denim; however, the dyeing process requires chemical steps that are environmentally damaging. We describe a sustainable dyeing strategy that not only circumvents the use of toxic reagents for indigo chemical synthesis but also removes the need for a reducing agent for dye solubilization. This strategy utilizes a glucose moiety as a biochemical protecting group to stabilize the reactive indigo precursor indoxyl to form indican, preventing spontaneous oxidation to crystalline indigo during microbial fermentation. Application of a β-glucosidase removes the protecting group from indican, resulting in indigo crystal formation in the cotton fibers. We identified the gene coding for the glucosyltransferase PtUGT1 from the indigo plant Polygonum tinctorium and solved the structure of PtUGT1. Heterologous expression of PtUGT1 in Escherichia coli supported high indican conversion, and biosynthesized indican was used to dye cotton swatches and a garment.

Doc number: 14 Abstract Background: Lignin is often overlooked in the valorization of lignocellulosic biomass, but lignin-based materials and chemicals represent potential value-added products for biorefineries that could significantly improve the economics of a biorefinery. Fluctuating crude oil prices and changing fuel specifications are some of the driving factors to develop new technologies that could be used to convert polymeric lignin into low molecular weight lignin and or monomeric aromatic feedstocks to assist in the displacement of the current products associated with the conversion of a whole barrel of oil. We present an approach to produce these chemicals based on the selective breakdown of lignin during ionic liquid pretreatment. Results: The lignin breakdown products generated are found to be dependent on the starting biomass, and significant levels were generated on dissolution at 160°C for 6 hrs. Guaiacol was produced on dissolution of biomass and technical lignins. Vanillin was produced on dissolution of kraft lignin and eucalytpus. Syringol and allyl guaiacol were the major products observed on dissolution of switchgrass and pine, respectively, whereas syringol and allyl syringol were obtained by dissolution of eucalyptus. Furthermore, it was observed that different lignin-derived products could be generated by tuning the process conditions. Conclusions: We have developed an ionic liquid based process that depolymerizes lignin and converts the low molecular weight lignin fractions into a variety of renewable chemicals from biomass. The generated chemicals (phenols, guaiacols, syringols, eugenol, catechols), their oxidized products (vanillin, vanillic acid, syringaldehyde) and their easily derivatized hydrocarbons (benzene, toluene, xylene, styrene, biphenyls and cyclohexane) already have relatively high market value as commodity and specialty chemicals, green building materials, nylons, and resins.

The cryo-electron microscopy structure of the ten-subunit human transcription factor IIH, revealing the molecular architecture of the TFIIH core complex, the detailed structures of its constituent XPB and XPD ATPases, and how the core and kinase subcomplexes of TFIIH are connected. Structure of a general transcription factor Transcription factor IIH (TFIIH) is part of the general transcriptional machinery required to initiate eukaryotic gene transcription by RNA polymerase II (Pol II). TFIIH is also required for nucleotide excision DNA repair, and mutations in certain subunits occur in human genetic diseases. Here, Eva Nogales and colleagues have determined a cryo-electron microscopy structure of the 10-subunit human TFIIH, revealing an architecture dominated by the two ATPase subunits XPB and XPD. Disease-linked mutations can be mapped onto the structure. Comparisons between the structure of free TFIIH and that of TFIIH complexed with the Pol II pre-initiation complex provide initial insight into conformational rearrangements and a further step towards a complete mechanistic understanding of transcription initiation. Human transcription factor IIH (TFIIH) is part of the general transcriptional machinery required by RNA polymerase II for the initiation of eukaryotic gene transcription 1 . Composed of ten subunits that add up to a molecular mass of about 500 kDa, TFIIH is also essential for nucleotide excision repair 1 . The seven-subunit TFIIH core complex formed by XPB, XPD, p62, p52, p44, p34, and p8 is competent for DNA repair 2 , while the CDK-activating kinase subcomplex, which includes the kinase activity of CDK7 as well as the cyclin H and MAT1 subunits, is additionally required for transcription initiation 1 , 2 . Mutations in the TFIIH subunits XPB, XPD, and p8 lead to severe premature ageing and cancer propensity in the genetic diseases xeroderma pigmentosum, Cockayne syndrome, and trichothiodystrophy, highlighting the importance of TFIIH for cellular physiology 3 . Here we present the cryo-electron microscopy structure of human TFIIH at 4.4 Å resolution. The structure reveals the molecular architecture of the TFIIH core complex, the detailed structures of its constituent XPB and XPD ATPases, and how the core and kinase subcomplexes of TFIIH are connected. Additionally, our structure provides insight into the conformational dynamics of TFIIH and the regulation of its activity.

Electron cryomicroscopy (cryo-EM) has been used to determine the atomic coordinates (models) from density maps of biological assemblies. These models can be assessed by their overall fit to the experimental data and stereochemical information. However, these models do not annotate the actual density values of the atoms nor their positional uncertainty. Here, we introduce a computational procedure to derive an atomic model from a cryo-EM map with annotated metadata. The accuracy of such a model is validated by a faithful replication of the experimental cryo-EM map computed using the coordinates and associated metadata. The functional interpretation of any structural features in the model and its utilization for future studies can be made in the context of its measure of uncertainty. We applied this protocol to the 3.3-Å map of the mature P22 bacteriophage capsid, a large and complex macromolecular assembly. With this protocol, we identify and annotate previously undescribed molecular interactions between capsid subunits that are crucial to maintain stability in the absence of cementing proteins or cross-linking, as occur in other bacteriophages.

Single-structure models derived from X-ray data do not adequately account for the inherent, functionally important dynamics of protein molecules. We generated ensembles of structures by time-averaged refinement, where local molecular vibrations were sampled by molecular-dynamics (MD) simulation whilst global disorder was partitioned into an underlying overall translation–libration–screw (TLS) model. Modeling of 20 protein datasets at 1.1–3.1 Å resolution reduced cross-validated Rfree values by 0.3–4.9%, indicating that ensemble models fit the X-ray data better than single structures. The ensembles revealed that, while most proteins display a well-ordered core, some proteins exhibit a ‘molten core’ likely supporting functionally important dynamics in ligand binding, enzyme activity and protomer assembly. Order–disorder changes in HIV protease indicate a mechanism of entropy compensation for ordering the catalytic residues upon ligand binding by disordering specific core residues. Thus, ensemble refinement extracts dynamical details from the X-ray data that allow a more comprehensive understanding of structure–dynamics–function relationships. It has been clear since the early days of structural biology in the late 1950s that proteins and other biomolecules are continually changing shape, and that these changes have an important influence on both the structure and function of the molecules. X-ray diffraction can provide detailed information about the structure of a protein, but only limited information about how its structure fluctuates over time. Detailed information about the dynamic behaviour of proteins is essential for a proper understanding of a variety of processes, including catalysis, ligand binding and protein–protein interactions, and could also prove useful in drug design. Currently most of the X-ray crystal structures in the Protein Data Bank are ‘snap-shots’ with limited or no information about protein dynamics. However, X-ray diffraction patterns are affected by the dynamics of the protein, and also by distortions of the crystal lattice, so three-dimensional (3D) models of proteins ought to take these phenomena into account. Molecular-dynamics (MD) computer simulations transform 3D structures into 4D ‘molecular movies’ by predicting the movement of individual atoms. Combining MD simulations with crystallographic data has the potential to produce more realistic ensemble models of proteins in which the atomic fluctuations are represented by multiple structures within the ensemble. Moreover, in addition to improved structural information, this process—which is called ensemble refinement—can provide dynamical information about the protein. Earlier attempts to do this ran into problems because the number of model parameters needed was greater than the number of observed data points. Burnley et al. now overcome this problem by modelling local molecular vibrations with MD simulations and, at the same time, using a course-grain model to describe global disorder of longer length scales. Ensemble refinement of high-resolution X-ray diffraction datasets for 20 different proteins from the Protein Data Bank produced a better fit to the data than single structures for all 20 proteins. Ensemble refinement also revealed that 3 of the 20 proteins had a ‘molten core’, rather than the well-ordered residues core found in most proteins: this is likely to be important in various biological functions including ligand binding, filament formation and enzymatic function. Burnley et al. also showed that a HIV enzyme underwent an order–disorder transition that is likely to influence how this enzyme works, and that similar transitions might influence the interactions between the small-molecule drug Imatinib (also known as Gleevec) and the enzymes it targets. Ensemble refinement could be applied to the majority of crystallography data currently being collected, or collected in the past, so further insights into the properties and interactions of a variety of proteins and other biomolecules can be expected.

Branched five carbon (C 5 ) alcohols are attractive targets for microbial production due to their desirable fuel properties and importance as platform chemicals. In this study, we engineered a heterologous isoprenoid pathway in E. coli for the high-yield production of 3-methyl-3-buten-1-ol, 3-methyl-2-buten-1-ol and 3-methyl-1-butanol, three C 5 alcohols that serve as potential biofuels. We first constructed a pathway for 3-methyl-3-buten-1-ol, where metabolite profiling identified NudB, a promiscuous phosphatase, as a likely pathway bottleneck. We achieved a 60% increase in the yield of 3-methyl-3-buten-1-ol by engineering the Shine-Dalgarno sequence of nudB , which increased protein levels by 9-fold and reduced isopentenyl diphosphate (IPP) accumulation by 4-fold. To further optimize the pathway, we adjusted mevalonate kinase (MK) expression and investigated MK enzymes from alternative microbes such as Methanosarcina mazei . Next, we expressed a fusion protein of IPP isomerase and the phosphatase (Idi1~NudB) along with a reductase (NemA) to diversify production to 3-methyl-2-buten-1-ol and 3-methyl-1-butanol. Finally, we used an oleyl alcohol overlay to improve alcohol recovery, achieving final titers of 2.23 g/L of 3-methyl-3-buten-1-ol (~70% of pathway-dependent theoretical yield), 150 mg/L of 3-methyl-2-buten-1-ol and 300 mg/L of 3-methyl-1-butanol.

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 (M pro also known as 3CL pro ) 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 M pro 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 M pro as potential COVID-19 therapeutics for further testing and possibly clinical trials.

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical data, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. Here we present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 machine learned interatomic potential (MLIP) mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data. Notably, AQuaRef aids in determining proton positions, as illustrated in the challenging case of short hydrogen bonds in the parkinsonism-associated human protein DJ-1 and its bacterial homolog YajL. AQuaRef employs machine learning to refine protein structures from cryo-EM and X-ray data in Phenix. It achieves quantum-level precision, improving model geometry and fit to the data while reducing overfitting.