Biochemical and in silico characterization of glycosyltransferases from red sweet cherry (Prunus avium L.) reveals their broad specificity toward phenolic substrates.

Polyphenolic compounds are a class of phytonutrients that play important roles in plants and contribute to human health when incorporated into our diet through fruit consumption. A large proportion occur as glycoconjugates but the enzymes responsible for their glycosylation are poorly characterized. Here, we report the biochemical and structural characterization of two glycosyltransferases from sweet cherry named PaUGT1 and PaUGT2. Both are promiscuous glucosyltransferases active on diverse anthocyanidins and flavonols, as well as phenolic acids in the case of PaUGT1. They also exhibit weaker galactosyltransferase activity. The expression of the gene encoding PaUGT1, the most active of the two proteins, follows anthocyanin accumulation during fruit ripening, suggesting that this enzyme is the primary glycosyltransferase involved in flavonoid glycosylation in sweet cherry. It can potentially be used to synthesize diverse glycoconjugates of flavonoids for integration into bioactive formulations, and for generating new fruit cultivars with enhanced health-promoting properties using breeding methods.

Emerging insight of whole genome sequencing coupled with protein structure prediction into the pyrazinamide-resistance signature of Mycobacterium tuberculosis.

Pyrazinamide (PZA) is considered to be a pivotal drug to shorten the treatment of both drug-susceptible and drug-resistant tuberculosis, but its use is challenged by the reliability of drug-susceptibility testing (DST). PZA resistance in Mycobacterium tuberculosis (MTB) is relevant to the amino acid substitution of pyrazinamidase that is responsible for the conversion of PZA to active pyrazinoic acid (POA). The single nucleotide variants (SNVs) within ribosomal protein S1 (rpsA) or aspartate decarboxylase (panD), the binding targets of POA, has been reported to drive the PZA-resistance signature of MTB. In this study, whole genome sequencing (WGS) was used to identify SNVs within the pncA, rpsA and panD genes in 100 clinical MTB isolates associated with DST results for PZA. The potential influence of high-confidence, interim-confidence or emerging variants on the interplay between target genes and PZA or POA was simulated computationally, and predicted with a protein structure modelling approach. The DST results showed weak agreement with the identification of high-confidence variants within the pncA gene (Cohen's kappa coefficient=0.58), the analytic results of WGS coupled with protein structure modelling on pncA mutants (Cohen's kappa coefficient=0.524) or related genes (Cohen's kappa coefficient=0.504). Taken together, these results suggest the practicable application of a genotypic-coupled bioinformatic approach to manage PZA-containing regimens for patients with MTB.

Transcriptional and protein structural characterization of homogentisate phytyltransferase genes in barley, wheat, and oat.

BACKGROUND: Homogentisate phytyltransferase (HPT) is the critical enzyme for the biosynthesis of tocopherols (vitamin E), which are the major lipid-soluble antioxidants and help plants adapt to various stress conditions. HPT is generally strictly conserved in various plant genomes; however, a divergent lineage HPT2 was identified recently in some Triticeae species. The molecular function and transcriptional profiles of HPT2 remain to be characterized. RESULTS: In this study, we performed comprehensive transcriptome data mining of HPT1 and HPT2 in different tissues and stages of barley (Hordeum vulgare), wheat (Triticum aestivum), and oat (Avena sativa), followed by qRT-PCR experiments on HPT1 and HPT2 in different tissues of barley and wheat. We found that the common HPT1 genes (HvHPT1, TaHPT1s, and AsHPT1s) displayed a conserved transcriptional pattern in the three target species and were universally transcribed in various tissues, with a notable preference in leaf. In contrast, HPT2 genes (HvHPT2, TaHPT2, and AsHPT2) were specifically transcribed in spike (developmentally up-regulated) and shoot apex tissues, displaying a divergent tissue-specific pattern. Cis-regulatory elements prediction in the promoter region identified common factors related to light-, plant hormone-, low temperature-, drought- and defense- responses in both HPT1s and HPT2s. We observed the transcriptional up-regulation of HvHPT1 and HvHPT2 under various stress conditions, supporting their conserved function in environmental adaption. We detected a clear, relaxed selection pressure in the HPT2 lineage, consistent with the predicted evolution pattern following gene duplication. Protein structural modelling and substrate docking analyses identified putative catalytic amino acid residues for HvHPT1 and HvHPT2, which are strictly conserved and consistent with their function in vitamin E biosynthesis. CONCLUSIONS: We confirmed the presence of two lineages of HPT in Triticeae and Aveninae, including hexaploid oat, and characterized their transcriptional profiles based on transcriptome and qRT-PCR data. HPT1s were ubiquitously transcribed in various tissues, whilst HPT2s were highly expressed in specific stages and tissue. The active transcription of HPT2s, together with its conserved cis-elements and protein structural features, support HPT2s' role in tocopherol production in Triticeae. This study is the first protein structural analysis on the membrane-bound plant HPTs and provides valuable insights into its catalytic mechanism.

Identification and characterisation of MdUGT78T2 as a galactosyltransferase with dual activity on flavonol and anthocyanidin substrates in red-skinned apple fruit (Malus domestica L.).

Anthocyanidin and flavonol glycosides have been linked to the health-promoting effects associated with apple consumption. However, very few enzymes involved in flavonoid glycosylation have been characterised to date. Here, we present the identification and phylogenetic analysis of 234 putative glycosyltransferases involved in flavonoid biosynthesis, and detail the biochemical and structural characterisation of MdUGT78T2 as a strict galactosyltransferase involved in the formation of quercetin-3-O-galactoside and cyanidin-3-O-galactoside, the major glycoconjugates of flavonoids in apple. The enzyme is also active on other flavonoids but with a lower catalytic efficiency. Our data, complemented with gene expression analysis suggest that MdUGT78T2 synthesises the glycoconjugates at both the early and late stages of fruit development. This newly discovered type of catalytic activity can potentially be exploited for in vitro modification of flavonoids to increase their stability in food products and to modify apple fruits and other commercial crops through breeding approaches to enhance their health benefits.

Protein structure dynamics by crosslinking mass spectrometry.

Crosslinking mass spectrometry captures protein structures in solution. The crosslinks reveal spatial proximities as distance restraints, but do not easily reveal which of these restraints derive from the same protein conformation. This superposition can be reduced by photo-crosslinking, and adding information from protein structure models, or quantitative crosslinking reveals conformation-specific crosslinks. As a consequence, crosslinking MS has proven useful already in the context of multiple dynamic protein systems. We foresee a breakthrough in the resolution and scale of studying protein dynamics when crosslinks are used to guide deep-learning-based protein modelling. Advances in crosslinking MS, such as photoactivatable crosslinking and in-situ crosslinking, will then reveal protein conformation dynamics in the cellular context, at a pseudo-atomic resolution, and plausibly in a time-resolved manner.

An integrated protein structure fitness scoring approach for identifying native-like model structures.

The structural information of a protein is pivotal to comprehend its functions, protein-protein and protein-ligand interactions. There is a widening gap between the number of known protein sequences and that of experimentally determined structures. The protein structure prediction has emerged as an efficient alternative to deliver the reliable structural information of proteins. However, it remains a challenge to identify the best model among the many predicted by one or a few structure prediction methods. Here we report ProFitFun-Meta, a neural network based pure single model scoring method for assessing the quality of predicted model structures by an effective combination structural information of various backbone dihedral angle and residue surface accessibility preferences of amino acid residues with other spatial properties of protein structures. The performance of ProFitFun-Meta was validated and benchmarked against current state-of-the-art methods on the extensive datasets, comprising a Test Dataset (n = 26,604), an External Dataset (n = 40,000), and CASP14 Dataset (n = 1200). The comprehensive performance evaluation of ProFitFun-Meta demonstrated its reliability and efficiency in terms of Spearman's (ρ) and Pearson's (r) correlation coefficients, GDT-TS loss (g), and absolute loss (d). An improved performance over the current state-of-the-art methods and leading performers of CASP14 experiment in quality assessment category demonstrated its potential to become an integral component of computational pipelines for protein modeling and design. The minimal dependencies, high computational efficiency, and portability to various Linux and Windows OS provide an additional edge to ProFitFun-Meta for its easy implementation and applications in various regimes of computational protein folding.

In Silico Characterisation of the Late Embryogenesis Abundant (LEA) Protein Families and Their Role in Desiccation Tolerance in Ramonda serbica Panc.

Ramonda serbica Panc. is an ancient resurrection plant able to survive a long desiccation period and recover metabolic functions upon watering. The accumulation of protective late embryogenesis abundant proteins (LEAPs) is a desiccation tolerance hallmark. To propose their role in R. serbica desiccation tolerance, we structurally characterised LEAPs and evaluated LEA gene expression levels in hydrated and desiccated leaves. By integrating de novo transcriptomics and homologues LEAP domains, 318 R. serbica LEAPs were identified and classified according to their conserved motifs and phylogeny. The in silico analysis revealed that hydrophilic LEA4 proteins exhibited an exceptionally high tendency to form amphipathic α-helices. The most abundant, atypical LEA2 group contained more hydrophobic proteins predicted to fold into the defined globular domains. Within the desiccation-upregulated LEA genes, the majority encoded highly disordered DEH1, LEA1, LEA4.2, and LEA4.3 proteins, while the greatest portion of downregulated genes encoded LEA2.3 and LEA2.5 proteins. While dehydrins might chelate metals and bind DNA under water deficit, other intrinsically disordered LEAPs might participate in forming intracellular proteinaceous condensates or adopt amphipathic α-helical conformation, enabling them to stabilise desiccation-sensitive proteins and membranes. This comprehensive LEAPs structural characterisation is essential to understanding their function and regulation during desiccation aiming at crop drought tolerance improvement.

Epistasis at the SARS-CoV-2 RBD Interface and the Propitiously Boring Implications for Vaccine Escape.

At the time of this writing, December 2021, potential emergence of vaccine escape variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a grave global concern. The interface between the receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein and the host receptor (ACE2) overlap with the binding site of principal neutralizing antibodies (NAb), limiting the repertoire of viable mutations. Nonetheless, variants with multiple mutations in the RBD have rose to dominance. Non-additive, epistatic relationships among RBD mutations are apparent, and assessing the impact of such epistasis on the mutational landscape is crucial. Epistasis can substantially increase the risk of vaccine escape and cannot be completely characterized through the study of the wild type (WT) alone. We employed protein structure modeling using Rosetta to compare the effects of all single mutants at the RBD-NAb and RBD-ACE2 interfaces for the WT, Delta, Gamma, and Omicron variants. Overall, epistasis at the RBD interface appears to be limited and the effects of most multiple mutations are additive. Epistasis at the Delta variant interface weakly stabilizes NAb interaction relative to ACE2 interaction, whereas in the Gamma variant, epistasis more substantially destabilizes NAb interaction. Although a small, systematic trend towards NAb destabilization not observed for Delta or Gamma was detected for Omicron, and despite bearing significantly more RBD mutations, the epistatic landscape of the Omicron variant closely resembles that of Gamma. These results suggest that, although Omicron poses new risks not observed with Delta, structural constraints on the RBD hamper continued evolution towards more complete vaccine escape. The modest ensemble of mutations relative to the WT that are currently known to reduce vaccine efficacy is likely to comprise the majority of all possible escape mutations for future variants, predicting continued efficacy of the existing vaccines.

Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry.

Modeling macromolecular assemblies with restraints from crosslinking mass spectrometry (XL-MS) tends to focus solely on distance violation. Recently, we identified three different modeling features inherent in crosslink data: (1) expected distance between crosslinked residues; (2) violation of the crosslinker's maximum bound; and (3) solvent accessibility of crosslinked residues. Here, we implement these features in a scoring function. cMNXL, and demonstrate that it outperforms the commonlyused crosslink distance violation. We compare the different methods of calculating the distance between crosslinked residues, which shows no significant change in performance when using Euclidean distance compared with the solvent-accessible surface distance. Finally, we create a combined score that incorporates information from 3D electron microscopy maps as well as crosslinking. This achieves, on average, better results than either information type alone and demonstrates the potential of integrative modeling with XL-MS and low-resolution cryoelectron microscopy.

Variability of Protein Structure Models from Electron Microscopy.

An increasing number of biomolecular structures are solved by electron microscopy (EM). However, the quality of structure models determined from EM maps vary substantially. To understand to what extent structure models are supported by information embedded in EM maps, we used two computational structure refinement methods to examine how much structures can be refined using a dataset of 49 maps with accompanying structure models. The extent of structure modification as well as the disagreement between refinement models produced by the two computational methods scaled inversely with the global and the local map resolutions. A general quantitative estimation of deviations of structures for particular map resolutions are provided. Our results indicate that the observed discrepancy between the deposited map and the refined models is due to the lack of structural information present in EM maps and thus these annotations must be used with caution for further applications.

Refinement of atomic models in high resolution EM reconstructions using Flex-EM and local assessment.

As the resolutions of Three Dimensional Electron Microscopic reconstructions of biological macromolecules are being improved, there is a need for better fitting and refinement methods at high resolutions and robust approaches for model assessment. Flex-EM/MODELLER has been used for flexible fitting of atomic models in intermediate-to-low resolution density maps of different biological systems. Here, we demonstrate the suitability of the method to successfully refine structures at higher resolutions (2.5-4.5Å) using both simulated and experimental data, including a newly processed map of Apo-GroEL. A hierarchical refinement protocol was adopted where the rigid body definitions are relaxed and atom displacement steps are reduced progressively at successive stages of refinement. For the assessment of local fit, we used the SMOC (segment-based Manders' overlap coefficient) score, while the model quality was checked using the Qmean score. Comparison of SMOC profiles at different stages of refinement helped in detecting regions that are poorly fitted. We also show how initial model errors can have significant impact on the goodness-of-fit. Finally, we discuss the implementation of Flex-EM in the CCP-EM software suite.