Mapping the glycosyltransferase fold landscape using interpretable deep learning

Exploring domain architectures of human glycosyltransferases: Highlighting the functional diversity of non-catalytic add-on domains

Human glycosyltransferases (GTs) play crucial roles in glycan biosynthesis, exhibiting diverse domain architectures. This study explores the functional diversity of "add-on" domains within human GTs, using data from the AlphaFold Protein Structure Database. Among 215 annotated human GTs, 74 contain one or more add-on domains in addition to their catalytic domain. These domains include lectin folds, fibronectin type III, and thioredoxin-like domains and contribute to substrate specificity, oligomerization, and consequent enzymatic activity. Notably, certain GTs possess dual enzymatic functions due to catalytic add-on domains. The analysis highlights the importance of add-on domains in enzyme functionality and disease implications, such as congenital disorders of glycosylation. This comprehensive overview enhances our understanding of GT domain organization, providing insights into glycosylation mechanisms and potential therapeutic targets.

Harnessing de novo transcriptome sequencing to identify and characterize genes regulating carbohydrate biosynthesis pathways in Salvia guaranitica L

Introduction

Carbohydrate compounds serve multifaceted roles, from energy sources to stress protectants, found across diverse organisms including bacteria, fungi, and plants. Despite this broad importance, the molecular genetic framework underlying carbohydrate biosynthesis pathways, such as starch, sucrose, and glycolysis/gluconeogenesis in Salvia guaranitica, remains largely unexplored.

Methods

In this study, the Illumina-HiSeq 2500 platform was used to sequence the transcripts of S. guaranitica leaves, generating approximately 8.2 Gb of raw data. After filtering and removing adapter sequences, 38 million reads comprising 210 million high-quality nucleotide bases were obtained. De novo assembly resulted in 75,100 unigenes, which were annotated to establish a comprehensive database for investigating starch, sucrose, and glycolysis biosynthesis. Functional analyses of glucose-6-phosphate isomerase (SgGPI), trehalose-6-phosphate synthase/phosphatase (SgT6PS), and sucrose synthase (SgSUS) were performed using transgenic Arabidopsis thaliana.

Results

Among the unigenes, 410 were identified as putatively involved in these metabolic pathways, including 175 related to glycolysis/gluconeogenesis and 235 to starch and sucrose biosynthesis. Overexpression of SgGPI, SgT6PS, and SgSUS in transgenic A. thaliana enhanced leaf area, accelerated flower formation, and promoted overall growth compared to wild-type plants.

Discussion

These findings lay a foundation for understanding the roles of starch, sucrose, and glycolysis biosynthesis genes in S. guaranitica, offering insights into future metabolic engineering strategies for enhancing the production of valuable carbohydrate compounds in S. guaranitica or other plants.

Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments

Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However, guidelines for using IML in computational biology are generally underdeveloped. We provide an overview of IML methods and evaluation techniques and discuss common pitfalls encountered when applying IML methods to computational biology problems. We also highlight open questions, especially in the era of large language models, and call for collaboration between IML and computational biology researchers.

Polysaccharide sulfotransferases: the identification of putative sequences and respective functional characterisation

The vast structural diversity of sulfated polysaccharides demands an equally diverse array of enzymes known as polysaccharide sulfotransferases (PSTs). PSTs are present across all kingdoms of life, including algae, fungi and archaea, and their sulfation pathways are relatively unexplored. Sulfated polysaccharides possess anti-inflammatory, anticoagulant and anti-cancer properties and have great therapeutic potential. Current identification of PSTs using Pfam has been predominantly focused on the identification of glycosaminoglycan (GAG) sulfotransferases because of their pivotal roles in cell communication, extracellular matrix formation and coagulation. As a result, our knowledge of non-GAG PSTs structure and function remains limited. The major sulfotransferase families, Sulfotransfer_1 and Sulfotransfer_2, display broad homology and should enable the capture of a wide assortment of sulfotransferases but are limited in non-GAG PST sequence annotation. In addition, sequence annotation is further restricted by the paucity of biochemical analyses of PSTs. There are now high-throughput and robust assays for sulfotransferases such as colorimetric PAPS (3'-phosphoadenosine 5'-phosphosulfate) coupled assays, Europium-based fluorescent probes for ratiometric PAP (3'-phosphoadenosine-5'-phosphate) detection, and NMR methods for activity and product analysis. These techniques provide real-time and direct measurements to enhance the functional annotation and subsequent analysis of sulfated polysaccharides across the tree of life to improve putative PST identification and characterisation of function. Improved annotation and biochemical analysis of PST sequences will enhance the utility of PSTs across biomedical and biotechnological sectors.

Architecture, Function, Regulation, and Evolution of α-Glucans Metabolic Enzymes in Prokaryotes

Bacteria have acquired sophisticated mechanisms for assembling and disassembling polysaccharides of different chemistry. α-d-Glucose homopolysaccharides, so-called α-glucans, are the most widespread polymers in nature being key components of microorganisms. Glycogen functions as an intracellular energy storage while some bacteria also produce extracellular assorted α-glucans. The classical bacterial glycogen metabolic pathway comprises the action of ADP-glucose pyrophosphorylase and glycogen synthase, whereas extracellular α-glucans are mostly related to peripheral enzymes dependent on sucrose. An alternative pathway of glycogen biosynthesis, operating via a maltose 1-phosphate polymerizing enzyme, displays an essential wiring with the trehalose metabolism to interconvert disaccharides into polysaccharides. Furthermore, some bacteria show a connection of intracellular glycogen metabolism with the genesis of extracellular capsular α-glucans, revealing a relationship between the storage and structural function of these compounds. Altogether, the current picture shows that bacteria have evolved an intricate α-glucan metabolism that ultimately relies on the evolution of a specific enzymatic machinery. The structural landscape of these enzymes exposes a limited number of core catalytic folds handling many different chemical reactions. In this Review, we present a rationale to explain how the chemical diversity of α-glucans emerged from these systems, highlighting the underlying structural evolution of the enzymes driving α-glucan bacterial metabolism.

Insights into the inner workings of transformer models for protein function prediction

MOTIVATION

We explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such that latent representations inside of transformer models, which were finetuned to Gene Ontology term and Enzyme Commission number prediction, can be inspected too.

RESULTS

The approach enabled us to identify amino acids in the sequences that the transformers pay particular attention to, and to show that these relevant sequence parts reflect expectations from biology and chemistry, both in the embedding layer and inside of the model, where we identified transformer heads with a statistically significant correspondence of attribution maps with ground truth sequence annotations (e.g. transmembrane regions, active sites) across many proteins.

AVAILABILITY AND IMPLEMENTATION

Source code can be accessed at https://github.com/markuswenzel/xai-proteins.

Specificity determinants revealed by the structure of glycosyltransferase Campylobacter concisus PglA

In selected Campylobacter species, the biosynthesis of N-linked glycoconjugates via the pgl pathway is essential for pathogenicity and survival. However, most of the membrane-associated GT-B fold glycosyltransferases responsible for diversifying glycans in this pathway have not been structurally characterized which hinders the understanding of the structural factors that govern substrate specificity and prediction of resulting glycan composition. Herein, we report the 1.8 Å resolution structure of Campylobacter concisus PglA, the glycosyltransferase responsible for the transfer of N-acetylgalatosamine (GalNAc) from uridine 5'-diphospho-N-acetylgalactosamine (UDP-GalNAc) to undecaprenyl-diphospho-N,N'-diacetylbacillosamine (UndPP-diNAcBac) in complex with the sugar donor GalNAc. This study identifies distinguishing characteristics that set PglA apart within the GT4 enzyme family. Computational docking of the structure in the membrane in comparison to homologs points to differences in interactions with the membrane-embedded acceptor and the structural analysis of the complex together with bioinformatics and site-directed mutagenesis identifies donor sugar binding motifs. Notably, E113, conserved solely among PglA enzymes, forms a hydrogen bond with the GalNAc C6″-OH. Mutagenesis of E113 reveals activity consistent with this role in substrate binding, rather than stabilization of the oxocarbenium ion transition state, a function sometimes ascribed to the corresponding residue in GT4 homologs. The bioinformatic analyses reveal a substrate-specificity motif, showing that Pro281 in a substrate binding loop of PglA directs configurational preference for GalNAc over GlcNAc. This proline is replaced by a conformationally flexible glycine, even in distant homologs, which favor substrates with the same stereochemistry at C4, such as glucose. The signature loop is conserved across all Campylobacter PglA enzymes, emphasizing its importance in substrate specificity.

Glycosyltransferase 8 domain-containing protein 1 (GLT8D1) is a UDP-dependent galactosyltransferase

Glycosyltransferases (GTs) are enzymes that catalyze the formation of glycosidic bonds and hundreds of GTs have been identified so far in humans. Glycosyltransferase 8 domain-containing protein 1 (GLT8D1) has been associated with central nervous system diseases and cancer. However, evidence on its enzymatic properties, including its substrates, has been scarcely described. In this paper, we have produced and purified recombinant secretory GLT8D1. The enzyme was found to be N-glycosylated. Differential scanning fluorimetry was employed to analyze the stabilization of GLT8D1 by Mn2+ and nucleotides, revealing UDP as the most stabilizing nucleotide scaffold. GLT8D1 displayed glycosyltransferase activity from UDP-galactose onto N-acetylgalactosamine but with a low efficiency. Modeling of the structure revealed similarities with other GT-A fold enzymes in CAZy family GT8 and glycosyltransferases in other families with galactosyl-, glucosyl-, and xylosyltransferase activities, each with retaining catalytic mechanisms. Our study provides novel structural and functional insights into the properties of GLT8D1 with implications in pathological processes.

Structural Characterization and Ligand-Induced Conformational Changes of SenB, a Se-Glycosyltransferase Involved in Selenoneine Biosynthesis

Selenium (Se) is an essential micronutrient that is found naturally in proteins, nucleic acids, and natural products. Unlike selenoproteins and selenonucleic acids, little is known about the structures of biosynthetic enzymes that incorporate Se into small molecules. Here, we report the X-ray crystal structure of SenB, the first known Se-glycosyltransferase that was recently found to be involved in the biosynthesis of the Se-containing metabolite selenoneine. SenB catalyzes C-Se bond formation using selenophosphate and an activated uridine diphosphate sugar as a Se and glycosyl donor, respectively, making it the first known selenosugar synthase and one of only four bona fide C-Se bond-forming enzymes discovered to date. Our crystal structure, determined to 2.25 Å resolution, reveals that SenB is a type B glycosyltransferase, displaying the prototypical fold with two globular Rossmann-like domains and a catalytic interdomain cleft. By employing complementary structural biology techniques, we find that SenB undergoes both local and global substrate-induced conformational changes, demonstrating a significant increase in α-helicity and a transition to a more compact conformation. Our results provide the first structure of SenB and set the stage for further biochemical characterization in the future.

Functional annotation of enzyme-encoding genes using deep learning with transformer layers

Functional annotation of open reading frames in microbial genomes remains substantially incomplete. Enzymes constitute the most prevalent functional gene class in microbial genomes and can be described by their specific catalytic functions using the Enzyme Commission (EC) number. Consequently, the ability to predict EC numbers could substantially reduce the number of un-annotated genes. Here we present a deep learning model, DeepECtransformer, which utilizes transformer layers as a neural network architecture to predict EC numbers. Using the extensively studied Escherichia coli K-12 MG1655 genome, DeepECtransformer predicted EC numbers for 464 un-annotated genes. We experimentally validated the enzymatic activities predicted for three proteins (YgfF, YciO, and YjdM). Further examination of the neural network's reasoning process revealed that the trained neural network relies on functional motifs of enzymes to predict EC numbers. Thus, DeepECtransformer is a method that facilitates the functional annotation of uncharacterized genes.

Rewiring the pneumococcal capsule pathway for investigating glycosyltransferase specificity and genetic glycoengineering

Virtually all living cells are covered with glycans. Their structures are primarily controlled by the specificities of glycosyltransferases (GTs). GTs typically adopt one of the three folds, namely, GT-A, GT-B, and GT-C. However, what defines their specificities remain poorly understood. Here, we developed a genetic glycoengineering platform by reprogramming the capsular polysaccharide pathways in Streptococcus pneumoniae to interrogate GT specificity and manipulate glycan structures. Our findings suggest that the central cleft of GT-B enzymes is important for determining acceptor specificity. The constraint of the glycoengineering platform was partially alleviated when the specificity of the precursor transporter was reduced, indicating that the transporter contributes to the overall fidelity of glycan synthesis. We also modified the pneumococcal capsule to produce several medically important mammalian glycans, as well as demonstrated the importance of regiochemistry in a glycosidic linkage on binding lung epithelial cells. Our work provided mechanistic insights into GT specificity and an approach for investigating glycan functions.

Structural basis for Lewis antigen synthesis by the α1,3-fucosyltransferase FUT9

Mammalian cell surface and secreted glycoproteins exhibit remarkable glycan structural diversity that contributes to numerous physiological and pathogenic interactions. Terminal glycan structures include Lewis antigens synthesized by a collection of α1,3/4-fucosyltransferases (CAZy GT10 family). At present, the only available crystallographic structure of a GT10 member is that of the Helicobacter pylori α1,3-fucosyltransferase, but mammalian GT10 fucosyltransferases are distinct in sequence and substrate specificity compared with the bacterial enzyme. Here, we determined crystal structures of human FUT9, an α1,3-fucosyltransferase that generates Lewisx and Lewisy antigens, in complex with GDP, acceptor glycans, and as a FUT9-donor analog-acceptor Michaelis complex. The structures reveal substrate specificity determinants and allow prediction of a catalytic model supported by kinetic analyses of numerous active site mutants. Comparisons with other GT10 fucosyltransferases and GT-B fold glycosyltransferases provide evidence for modular evolution of donor- and acceptor-binding sites and specificity for Lewis antigen synthesis among mammalian GT10 fucosyltransferases.

Structure and dynamics of the Arabidopsis O-fucosyltransferase SPINDLY

SPINDLY (SPY) in Arabidopsis thaliana is a novel nucleocytoplasmic protein O-fucosyltransferase (POFUT), which regulates diverse developmental processes. Sequence analysis indicates that SPY is distinct from ER-localized POFUTs and contains N-terminal tetratricopeptide repeats (TPRs) and a C-terminal catalytic domain resembling the O-linked-N-acetylglucosamine (GlcNAc) transferases (OGTs). However, the structural feature that determines the distinct enzymatic selectivity of SPY remains unknown. Here we report the cryo-electron microscopy (cryo-EM) structure of SPY and its complex with GDP-fucose, revealing distinct active-site features enabling GDP-fucose instead of UDP-GlcNAc binding. SPY forms an antiparallel dimer instead of the X-shaped dimer in human OGT, and its catalytic domain interconverts among multiple conformations. Analysis of mass spectrometry, co-IP, fucosylation activity, and cryo-EM data further demonstrates that the N-terminal disordered peptide in SPY contains trans auto-fucosylation sites and inhibits the POFUT activity, whereas TPRs 1-5 dynamically regulate SPY activity by interfering with protein substrate binding.

Phosformer: an explainable transformer model for protein kinase-specific phosphorylation predictions

MOTIVATION

The human genome encodes over 500 distinct protein kinases which regulate nearly all cellular processes by the specific phosphorylation of protein substrates. While advances in mass spectrometry and proteomics studies have identified thousands of phosphorylation sites across species, information on the specific kinases that phosphorylate these sites is currently lacking for the vast majority of phosphosites. Recently, there has been a major focus on the development of computational models for predicting kinase-substrate associations. However, most current models only allow predictions on a subset of well-studied kinases. Furthermore, the utilization of hand-curated features and imbalances in training and testing datasets pose unique challenges in the development of accurate predictive models for kinase-specific phosphorylation prediction. Motivated by the recent development of universal protein language models which automatically generate context-aware features from primary sequence information, we sought to develop a unified framework for kinase-specific phosphosite prediction, allowing for greater investigative utility and enabling substrate predictions at the whole kinome level.

RESULTS

We present a deep learning model for kinase-specific phosphosite prediction, termed Phosformer, which predicts the probability of phosphorylation given an arbitrary pair of unaligned kinase and substrate peptide sequences. We demonstrate that Phosformer implicitly learns evolutionary and functional features during training, removing the need for feature curation and engineering. Further analyses reveal that Phosformer also learns substrate specificity motifs and is able to distinguish between functionally distinct kinase families. Benchmarks indicate that Phosformer exhibits significant improvements compared to the state-of-the-art models, while also presenting a more generalized, unified, and interpretable predictive framework.

AVAILABILITY AND IMPLEMENTATION

Code and data are available at https://github.com/esbgkannan/phosformer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Expanding the viewpoint: Leveraging sequence information in enzymology

The use of protein sequence to inform enzymology in terms of structure, mechanism, and function has burgeoned over the past two decades. Referred to as genomic enzymology, the utilization of bioinformatic tools such as sequence similarity networks and phylogenetic analyses has allowed the identification of new substrates and metabolites, novel pathways, and unexpected reaction mechanisms. The holistic examination of superfamilies can yield insight into the origins and paths of evolution of enzymes and the range of their substrates and mechanisms. Herein, we highlight advances in the use of genomic enzymology to address problems which the in-depth analyses of a single enzyme alone could not enable.

Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies

Protein language models, trained on millions of biologically observed sequences, generate feature-rich numerical representations of protein sequences. These representations, called sequence embeddings, can infer structure-functional properties, despite protein language models being trained on primary sequence alone. While sequence embeddings have been applied toward tasks such as structure and function prediction, applications toward alignment-free sequence classification have been hindered by the lack of studies to derive, quantify and evaluate relationships between protein sequence embeddings. Here, we develop workflows and visualization methods for the classification of protein families using sequence embedding derived from protein language models. A benchmark of manifold visualization methods reveals that Neighbor Joining (NJ) embedding trees are highly effective in capturing global structure while achieving similar performance in capturing local structure compared with popular dimensionality reduction techniques such as t-SNE and UMAP. The statistical significance of hierarchical clusters on a tree is evaluated by resampling embeddings using a variational autoencoder (VAE). We demonstrate the application of our methods in the classification of two well-studied enzyme superfamilies, phosphatases and protein kinases. Our embedding-based classifications remain consistent with and extend upon previously published sequence alignment-based classifications. We also propose a new hierarchical classification for the S-Adenosyl-L-Methionine (SAM) enzyme superfamily which has been difficult to classify using traditional alignment-based approaches. Beyond applications in sequence classification, our results further suggest NJ trees are a promising general method for visualizing high-dimensional data sets.

Polymerization of the backbone of the pectic polysaccharide rhamnogalacturonan I

Rhamnogalacturonan I (RG-I) is a major plant cell wall pectic polysaccharide defined by its repeating disaccharide backbone structure of [4)-α-D-GalA-(1,2)-α-L-Rha-(1,]. A family of RG-I:Rhamnosyltransferases (RRT) has previously been identified, but synthesis of the RG-I backbone has not been demonstrated in vitro because the identity of Rhamnogalacturonan I:Galaturonosyltransferase (RG-I:GalAT) was unknown. Here a putative glycosyltransferase, At1g28240/MUCI70, is shown to be an RG-I:GalAT. The name RGGAT1 is proposed to reflect the catalytic activity of this enzyme. When incubated together with the rhamnosyltransferase RRT4, the combined activities of RGGAT1 and RRT4 result in elongation of RG-I acceptors in vitro into a polymeric product. RGGAT1 is a member of a new GT family categorized as GT116, which does not group into existing GT-A clades and is phylogenetically distinct from the GALACTURONOSYLTRANSFERASE (GAUT) family of GalA transferases that synthesize the backbone of the pectin homogalacturonan. RGGAT1 has a predicted GT-A fold structure but employs a metal-independent catalytic mechanism that is rare among glycosyltransferases with this fold type. The identification of RGGAT1 and the 8-member Arabidopsis GT116 family provides a new avenue for studying the mechanism of RG-I synthesis and the function of RG-I in plants.

Artificial intelligence in the analysis of glycosylation data

Glycans are complex, yet ubiquitous across biological systems. They are involved in diverse essential organismal functions. Aberrant glycosylation may lead to disease development, such as cancer, autoimmune diseases, and inflammatory diseases. Glycans, both normal and aberrant, are synthesized using extensive glycosylation machinery, and understanding this machinery can provide invaluable insights for diagnosis, prognosis, and treatment of various diseases. Increasing amounts of glycomics data are being generated thanks to advances in glycoanalytics technologies, but to maximize the value of such data, innovations are needed for analyzing and interpreting large-scale glycomics data. Artificial intelligence (AI) provides a powerful analysis toolbox in many scientific fields, and here we review state-of-the-art AI approaches on glycosylation analysis. We further discuss how models can be analyzed to gain mechanistic insights into glycosylation machinery and how the machinery shapes glycans under different scenarios. Finally, we propose how to leverage the gained knowledge for developing predictive AI-based models of glycosylation. Thus, guiding future research of AI-based glycosylation model development will provide valuable insights into glycosylation and glycan machinery.

Glycoinformatics in the Artificial Intelligence Era

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

Host genetic requirements for DNA release of lactococcal phage TP901-1

The first step in phage infection is the recognition of, and adsorption to, a receptor located on the host cell surface. This reversible host adsorption step is commonly followed by an irreversible event, which involves phage DNA delivery or release into the bacterial cytoplasm. The molecular components that trigger this latter event are unknown for most phages of Gram-positive bacteria. In the current study, we present a comparative genome analysis of three mutants of Lactococcus cremoris 3107, which are resistant to the P335 group phage TP901-1 due to mutations that affect TP901-1 DNA release. Through genetic complementation and phage infection assays, a predicted lactococcal three-component glycosylation system (TGS) was shown to be required for TP901-1 infection. Major cell wall saccharidic components were analysed, but no differences were found. However, heterologous gene expression experiments indicate that this TGS is involved in the glucosylation of a cell envelope-associated component that triggers TP901-1 DNA release. To date, a saccharide modification has not been implicated in the DNA delivery process of a Gram-positive infecting phage.

Common and unique features of glycosylation and glycosyltransferases in African trypanosomes

Eukaryotic protein glycosylation is mediated by glycosyl- and oligosaccharyl-transferases. Here, we describe how African trypanosomes exhibit both evolutionary conservation and significant divergence compared with other eukaryotes in how they synthesise their glycoproteins. The kinetoplastid parasites have conserved components of the dolichol-cycle and oligosaccharyltransferases (OSTs) of protein N-glycosylation, and of glycosylphosphatidylinositol (GPI) anchor biosynthesis and transfer to protein. However, some components are missing, and they process and decorate their N-glycans and GPI anchors in unique ways. To do so, they appear to have evolved a distinct and functionally flexible glycosyltransferases (GT) family, the GT67 family, from an ancestral eukaryotic β3GT gene. The expansion and/or loss of GT67 genes appears to be dependent on parasite biology. Some appear to correlate with the obligate passage of parasites through an insect vector, suggesting they were acquired through GT67 gene expansion to assist insect vector (tsetse fly) colonisation. Others appear to have been lost in species that subsequently adopted contaminative transmission. We also highlight the recent discovery of a novel and essential GT11 family of kinetoplastid parasite fucosyltransferases that are uniquely localised to the mitochondria of Trypanosoma brucei and Leishmania major. The origins of these kinetoplastid FUT1 genes, and additional putative mitochondrial GT genes, are discussed.

Modularity of the hydrophobic core and evolution of functional diversity in fold A glycosyltransferases

Hydrophobic cores are fundamental structural properties of proteins typically associated with protein folding and stability; however, how the hydrophobic core shapes protein evolution and function is poorly understood. Here, we investigated the role of conserved hydrophobic cores in fold-A glycosyltransferases (GT-As), a large superfamily of enzymes that catalyze formation of glycosidic linkages between diverse donor and acceptor substrates through distinct catalytic mechanisms (inverting versus retaining). Using hidden Markov models and protein structural alignments, we identify similarities in the phosphate-binding cassette (PBC) of GT-As and unrelated nucleotide-binding proteins, such as UDP-sugar pyrophosphorylases. We demonstrate that GT-As have diverged from other nucleotide-binding proteins through structural elaboration of the PBC and its unique hydrophobic tethering to the F-helix, which harbors the catalytic base (xED-Asp). While the hydrophobic tethering is conserved across diverse GT-A fold enzymes, some families, such as B3GNT2, display variations in tethering interactions and core packing. We evaluated the structural and functional impact of these core variations through experimental mutational analysis and molecular dynamics simulations and find that some of the core mutations (T336I in B3GNT2) increase catalytic efficiency by modulating the conformational occupancy of the catalytic base between “D-in” and acceptor-accessible “D-out” conformation. Taken together, our studies support a model of evolution in which the GT-A core evolved progressively through elaboration upon an ancient PBC found in diverse nucleotide-binding proteins, and malleability of this core provided the structural framework for evolving new catalytic and substrate-binding functions in extant GT-A fold enzymes.

Structural basis for the synthesis of the core 1 structure by C1GalT1

C1GalT1 is an essential inverting glycosyltransferase responsible for synthesizing the core 1 structure, a common precursor for mucin-type O-glycans found in many glycoproteins. To date, the structure of C1GalT1 and the details of substrate recognition and catalysis remain unknown. Through biophysical and cellular studies, including X-ray crystallography of C1GalT1 complexed to a glycopeptide, we report that C1GalT1 is an obligate GT-A fold dimer that follows a S_N2 mechanism. The binding of the glycopeptides to the enzyme is mainly driven by the GalNAc moiety while the peptide sequence provides optimal kinetic and binding parameters. Interestingly, to achieve glycosylation, C1GalT1 recognizes a high-energy conformation of the α-GalNAc-Thr linkage, negligibly populated in solution. By imposing this 3D-arrangement on that fragment, characteristic of α-GalNAc-Ser peptides, C1GalT1 ensures broad glycosylation of both acceptor substrates. These findings illustrate a structural and mechanistic blueprint to explain glycosylation of multiple acceptor substrates, extending the repertoire of mechanisms adopted by glycosyltransferases.

The glycosyltransferase C1GalT1 directs a key step in protein O-glycosylation important for the expression of the cancer-associated Tn and T antigens. Here, the authors provide molecular insights into the function of C1GalT1 by solving the crystal structure of the Drosophila enzyme-substrate complex.

Glycosyltransferases: Mining, engineering and applications in biosynthesis of glycosylated plant natural products

UDP-Glycosyltransferases (UGTs) catalyze the transfer of nucleotide-activated sugars to specific acceptors, among which the GT1 family enzymes are well-known for their function in biosynthesis of natural product glycosides. Elucidating GT function represents necessary step in metabolic engineering of aglycone glycosylation to produce drug leads, cosmetics, nutrients and sweeteners. In this review, we systematically summarize the phylogenetic distribution and catalytic diversity of plant GTs. We also discuss recent progress in the identification of novel GT candidates for synthesis of plant natural products (PNPs) using multi-omics technology and deep learning predicted models. We also highlight recent advances in rational design and directed evolution engineering strategies for new or improved GT functions. Finally, we cover recent breakthroughs in the application of GTs for microbial biosynthesis of some representative glycosylated PNPs, including flavonoid glycosides (fisetin 3-O-glycosides, astragalin, scutellarein 7-O-glucoside), terpenoid glycosides (rebaudioside A, ginsenosides) and polyketide glycosides (salidroside, polydatin).