What comes next in glycobiology.

Glycans, with their variable compositions and highly dynamic conformations, vastly expand the heterogeneity of whatever factor or cell they are attached to. These properties make them crucial contributors to biological function and organismal health and also very difficult to study. That may be changing as we look to the future of glycobiology.

Predicting glycan structure from tandem mass spectrometry via deep learning.

Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 500,000 annotated MS/MS spectra, here we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw liquid chromatography-MS/MS data in seconds (top-1 accuracy: 90.3%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb . We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.

Can ChatGPT pass Glycobiology?

The release of text-generating applications based on interactive Large Language Models (LLMs) in late 2022 triggered an unprecedented and ever-growing interest worldwide. The almost instantaneous success of LLMs stimulated lively discussions in public media and in academic fora alike not only on the value and potentials of such tools in all areas of knowledge and information acquisition and distribution but also on the dangers posed by their uncontrolled and indiscriminate use. This conversation is now particularly active in the higher education sector, where LLMs are seen as a potential threat to academic integrity at all levels, from facilitating cheating by students in assignments to plagiarizing academic writing in the case of researchers and administrators. Within this framework, we are interested in testing the boundaries of the LLM ChatGPT (www.openai.com) in areas of our scientific interest and expertise and in analyzing the results from different perspectives, i.e. of a final year BSc student, of a research scientist, and of a lecturer in higher education. To this end, in this paper, we present and discuss a systematic evaluation on how ChatGPT addresses progressively complex scientific writing tasks and exam-type questions in Carbohydrate Chemistry and Glycobiology. The results of this project allowed us to gain insight on: (i) the strengths and limitations of the ChatGPT model to provide relevant and (most importantly) correct scientific information, (ii) the format(s) and complexity of the query required to obtain the desired output, and (iii) strategies to integrate LLMs in teaching and learning.

A chromatin-independent role of Polycomb-like 1 to stabilize p53 and promote cellular quiescence.

Polycomb-like proteins 1-3 (PCL1-3) are substoichiometric components of the Polycomb-repressive complex 2 (PRC2) that are essential for association of the complex with chromatin. However, it remains unclear why three proteins with such apparent functional redundancy exist in mammals. Here we characterize their divergent roles in both positively and negatively regulating cellular proliferation. We show that while PCL2 and PCL3 are E2F-regulated genes expressed in proliferating cells, PCL1 is a p53 target gene predominantly expressed in quiescent cells. Ectopic expression of any PCL protein recruits PRC2 to repress the INK4A gene; however, only PCL2 and PCL3 confer an INK4A-dependent proliferative advantage. Remarkably, PCL1 has evolved a PRC2- and chromatin-independent function to negatively regulate proliferation. We show that PCL1 binds to and stabilizes p53 to induce cellular quiescence. Moreover, depletion of PCL1 phenocopies the defects in maintaining cellular quiescence associated with p53 loss. This newly evolved function is achieved by the binding of the PCL1 N-terminal PHD domain to the C-terminal domain of p53 through two unique serine residues, which were acquired during recent vertebrate evolution. This study illustrates the functional bifurcation of PCL proteins, which act in both a chromatin-dependent and a chromatin-independent manner to regulate the INK4A and p53 pathways.

GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data.

Carbohydrate sequencing is a formidable task identified as a strategic goal in modern biochemistry. It relies on identifying a large number of isomers and their connectivity with high accuracy. Recently, gas phase vibrational laser spectroscopy combined with mass spectrometry tools have been proposed as a very promising sequencing approach. However, its use as a generic analytical tool relies on the development of recognition techniques that can analyse complex vibrational fingerprints for a large number of monomers. In this study, we used a Bayesian deep neural network model to automatically identify and classify vibrational fingerprints of several monosaccharides. We report high performances of the obtained trained algorithm (GlAIcomics), that can be used to discriminate contamination and identify a molecule with a high degree of confidence. It opens the possibility to use artificial intelligence in combination with spectroscopy-augmented mass spectrometry for carbohydrates sequencing and glycomics applications.

An atomistic perspective on antibody-dependent cellular cytotoxicity quenching by core-fucosylation of IgG1 Fc N-glycans from enhanced sampling molecular dynamics.

The immunoglobulin type G (IgG) Fc N-glycans are known to modulate the interaction with membrane-bound Fc γ receptors (FcγRs), fine-tuning the antibody's effector function in a sequence-dependent manner. Particularly interesting in this respect are the roles of galactosylation, which levels are linked to autoimmune conditions and aging, of core fucosylation, which is known to reduce significantly the antibody-dependent cellular cytotoxicity (ADCC), and of sialylation, which also reduces antibody-dependent cellular cytotoxicity (ADCC) but only in the context of core-fucosylation. In this article, we provide an atomistic level perspective through enhanced sampling computer simulations, based on replica exchange molecular dynamics (REMD), to understand the molecular determinants linking the Fc N-glycans sequence to the observed IgG1 function. Our results indicate that the two symmetrically opposed N-glycans interact extensively through their core trimannose residues. At room temperature, the terminal galactose on the α (1-6) arm is restrained to the protein through a network of interactions that keep the arm outstretched; meanwhile, the α (1-3) arm extends toward the solvent where a terminal sialic acid remains fully accessible. We also find that the presence of core fucose interferes with the extended sialylated α (1-3) arm, altering its conformational propensity and as a consequence of steric hindrance, significantly enhancing the Fc dynamics. Furthermore, structural analysis shows that the core-fucose position within the Fc core obstructs the access of N162 glycosylated FcγRs very much like a "door-stop," potentially decreasing the IgG/FcγR binding free energy. These results provide an atomistic level-of-detail framework for the design of high potency IgG1 Fc N-glycoforms.

How and why plants and human N-glycans are different: Insight from molecular dynamics into the "glycoblocks" architecture of complex carbohydrates.

The N-glycosylation is one of the most abundant and diverse post-translational modifications of proteins, implicated in protein folding and structural stability, and mediating interactions with receptors and with the environment. All N-glycans share a common core from which linear or branched arms stem from, with functionalization specific to different species and to the cells' health and disease state. This diversity generates a rich collection of structures, all diversely able to trigger molecular cascades and to activate pathways, which also include adverse immunogenic responses. These events are inherently linked to the N-glycans' 3D architecture and dynamics, which remain for the large part unresolved and undetected because of their intrinsic structural disorder. In this work we use molecular dynamics (MD) simulations to provide insight into N-glycans' 3D structure by analysing the effects of a set of very specific modifications found in plants and invertebrate N-glycans, which are immunogenic in humans. We also compare these structural motifs and combine them with mammalian N-glycan motifs to devise strategies for the control of the N-glycan 3D structure through sequence. Our results suggest that the N-glycans' architecture can be described in terms of the local spatial environment of groups of monosaccharides. We define these "glycoblocks" as self-contained 3D units, uniquely identified by the nature of the residues they comprise, their linkages and structural/dynamic features. This alternative description of glycans' 3D architecture can potentially lead to an easier prediction of sequence-to-structure relationships in complex carbohydrates, with important implications in glycoengineering design.

Sequence-to-structure dependence of isolated IgG Fc complex biantennary N-glycans: a molecular dynamics study.

Fc glycosylation of human immunoglobulins G (IgGs) is essential for their structural integrity and activity. Interestingly, the specific nature of the Fc glycoforms is known to modulate the IgG effector function and inflammatory properties. Indeed, while core-fucosylation of IgG Fc-glycans greatly affects the antibody-dependent cell-mediated cytotoxicity function, with obvious repercussions in the design of therapeutic antibodies, sialylation can reverse the antibody inflammatory response, and galactosylation levels have been linked to aging, to the onset of inflammation, and to the predisposition to rheumatoid arthritis. Within the framework of a structure-to-function relationship, we have studied the role of the N-glycan sequence on its intrinsic conformational propensity. Here we report the results of a systematic study, based on extensive molecular dynamics simulations in excess of 62 µs of cumulative simulation time, on the effect of sequence on the structure and dynamics of increasingly larger, complex biantennary N-glycoforms isolated from the protein, i.e. from chitobiose to the larger N-glycan species commonly found in the Fc region of human IgGs. Our results show that while core fucosylation and sialylation do not affect the intrinsic dynamics of the unlinked N-glycans, galactosylation of the α(1-6) arm shifts dramatically its conformational equilibrium from an outstretched to a folded conformation. These findings are in agreement with and can help rationalize recent experimental evidence showing a differential recognition of positional isomers in glycan array data and also the preference of sialyltransferase for the more accessible, outstretched α(1-3) arm in both isolated, and Fc-bound N-glycans.

Aminoquinoline Fluorescent Labels Obstruct Efficient Removal of N-Glycan Core α(1-6) Fucose by Bovine Kidney α-l-Fucosidase (BKF).

Here we report evidence that new aminoquinoline N-glycan fluorescent labels interfere with the release of core α(1-6) fucose from N-glycans by bovine kidney α-l-fucosidase (BKF). BKF is a commonly employed exoglycosidase for core α(1-6) fucose determination. Molecular simulations of the bound and unbound Fuc-α(1-6)-GlcNAc, where GlcNAc is situated at the reducing end for all N-glycans, suggest that the reduced BKF activity may be due to a nonoptimal fit of the highest populated conformers in the BKF active binding site at room temperature. Population analysis and free energy estimates suggest that an enhanced flexibility of the labeled sugar, which facilitates recognition and binding, can be achievable with extended reaction conditions. We provide these experimental conditions using a sequential exoglycosidase digestion array using high concentrations of BKF.

The role of conformational selection in the molecular recognition of the wild type and mutants XPA67-80 peptides by ERCC1.

Molecular recognition is a fundamental step in the coordination of biomolecular pathways. Understanding how recognition and binding occur between highly flexible protein domains is a complex task. The conformational selection theory provides an elegant rationalization of the recognition mechanism, especially valid in cases when unstructured protein regions are involved. The recognition of a poorly structured peptide, namely XPA67-80 , by its target receptor ERCC1, falls in this challenging study category. The microsecond molecular dynamics (MD) simulations, discussed in this work, show that the conformational propensity of the wild type XPA67-80 peptide in solution supports conformational selection as the key mechanism driving its molecular recognition by ERCC1. Moreover, all the mutations of the XPA67-80 peptide studied here cause a significant increase of its conformational disorder, relative to the wild type. Comparison to experimental data suggests that the loss of the recognized structural motifs at the microscopic time scale can contribute to the critical decrease in binding observed for one of the mutants, further substantiating the key role of conformational selection in recognition. Ultimately, because of the high sequence identity and analogy in binding, it is conceivable that the conclusions of this study on the XPA67-80 peptide also apply to the ERCC1-binding domain of the XPA protein.

Defining the structural origin of the substrate sequence independence of O-GlcNAcase using a combination of molecular docking and dynamics simulation.

Protein glycosylation with O-linked N-acetylglucosamine (O-GlcNAc) is a post-translational modification of serine/threonine residues in nucleocytoplasmic proteins. O-GlcNAc has been shown to play a role in many different cellular processes and O-GlcNAcylation is often found at sites that are also known to be phosphorylated. Unlike phosphorylation, O-GlcNAc levels are regulated by only two enzymes, O-GlcNAc transferase (OGT) and O-GlcNAc hydrolase (O-GlcNAcase or OGA). So far, no obvious consensus sequence has been found for sites of O-GlcNAcylation. Additionally, O-GlcNAcase recognizes and cleaves all O-GlcNAcylated proteins, independent of their sequence. In this work, we generate and analyze five models of O-GlcNAcylated peptides in complex with a bacterial OGA. Each of the five glycopeptides bind to OGA in a similar fashion, with OGA-peptide interactions primarily, but not exclusively, involving the peptide backbone atoms, thus explaining the lack of sensitivity to peptide sequence. Nonetheless, differences in peptide sequences, particularly at the -1 to -4 positions, lead to variations in predicted affinity, consistent with observed experimental variations in enzyme kinetics. The potential exists, therefore, to employ the present analysis to guide the development glycopeptide-specific inhibitors, or conversely, the conversion of OGA into a reagent that could target specific O-GlcNAcylated peptide sequences.

Presentation, presentation, presentation! Molecular-level insight into linker effects on glycan array screening data.

Changes in cell-surface glycan patterns are markers of the presence of many different disease and cancer types, offering a relatively untapped niche for glycan-targeting reagents and therapeutics in diagnosis and treatment. Of paramount importance for the success of any glycan-targeting reagent is the ability to specifically recognize the target among the plethora of different glycans that exist in the human body. The preeminent technique for defining specificity is glycan array screening, in which a glycan-binding protein (GBP) can be simultaneously screened against multiple glycans. Glycan array screening has provided unparalleled insight into GBP specificity, but data interpretation suffers from difficulties in identifying false-negative binding arising from altered glycan presentation, associated with the linker used to conjugate the glycan to the surface. In this work, we model the structure and dynamics of the linkers employed in the glycan arrays developed by the Consortium for Functional Glycomics. The modeling takes into account the physical presence and surface polarity of the array, and provides a structure-based rationalization of false-negative results arising from the so-called "linker effect." The results also serve as a guide for interpreting glycan array screening data in a biological context; in particular, we show that attempts to employ natural amino acids as linkers may be prone to unexpected artifacts compromising glycan recognition.

ASM variants in the spotlight: A structure-based atlas for unraveling pathogenic mechanisms in lysosomal acid sphingomyelinase.

Lysosomal acid sphingomyelinase (ASM), a critical enzyme in lipid metabolism encoded by the SMPD1 gene, plays a crucial role in sphingomyelin hydrolysis in lysosomes. ASM deficiency leads to acid sphingomyelinase deficiency, a rare genetic disorder with diverse clinical manifestations, and the protein can be found mutated in other diseases. We employed a structure-based framework to comprehensively understand the functional implications of ASM variants, integrating pathogenicity predictions with molecular insights derived from a molecular dynamics simulation in a lysosomal membrane environment. Our analysis, encompassing over 400 variants, establishes a structural atlas of missense variants of lysosomal ASM, associating mechanistic indicators with pathogenic potential. Our study highlights variants that influence structural stability or exert local and long-range effects at functional sites. To validate our predictions, we compared them to available experimental data on residual catalytic activity in 135 ASM variants. Notably, our findings also suggest applications of the resulting data for identifying cases suited for enzyme replacement therapy. This comprehensive approach enhances the understanding of ASM variants and provides valuable insights for potential therapeutic interventions.

Role of N343 glycosylation on the SARS-CoV-2 S RBD structure and co-receptor binding across variants of concern.

Glycosylation of the SARS-CoV-2 spike (S) protein represents a key target for viral evolution because it affects both viral evasion and fitness. Successful variations in the glycan shield are difficult to achieve though, as protein glycosylation is also critical to folding and structural stability. Within this framework, the identification of glycosylation sites that are structurally dispensable can provide insight into the evolutionary mechanisms of the shield and inform immune surveillance. In this work, we show through over 45 µs of cumulative sampling from conventional and enhanced molecular dynamics (MD) simulations, how the structure of the immunodominant S receptor binding domain (RBD) is regulated by N-glycosylation at N343 and how this glycan's structural role changes from WHu-1, alpha (B.1.1.7), and beta (B.1.351), to the delta (B.1.617.2), and omicron (BA.1 and BA.2.86) variants. More specifically, we find that the amphipathic nature of the N-glycan is instrumental to preserve the structural integrity of the RBD hydrophobic core and that loss of glycosylation at N343 triggers a specific and consistent conformational change. We show how this change allosterically regulates the conformation of the receptor binding motif (RBM) in the WHu-1, alpha, and beta RBDs, but not in the delta and omicron variants, due to mutations that reinforce the RBD architecture. In support of these findings, we show that the binding of the RBD to monosialylated ganglioside co-receptors is highly dependent on N343 glycosylation in the WHu-1, but not in the delta RBD, and that affinity changes significantly across VoCs. Ultimately, the molecular and functional insight we provide in this work reinforces our understanding of the role of glycosylation in protein structure and function and it also allows us to identify the structural constraints within which the glycosylation site at N343 can become a hotspot for mutations in the SARS-CoV-2 S glycan shield.

Conformational determinants for the recruitment of ERCC1 by XPA in the nucleotide excision repair (NER) Pathway: structure and dynamics of the XPA binding motif.

XPA is an essential protein in the nucleotide excision repair (NER) pathway, in charge of recruiting the ERCC1-XPF endonuclease complex to the DNA damage site. The only currently available structural insight into the binding of XPA to ERCC1 derives from the solution NMR structure of a complex between the ERCC1 central fragment and a 14-residue peptide, corresponding to the highly conserved binding motif of the XPA N-terminus, XPA67₋80. The extensive all-atom molecular-dynamics simulation study of the XPA67₋80 peptide both bound to the ERCC1 central fragment and free in solution presented here completes the profile of the structural determinants responsible for the ERCC1/XPA67₋80 complex stability. In addition to the wild-type, this study also looks at specific XPA67₋80 mutants in complex with the ERCC1 central domain and thus contributes to defining the conformational determinants for binding, as well as all of the essential structural elements necessary for the rational design of an XPA-based, ERCC1-specific inhibitor.

On the molecular basis of uracil recognition in DNA: comparative study of T-A versus U-A structure, dynamics and open base pair kinetics.

Uracil (U) can be found in DNA as a mismatch paired either to adenine (A) or to guanine (G). Removal of U from DNA is performed by a class of enzymes known as uracil-DNA-glycosylases (UDG). Recent studies suggest that recognition of U-A and U-G mismatches by UDG takes place via an extra-helical mechanism. In this work, we use molecular dynamics simulations to analyze the structure, dynamics and open base pair kinetics of U-A base pairs relative to their natural T-A counterpart in 12 dodecamers. Our results show that the presence of U does not alter the local conformation of B-DNA. Breathing dynamics and base pair closing kinetics are only weakly dependent on the presence of U versus T, with open T-A and U-A pairs lifetimes in the nanosecond timescale. Additionally, we observed spontaneous base flipping in U-A pairs. We analyze the structure and dynamics for this event and compare the results to available crystallographic data of open base pair conformations. Our results are in agreement with both structural and kinetic data derived from NMR imino proton exchange measurements, providing the first detailed description at the molecular level of elusive events such as spontaneous base pair opening and flipping in mismatched U-A sequences in DNA. Based on these results, we propose that base pair flipping can occur spontaneously at room temperature via a 3-step mechanism with an open base pair intermediate. Implications for the molecular basis of U recognition by UDG are discussed.

Variations within the Glycan Shield of SARS-CoV-2 Impact Viral Spike Dynamics.

The emergence of SARS-CoV-2 variants alters the efficacy of existing immunity, whether arisen naturally or through vaccination. Understanding the structure of the viral spike assists in determining the impact of mutations on the antigenic surface. One class of mutation impacts glycosylation attachment sites, which have the capacity to influence the antigenic structure beyond the immediate site of attachment. Here, we compare the site-specific glycosylation of recombinant viral spike mimetics of B.1.351 (Beta), P.1 (Gamma), B.1.617.2 (Delta), B.1.1.529 (Omicron). The P.1 strain exhibits two additional N-linked glycan sites compared to the other variants analyzed and we investigate the impact of these glycans by molecular dynamics. The acquired N188 site is shown to exhibit very limited glycan maturation, consistent with limited enzyme accessibility. Structural modeling and molecular dynamics reveal that N188 is located within a cavity by the receptor binding domain, which influences the dynamics of these attachment domains. These observations suggest a mechanism whereby mutations affecting viral glycosylation sites have a structural impact across the protein surface.

Glycosaminoglycans: What Remains To Be Deciphered?

Glycosaminoglycans (GAGs) are complex polysaccharides exhibiting a vast structural diversity and fulfilling various functions mediated by thousands of interactions in the extracellular matrix, at the cell surface, and within the cells where they have been detected in the nucleus. It is known that the chemical groups attached to GAGs and GAG conformations comprise "glycocodes" that are not yet fully deciphered. The molecular context also matters for GAG structures and functions, and the influence of the structure and functions of the proteoglycan core proteins on sulfated GAGs and vice versa warrants further investigation. The lack of dedicated bioinformatic tools for mining GAG data sets contributes to a partial characterization of the structural and functional landscape and interactions of GAGs. These pending issues will benefit from the development of new approaches reviewed here, namely (i) the synthesis of GAG oligosaccharides to build large and diverse GAG libraries, (ii) GAG analysis and sequencing by mass spectrometry (e.g., ion mobility-mass spectrometry), gas-phase infrared spectroscopy, recognition tunnelling nanopores, and molecular modeling to identify bioactive GAG sequences, biophysical methods to investigate binding interfaces, and to expand our knowledge and understanding of glycocodes governing GAG molecular recognition, and (iii) artificial intelligence for in-depth investigation of GAGomic data sets and their integration with proteomics.

Molecular simulations of complex carbohydrates and glycoconjugates.

Complex carbohydrates (glycans) are the most abundant and versatile biopolymers in nature. The broad diversity of biochemical functions that carbohydrates cover is a direct consequence of the variety of 3D architectures they can adopt, displaying branched or linear arrangements, widely ranging in sizes, and with the highest diversity of building blocks of any other natural biopolymer. Despite this unparalleled complexity, a common denominator can be found in the glycans' inherent flexibility, which hinders experimental characterization, but that can be addressed by high-performance computing (HPC)-based molecular simulations. In this short review, I present and discuss the state-of-the-art of molecular simulations of complex carbohydrates and glycoconjugates, highlighting methodological strengths and weaknesses, important insights through emblematic case studies, and suggesting perspectives for future developments.

Principles of SARS-CoV-2 glycosylation.

The structure and post-translational processing of the SARS-CoV-2 spike glycoprotein (S) is intimately associated with the function of the virus and of sterilising vaccines. The surface of the S protein is extensively modified by glycans, and their biosynthesis is driven by both the wider cellular context, and importantly, the underlining protein structure and local glycan density. Comparison of virally derived S protein with both recombinantly derived and adenovirally induced proteins, reveal hotspots of protein-directed glycosylation that drive conserved glycosylation motifs. Molecular dynamics simulations revealed that, while the S surface is extensively shielded by N-glycans, it presents regions vulnerable to neutralising antibodies. Furthermore, glycans have been shown to influence the accessibility of the receptor binding domain and the binding to the cellular receptor. The emerging picture is one of unifying, principles of S protein glycosylation and an intimate role of glycosylation in immunogen structure and efficacy.