Importance of ligand conformational energies in carbohydrate docking: Sorting the wheat from the chaff

TREM2 on microglia cell surface binds to and forms functional binary complexes with heparan sulfate modified with 6-O-sulfation and iduronic acid

The triggering receptor expressed on myeloid cells-2 (TREM2), a pivotal innate immune receptor, orchestrates functions such as inflammatory responses, phagocytosis, cell survival, and neuroprotection. TREM2 variants R47H and R62H have been associated with Alzheimer's disease, yet the underlying mechanisms remain elusive. Our previous research established that TREM2 binds to heparan sulfate (HS) and variants R47H and R62H exhibit reduced affinity for HS. Building upon this groundwork, our current study delves into the interplay between TREM2 and HS and its impact on microglial function. We confirm TREM2's binding to cell surface HS and demonstrate that TREM2 interacts with HS, forming HS-TREM2 binary complexes on microglia cell surfaces. Employing various biochemical techniques, including surface plasmon resonance, low molecular weight HS microarray screening, and serial HS mutant cell surface binding assays, we demonstrate TREM2's robust affinity for HS, and the effective binding requires a minimum HS size of approximately 10 saccharide units. Notably, TREM2 selectively binds specific HS structures, with 6-O-sulfation and, to a lesser extent, the iduronic acid residue playing crucial roles. N-sulfation and 2-O-sulfation are dispensable for this interaction. Furthermore, we reveal that 6-O-sulfation is essential for HS-TREM2 ternary complex formation on the microglial cell surface, and HS and its 6-O-sulfation are necessary for TREM2-mediated ApoE3 uptake in microglia. By delineating the interaction between HS and TREM2 on the microglial cell surface and demonstrating its role in facilitating TREM2-mediated ApoE uptake by microglia, our findings provide valuable insights that can inform targeted interventions for modulating microglial functions in Alzheimer's disease.

Unveiling crucial amino acids in the carbohydrate recognition domain of a viral protein through a structural bioinformatic approach

Carbohydrate binding modules (CBMs) are protein domains that typically reside near catalytic domains, increasing substrate-protein proximity by constraining the conformational space of carbohydrates. Due to the flexibility and variability of glycans, the molecular details of how these protein regions recognize their target molecules are not always fully understood. Computational methods, including molecular docking and molecular dynamics simulations, have been employed to investigate lectin-carbohydrate interactions. In this study, we introduce a novel approach that integrates multiple computational techniques to identify the critical amino acids involved in the interaction between a CBM located at the tip of bacteriophage J-1's tail and its carbohydrate counterparts. Our results highlight three amino acids that play a significant role in binding, a finding we confirmed through in vitro experiments. By presenting this approach, we offer an intriguing alternative for pinpointing amino acids that contribute to protein-sugar interactions, leading to a more thorough comprehension of the molecular determinants of protein-carbohydrate interactions.

Computational toolbox for the analysis of protein-glycan interactions

Protein-glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein-glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein-ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein-glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.

Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

Synthetic Glycans Reveal Determinants of Antibody Functional Efficacy against a Fungal Pathogen

Antibodies play a vital role in the immune response to infectious diseases and can be administered passively to protect patients. In the case of Cryptococcus neoformans, a WHO critical priority fungal pathogen, infection results in antibodies targeting capsular glucuronoxylomannan (GXM). These antibodies yield protective, non-protective, and disease-enhancing outcomes when administered passively. However, it was unknown how these distinct antibodies recognized their antigens at the molecular level, leading to the hypothesis that they may target different GXM epitopes. To test this hypothesis, we constructed a microarray containing 26 glycans representative of those found in highly virulent cryptococcal strains and utilized it to study 16 well-characterized monoclonal antibodies. Notably, we found that protective and non-protective antibodies shared conserved reactivity to the M2 motif of GXM, irrespective of the strain used in infection or GXM-isolated to produce a conjugate vaccine. Here, only two antibodies, 12A1 and 18B7, exhibited diverse trivalent GXM motif reactivity. IgG antibodies associated with protective responses showed cross-reactivity to at least two GXM motifs. This molecular understanding of antibody binding epitopes was used to map the antigenic diversity of two Cryptococcus neoformans strains, which revealed the exceptional complexity of fungal capsular polysaccharides. A multi-GXM motif vaccine holds the potential to effectively address this antigenic diversity. Collectively, these findings underscore the context-dependent nature of antibody function and challenge the classification of anti-GXM epitopes as either "protective" or "non-protective".

Molecular docking analysis of a dermatan sulfate tetra-saccharide to human alpha-L-iduronidase

Human alpha-L-iduronidase (IDUA) is a 653 amino acid protein involved in the sequential degradation of glycos-amino-glycans (GAG), heparan sulfate (HS), and dermatan sulfate (DS). Some variants in the IDUA gene produce a deficient enzyme that causes un-degraded DS and HS to accumulate in multiple tissues, leading to an organ dysfunction known as muco-poly-saccharidosis type I (MPS I). Molecular and catalytic activity assays of new or rare variants of IDUA do not predict the phenotype that a patient will develop. Therefore, it is of interest to describe the molecular docking analysis, to locate binding regions of DS to IDUA to better understand the effect of a variant on MPS I development. The results presented herein demonstrate the presence of a polar/acidic catalytic site and a basic region in the putative binding site of DS to IDUA. Further, synthetic substrate docking with the enzyme could help in the predictions of the MPS I phenotype.

Assessing Genetic Algorithm-Based Docking Protocols for Prediction of Heparin Oligosaccharide Binding Geometries onto Proteins

Although molecular docking has evolved dramatically over the years, its application to glycosaminoglycans (GAGs) has remained challenging because of their intrinsic flexibility, highly anionic character and rather ill-defined site of binding on proteins. GAGs have been treated as either fully "rigid" or fully "flexible" in molecular docking. We reasoned that an intermediate semi-rigid docking (SRD) protocol may be better for the recapitulation of native heparin/heparan sulfate (Hp/HS) topologies. Herein, we study 18 Hp/HS-protein co-complexes containing chains from disaccharide to decasaccharide using genetic algorithm-based docking with rigid, semi-rigid, and flexible docking protocols. Our work reveals that rigid and semi-rigid protocols recapitulate native poses for longer chains (5→10 mers) significantly better than the flexible protocol, while 2→4-mer poses are better predicted using the semi-rigid approach. More importantly, the semi-rigid docking protocol is likely to perform better when no crystal structure information is available. We also present a new parameter for parsing selective versus non-selective GAG-protein systems, which relies on two computational parameters including consistency of binding (i.e., RMSD) and docking score (i.e., GOLD Score). The new semi-rigid protocol in combination with the new computational parameter is expected to be particularly useful in high-throughput screening of GAG sequences for identifying promising druggable targets as well as drug-like Hp/HS sequences.

A quinolin-8-ol sub-millimolar inhibitor of UGGT, the ER glycoprotein folding quality control checkpoint

Misfolded glycoprotein recognition and endoplasmic reticulum (ER) retention are mediated by the ER glycoprotein folding quality control (ERQC) checkpoint enzyme, UDP-glucose glycoprotein glucosyltransferase (UGGT). UGGT modulation is a promising strategy for broad-spectrum antivirals, rescue-of-secretion therapy in rare disease caused by responsive mutations in glycoprotein genes, and many cancers, but to date no selective UGGT inhibitors are known. The small molecule 5-[(morpholin-4-yl)methyl]quinolin-8-ol (5M-8OH-Q) binds a CtUGGTGT24 "WY" conserved surface motif conserved across UGGTs but not present in other GT24 family glycosyltransferases. 5M-8OH-Q has a 47 μM binding affinity for CtUGGTGT24in vitro as measured by ligand-enhanced fluorescence. In cellula, 5M-8OH-Q inhibits both human UGGT isoforms at concentrations higher than 750 μM. 5M-8OH-Q binding to CtUGGTGT24 appears to be mutually exclusive to M5-9 glycan binding in an in vitro competition experiment. A medicinal program based on 5M-8OH-Q will yield the next generation of UGGT inhibitors.

Computationally guided conversion of the specificity of E-selectin to mimic that of Siglec-8

Sulfated glycans have been found to be associated with various diseases and therefore have significant potential in molecular pathology as biomarkers. Although lectins are useful reagents for detecting glycans, there is a paucity of sulfate-recognizing lectins, and those that exist, such as from Maackia amurensis, display mixed specificities. Recombinant lectin engineering offers an emerging tool for creating novel glycan recognition by altering and/or enhancing endogenous specificities. The present study demonstrated the use of computational approaches in the engineering of a mutated form of E-selectin that displayed highly specific recognition of 6'-sulfo-sialyl Lewis X (6'-sulfo-sLe^x), with negligible binding to its endogenous nonsulfated ligand, sLe^x. This new specificity mimics that of the unrelated protein Siglec-8, for which 6'-sulfo-sLe^x is its preferred ligand. Molecular dynamics simulations and energy calculations predicted that two point mutations (E92A/E107A) would be required to stabilize binding to the sulfated oligosaccharide with E-selectin. In addition to eliminating putative repulsions between the negatively charged side chains and the sulfate moiety, the mutations also abolished favorable interactions with the endogenous ligand. Glycan microarray screening of the recombinantly expressed proteins confirmed the predicted specificity change but also identified the introduction of unexpected affinity for the unfucosylated form of 6'-sulfo-sLe^x (6'-sulfo-sLacNAc). Three key requirements were demonstrated in this case for engineering specificity for sulfated oligosaccharide: 1) removal of unfavorable interactions with the 6'-sulfate, 2) introduction of favorable interactions for the sulfate, and 3) removal of favorable interactions with the endogenous ligand.

Multifaceted Computational Modeling in Glycoscience

Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.

Structural Insights into the Cofactor Role of Heparin/Heparan Sulfate in Binding between the SARS-CoV-2 Spike Protein and Host Angiotensin-Converting Enzyme II

The viral entry process of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires heparin and heparan sulfates from the cell surface, functioning as a cofactor for human angiotensin-converting enzyme 2 (ACE2) for recognizing the receptor-binding domain (RBD) of the spike (S) protein on the surface of the virion. In the present study, the binding poses of an oligosaccharide with four repeating units of GlcNS6S-IdoA2S (octa) predicted by Vina-Carb in the RBD binding site were employed in molecular dynamics (MD) simulations to provide atomic details for studying the cofactor mechanism. The molecular model in the MD simulations reproduced the length- and sequence-dependent behavior observed from the microarray experiments and revealed an important planar U-turn shape for HP/HS binding to RBD. The model for octa with this shape in the ACE2-RBD complex enhanced the interactions in the binding interface. The comparisons with the ACE2-RBD complex suggested that the presence of octa in the RBD binding site blocked the movements in a loop region at the distal end of the RBD binding interface and promoted the contacts of this loop region with the ACE2 N-terminus helix. This study shed light on the atomic and dynamic details for HP/HS interacting with RBD and provided insights into their cofactor role in the ACE2-RBD interactions.

Advanced Molecular Dynamics Approaches to Model a Tertiary Complex APRIL/TACI with Long Glycosaminoglycans

Glycosaminoglycans (GAGs) are linear anionic periodic polysaccharides participating in a number of biologically relevant processes in the extracellular matrix via interactions with their protein targets. Due to their periodicity, conformational flexibility, pseudo-symmetry of the sulfation pattern, and the key role of electrostatics, these molecules are challenging for both experimental and theoretical approaches. In particular, conventional molecular docking applied for GAGs longer than 10-mer experiences severe difficulties. In this work, for the first time, 24- and 48-meric GAGs were docked using all-atomic repulsive-scaling Hamiltonian replica exchange molecular dynamics (RS-REMD), a novel methodology based on replicas with van der Waals radii of interacting molecules being scaled. This approach performed well for proteins complexed with oligomeric GAGs and is independent of their length, which distinguishes it from other molecular docking approaches. We built a model of long GAGs in complex with a proliferation-inducing ligand (APRIL) prebound to its receptors, the B cell maturation antigen and the transmembrane activator and calcium modulator and cyclophilin ligand interactor (TACI). Furthermore, the prediction power of the RS-REMD for this tertiary complex was evaluated. We conclude that the TACI–GAG interaction could be potentially amplified by TACI’s binding to APRIL. RS-REMD outperformed Autodock3, the docking program previously proven the best for short GAGs.

Structural insights in galectin-1-glycan recognition: Relevance of the glycosidic linkage and the N-acetylation pattern of sugar moieties

Galectins, soluble lectins widely expressed intra- and extracellularly in different cell types, play major roles in deciphering the cellular glycocode. Galectin-1 (Gal-1), a prototype member of this family, presents a carbohydrate recognition domain (CRD) with specific affinity for β-galactosides such as N-acetyllactosamine (β-d-Galp-(1 → 4)-d-GlcpNAc), and mediate numerous physiological and pathological processes. In this work, Gal-1 binding affinity for β-(1 → 6) galactosides, including β-d-Galp-(1 → 6)-β-d-GlcpNAc-(1 → 4)-d-GlcpNAc was evaluated, and their performance was compared to that of β-(1 → 4) and β-(1 → 3) galactosides. To this end, the trisaccharide β-d-Galp-(1 → 6)-β-d-GlcpNAc-(1 → 4)-d-GlcpNAc was enzymatically synthesized, purified and structurally characterized. To evaluate the affinity of Gal-1 for the galactosides, competitive solid phase assays (SPA) and isothermal titration calorimetry (ITC) studies were carried out. The experimental dissociation constants and binding energies obtained were compared to those calculated by molecular docking. These analyses evidenced the critical role of the glycosidic linkage between the terminal galactopyranoside residue and the adjacent monosaccharide, as galactosides bearing β-(1 → 6) glycosidic linkages showed dissociation constants six- and seven-fold higher than those involving β-(1 → 4) and β-(1 → 3) linkages, respectively. Moreover, docking experiments revealed the presence of hydrogen bond interactions between the N-acetyl group of the glucosaminopyranose moiety of the evaluated galactosides and specific amino acid residues of Gal-1, relevant for galectin-glycan affinity. Noticeably, the binding free energies (ΔG_bind^calc) derived from the molecular docking were in good agreement with experimental values determined by ITC measurements (ΔG_bind^exp), evidencing a good correlation between theoretical and experimental approaches, which validates the in silico simulations and constitutes an important tool for the rational design of future optimized ligands.

Modulation of the carbohydrate-binding specificity of two Xenopus proto-type galectins by site-directed mutagenesis

The galectin family is a representative soluble lectin group, which is responsible for the modulation of various cell functions. Although the carbohydrate-binding specificity of galectins has been well-studied, the relationship between protein structure and specificity remains to be elucidated. We previously reported the characteristics of a Xenopus laevis skin galectin, xgalectin-Va, which had diverged from galectin-1. The carbohydrate selectivity of xgalectin-Va was different from that of human galectin-1 and xgalectin-Ib (a Xenopus laevis galectin-1 homolog). In this study, we clarified the key residues for this selectivity by site-directed mutagenesis. Substitution of two amino acids of xgalectin-Va, Val56Gly/Lys76Arg, greatly enhanced the binding ability to N-acetyllactosamine and conferred significant T-cell growth inhibition activity, although the wild type had no activity. These two residues, Gly54 and Arg74 in galectin-1, would cooperatively contribute to the N-acetyllactosamine recognition. The loop region between the S4 and S5 β-strands was involved in the binding to the TF-antigen disaccharide. The loop substitution successfully changed the carbohydrate selectivity of xgalectin-Va and xgalectin-Ib.

Development and Evaluation of GlycanDock: A Protein-Glycoligand Docking Refinement Algorithm in Rosetta

Carbohydrate chains are ubiquitous in the complex molecular processes of life. These highly diverse chains are recognized by a variety of protein receptors, enabling glycans to regulate many biological functions. High-resolution structures of protein-glycoligand complexes reveal the atomic details necessary to understand this level of molecular recognition and inform application-focused scientific and engineering pursuits. When experimental challenges hinder high-throughput determination of quality structures, computational tools can, in principle, fill the gap. In this work, we introduce GlycanDock, a residue-centric protein-glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. We performed a benchmark docking assessment using a set of 109 experimentally determined protein-glycoligand complexes as well as 62 unbound protein structures. The GlycanDock algorithm can sample and discriminate among protein-glycoligand models of native-like structural accuracy with statistical reliability from starting structures of up to 7 Å root-mean-square deviation in the glycoligand ring atoms. We show that GlycanDock-refined models qualitatively replicated the known binding specificity of a bacterial carbohydrate-binding module. Finally, we present a protein-glycoligand docking pipeline for generating putative protein-glycoligand complexes when only the glycoligand sequence and unbound protein structure are known. In combination with other carbohydrate modeling tools, the GlycanDock docking refinement algorithm will accelerate research in the glycosciences.

Investigation of the structure of regulatory proteins interacting with glycosaminoglycans by combining NMR spectroscopy and molecular modeling - the beginning of a wonderful friendship

The interaction of regulatory proteins with extracellular matrix or cell surface-anchored glycosaminoglycans (GAGs) plays important roles in molecular recognition, wound healing, growth, inflammation and many other processes. In spite of their high biological relevance, protein-GAG complexes are significantly underrepresented in structural databases because standard tools for structure determination experience difficulties in studying these complexes. Co-crystallization with subsequent X-ray analysis is hampered by the high flexibility of GAGs. NMR spectroscopy experiences difficulties related to the periodic nature of the GAGs and the sparse proton network between protein and GAG with distances that typically exceed the detection limit of nuclear Overhauser enhancement spectroscopy. In contrast, computer modeling tools have advanced over the last years delivering specific protein-GAG docking approaches successfully complemented with molecular dynamics (MD)-based analysis. Especially the combination of NMR spectroscopy in solution providing sparse structural constraints with molecular docking and MD simulations represents a useful synergy of forces to describe the structure of protein-GAG complexes. Here we review recent methodological progress in this field and bring up examples where the combination of new NMR methods along with cutting-edge modeling has yielded detailed structural information on complexes of highly relevant cytokines with GAGs.

Insights into the roles of charged residues in substrate binding and mode of action of mannuronan C-5 epimerase AlgE4

Mannuronan C-5 epimerases catalyse the epimerization of monomer residues in the polysaccharide alginate, changing the physical properties of the biopolymer. The enzymes are utilized to tailor alginate to numerous biological functions by alginate-producing organisms. The underlying molecular mechanisms that control the processive movement of the epimerase along the substrate chain is still elusive. To study this, we have used an interdisciplinary approach combining molecular dynamics simulations with experimental methods from mutant studies of AlgE4, where initial epimerase activity and product formation were addressed with NMR spectroscopy, and characteristics of enzyme-substrate interactions were obtained with isothermal titration calorimetry and optical tweezers. Positive charges lining the substrate-binding groove of AlgE4 appear to control the initial binding of poly-mannuronate, and binding also seems to be mediated by both electrostatic and hydrophobic interactions. After the catalytic reaction, negatively charged enzyme residues might facilitate dissociation of alginate from the positive residues, working like electrostatic switches, allowing the substrate to translocate in the binding groove. Molecular simulations show translocation increments of two monosaccharide units before the next productive binding event resulting in MG-block formation, with the epimerase moving with its N-terminus towards the reducing end of the alginate chain. Our results indicate that the charge pair R343-D345 might be directly involved in conformational changes of a loop that can be important for binding and dissociation. The computational and experimental approaches used in this study complement each other, allowing for a better understanding of individual residues' roles in binding and movement along the alginate chains.

A glycan FRET assay for detection and characterization of catalytic antibodies to the Cryptococcus neoformans capsule

Classic antibody functions include opsonization, complement activation, and enhancement of cellular antimicrobial function. Antibodies can also have catalytic activity, although the contribution of catalysis to their biological functions has been more difficult to establish. With the ubiquity of catalytic antibodies against glycans virtually unknown, we sought to advance this knowledge. The use of a glycan microarray allowed epitope mapping of several monoclonal antibodies (mAbs) against the capsule of Cryptococcus neoformans From this, we designed and synthesized two glycan-based FRET probes, which we used to discover antibodies with innate glycosidase activity and analyze their enzyme kinetics, including mAb 2H1, the most efficient identified to date. The validity of the FRET assay was confirmed by demonstrating that the mAbs mediate glycosidase activity on intact cryptococcal capsules, as observed by a reduction in capsule diameter. Furthermore, the mAb 18B7, a glycosidase hydrolase, resulted in the appearance of reducing ends in the capsule as labeled by a hydroxylamine-armed fluorescent (HAAF) probe. Finally, we demonstrate that exposing C. neoformans cells to catalytic antibodies results in changes in complement deposition and increased phagocytosis by macrophages, suggesting that the antiphagocytic properties of the capsule have been impaired. Our results raise questions over the ubiquity of antibodies with catalytic activity against glycans and establish the utility of glycan-based FRET and HAAF probes as tools for investigating this activity.

Carbohydrate-Protein Interactions: Advances and Challenges

A carbohydrate, also called saccharide in biochemistry, is a biomolecule consisting of carbon (C), hydrogen (H) and oxygen (O) atoms. For example, sugars are low molecular-weight carbohydrates, and starches are high molecular-weight carbohydrates. Carbohydrates are the most abundant organic substances in nature and essential constituents of all living things. Protein-carbohydrate interactions play important roles in many biological processes, such as cell growth, differentiation, and aggregation. They also have broad applications in pharmaceutical drug design. In this review, we will summarize the characteristic features of protein-carbohydrate interactions and review the computational methods for structure prediction, energy calculations, and kinetic studies of protein-carbohydrate complexes. Finally, we will discuss the challenges in this field.

GlycoTorch Vina: Docking Designed and Tested for Glycosaminoglycans

Glycosaminoglycans (GAGs) are a family of anionic carbohydrates that play an essential role in the physiology and pathology of all eukaryotic life forms. Experimental determination of GAG-protein complexes is challenging due to their difficult isolation from biological sources, natural heterogeneity, and conformational flexibility-including possible ring puckering of sulfated iduronic acid from ¹C₄ to ²S_O conformation. To overcome these challenges, we present GlycoTorch Vina (GTV), a molecular docking tool based on the carbohydrate docking program VinaCarb (VC). Our program is unique in that it contains parameters to model ²S_O sugars while also supporting glycosidic linkages specific to GAGs. We discuss how crystallographic models of carbohydrates can be biased by the choice of refinement software and structural dictionaries. To overcome these variations, we carefully curated 12 of the best available GAG and GAG-like crystal structures (ranging from tetra- to octasaccharides or longer) obtained from the PDB-REDO server and refined using the same protocol. Both GTV and VC produced pose predictions with a mean root-mean-square deviation (RMSD) of 3.1 Å from the native crystal structure-a statistically significant improvement when compared to AutoDock Vina (4.5 Å) and the commercial software Glide (5.9 Å). Examples of how real-space correlation coefficients can be used to better assess the accuracy of docking pose predictions are given. Comparisons between statistical distributions of empirical "salt bridge" interactions, relevant to GAGs, were compared to density functional theory (DFT) studies of model salt bridges, and water-mediated salt bridges; however, there was generally a poor agreement between these data. Water bridges appear to play an important, yet poorly understood, role in the structures of GAG-protein complexes. To aid in the rapid prototyping of future pose scoring functions, we include a module that allows users to include their own torsional and nonbonded parameters.

Three-Dimensional Structures of Carbohydrates and Where to Find Them

Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

Virus-Receptor Interactions of Glycosylated SARS-CoV-2 Spike and Human ACE2 Receptor

The SARS-CoV-2 betacoronavirus uses its highly glycosylated trimeric Spike protein to bind to the cell surface receptor angiotensin converting enzyme 2 (ACE2) glycoprotein and facilitate host cell entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatics analyses of natural variants and with existing 3D structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein both alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation. Taken together, these data can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.

Analysis of the SARS-CoV-2 spike protein glycan shield reveals implications for immune recognition

Here we have generated 3D structures of glycoforms of the spike (S) glycoprotein from SARS-CoV-2, based on reported 3D structures and glycomics data for the protein produced in HEK293 cells. We also analyze structures for glycoforms representing those present in the nascent glycoproteins (prior to enzymatic modifications in the Golgi), as well as those that are commonly observed on antigens present in other viruses. These models were subjected to molecular dynamics (MD) simulation to determine the extent to which glycan microheterogeneity impacts the antigenicity of the S glycoprotein. Lastly, we have identified peptides in the S glycoprotein that are likely to be presented in human leukocyte antigen (HLA) complexes, and discuss the role of S protein glycosylation in potentially modulating the innate and adaptive immune response to the SARS-CoV-2 virus or to a related vaccine. The 3D structures show that the protein surface is extensively shielded from antibody recognition by glycans, with the notable exception of the ACE2 receptor binding domain, and also that the degree of shielding is largely insensitive to the specific glycoform. Despite the relatively modest contribution of the glycans to the total molecular weight of the S trimer (17% for the HEK293 glycoform) they shield approximately 40% of the protein surface.

Dissecting the conformation of glycans and their interactions with proteins

The use of in silico strategies to develop the structural basis for a rational optimization of glycan-protein interactions remains a great challenge. This problem derives, in part, from the lack of technologies to quantitatively and qualitatively assess the complex assembling between a glycan and the targeted protein molecule. Since there is an unmet need for developing new sugar-targeted therapeutics, many investigators are searching for technology platforms to elucidate various types of molecular interactions within glycan-protein complexes and aid in the development of glycan-targeted therapies. Here we discuss three important technology platforms commonly used in the assessment of the complex assembly of glycosylated biomolecules, such as glycoproteins or glycosphingolipids: Biacore analysis, molecular docking, and molecular dynamics simulations. We will also discuss the structural investigation of glycosylated biomolecules, including conformational changes of glycans and their impact on molecular interactions within the glycan-protein complex. For glycoproteins, secreted protein acidic and rich in cysteine (SPARC), which is associated with various lung disorders, such as chronic obstructive pulmonary disease (COPD) and lung cancer, will be taken as an example showing that the core fucosylation of N-glycan in SPARC regulates protein-binding affinity with extracellular matrix collagen. For glycosphingolipids (GSLs), Globo H ceramide, an important tumor-associated GSL which is being actively investigated as a target for new cancer immunotherapies, will be used to demonstrate how glycan structure plays a significant role in enhancing angiogenesis in tumor microenvironments.

Allosteric regulation of lysosomal enzyme recognition by the cation-independent mannose 6-phosphate receptor

The cation-independent mannose 6-phosphate receptor (CI-MPR, IGF2 receptor or CD222), is a multifunctional glycoprotein required for normal development. Through the receptor's ability to bind unrelated extracellular and intracellular ligands, it participates in numerous functions including protein trafficking, lysosomal biogenesis, and regulation of cell growth. Clinically, endogenous CI-MPR delivers infused recombinant enzymes to lysosomes in the treatment of lysosomal storage diseases. Although four of the 15 domains comprising CI-MPR's extracellular region bind phosphorylated glycans on lysosomal enzymes, knowledge of how CI-MPR interacts with ~60 different lysosomal enzymes is limited. Here, we show by electron microscopy and hydroxyl radical protein footprinting that the N-terminal region of CI-MPR undergoes dynamic conformational changes as a consequence of ligand binding and different pH conditions. These data, coupled with X-ray crystallography, surface plasmon resonance and molecular modeling, allow us to propose a model explaining how high-affinity carbohydrate binding is achieved through allosteric domain cooperativity.

Virus-Receptor Interactions of Glycosylated SARS-CoV-2 Spike and Human ACE2 Receptor

The current COVID-19 pandemic is caused by the SARS-CoV-2 betacoronavirus, which utilizes its highly glycosylated trimeric Spike protein to bind to the cell surface receptor ACE2 glycoprotein and facilitate host cell entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatic analyses of natural variants and with existing 3D-structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation that, taken together, can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.

Analysis of the SARS-CoV-2 spike protein glycan shield: implications for immune recognition

Here we have generated 3D structures of glycoforms of the spike (S) glycoprotein from SARS-CoV-2, based on reported 3D structures and glycomics data for the protein produced in HEK293 cells. We also analyze structures for glycoforms representing those present in the nascent glycoproteins (prior to enzymatic modifications in the Golgi), as well as those that are commonly observed on antigens present in other viruses. These models were subjected to molecular dynamics (MD) simulation to determine the extent to which glycan microheterogeneity impacts the antigenicity of the S glycoprotein. Lastly, we have identified peptides in the S glycoprotein that are likely to be presented in human leukocyte antigen (HLA) complexes, and discuss the role of S protein glycosylation in potentially modulating the adaptive immune response to the SARS-CoV-2 virus or to a related vaccine. The 3D structures show that the protein surface is extensively shielded from antibody recognition by glycans, with the exception of the ACE2 receptor binding domain, and also that the degree of shielding is largely insensitive to the specific glycoform. Despite the relatively modest contribution of the glycans to the total molecular weight (17% for the HEK293 glycoform) the level of surface shielding is disproportionately high at 42%.

Stereoelectronic Effects Impact Glycan Recognition

Recognition of distinct glycans is central to biology, and lectins mediate this function. Lectin glycan preferences are usually centered on specific monosaccharides. In contrast, human intelectin-1 (hItln-1, also known as Omentin-1) is a soluble lectin that binds a range of microbial sugars, including β-d-galactofuranose (β-Galf), d-glycerol 1-phosphate, d-glycero-d-talo-oct-2-ulosonic acid (KO), and 3-deoxy-d-manno-oct-2-ulosonic acid (KDO). Though these saccharides differ dramatically in structure, they share a common feature-an exocyclic vicinal diol. How and whether such a small fragment is sufficient for recognition was unclear. We tested several glycans with this epitope and found that l-glycero-α-d-manno-heptose and d-glycero-α-d-manno-heptose possess the critical diol motif yet bind weakly. To better understand hItln-1 recognition, we determined the structure of the hItln-1·KO complex using X-ray crystallography, and our 1.59 Å resolution structure enabled unambiguous assignment of the bound KO conformation. This carbohydrate conformation was present in >97% of the KDO/KO structures in the Protein Data Bank. Bioinformatic analysis revealed that KO and KDO adopt a common conformation, while heptoses prefer different conformers. The preferred conformers of KO and KDO favor hItln-1 engagement, but those of the heptoses do not. Natural bond orbital (NBO) calculations suggest these observed conformations, including the side chain orientations, are stabilized by not only steric but also stereoelectronic effects. Thus, our data highlight a role for stereoelectronic effects in dictating the specificity of glycan recognition by proteins. Finally, our finding that hItln-1 avoids binding prevalent glycans with a terminal 1,2-diol (e.g., N-acetyl-neuraminic acid and l-glycero-α-d-manno-heptose) suggests the lectin has evolved to recognize distinct bacterial species.

The Structural Biology of Galectin-Ligand Recognition: Current Advances in Modeling Tools, Protein Engineering, and Inhibitor Design

Galectins (formerly known as “S-type lectins”) are a subfamily of soluble proteins that typically bind β-galactoside carbohydrates with high specificity. They are present in many forms of life, from nematodes and fungi to animals, where they perform a wide range of functions. Particularly in humans, different types of galectins have been described differing not only in their tissue expression but also in their cellular location, oligomerization, fold architecture and carbohydrate-binding affinity. This distinct yet sometimes overlapping distributions and physicochemical attributes make them responsible for a wide variety of both intra- and extracellular functions, including tremendous importance in immunity and disease. In this review, we aim to provide a general description of galectins most important structural features, with a special focus on the molecular determinants of their carbohydrate-recognition ability. For that purpose, we structurally compare the human galectins, in light of recent mutagenesis studies and novel X-ray structures. We also offer a detailed description on how to use the solvent structure surrounding the protein as a tool to get better predictions of galectin-carbohydrate complexes, with a potential application to the rational design of glycomimetic inhibitory compounds. Finally, using Gal-1 and Gal-3 as paramount examples, we review a series of recent advances in the development of engineered galectins and galectin inhibitors, aiming to dissect the structure-activity relationship through the description of their interaction at the molecular level.

Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions

Structure-based drug design is critically dependent on accuracy of molecular docking scoring functions, and there is of significant interest to advance scoring functions with machine learning approaches. In this work, by judiciously expanding the training set, exploring new features related to explicit mediating water molecules as well as ligand conformation stability, and applying extreme gradient boosting (XGBoost) with Δ-Vina parametrization, we have improved robustness and applicability of machine-learning scoring functions. The new scoring function ΔvinaXGB can not only perform consistently among the top compared to classical scoring functions for the CASF-2016 benchmark but also achieves significantly better prediction accuracy in different types of structures that mimic real docking applications.

Analysis of Procollagen C-Proteinase Enhancer-1/Glycosaminoglycan Binding Sites and of the Potential Role of Calcium Ions in the Interaction

In this study, we characterize the interactions between the extracellular matrix protein, procollagen C-proteinase enhancer-1 (PCPE-1), and glycosaminoglycans (GAGs), which are linear anionic periodic polysaccharides. We applied molecular modeling approaches to build a structural model of full-length PCPE-1, which is not experimentally available, to predict GAG binding poses for various GAG lengths, types and sulfation patterns, and to determine the effect of calcium ions on the binding. The computational data are analyzed and discussed in the context of the experimental results previously obtained using surface plasmon resonance binding assays. We also provide experimental data on PCPE-1/GAG interactions obtained using inhibition assays with GAG oligosaccharides ranging from disaccharides to octadecasaccharides. Our results predict the localization of GAG-binding sites at the amino acid residue level onto PCPE-1 and is the first attempt to describe the effects of ions on protein-GAG binding using modeling approaches. In addition, this study allows us to get deeper insights into the in silico methodology challenges and limitations when applied to GAG-protein interactions.

Computational analysis of the structure, glycosylation and CMP binding of human ST3GAL sialyltransferases

Sialyltransferases (STs) are the fundamental enzymes which are related to many biological processes such as cell signalling, cellular recognition, cell-cell and host-pathogen interactions and metastasis of cancer. All STs catalyse the terminal sialic acid addition from CMP donor to the glycan units. ST3GAL family is one of the most important STs and divided into the six subfamily in mouse and humans which are ST3Gal I, ST3Gal II, ST3Gal III, ST3Gal IV, ST3Gal V, and ST3Gal VI. The members of the ST3GAL family transfer sialic acid to the terminal galactose residues of glycochains through an α2,3-linkage. There are many reports on the ST3GAL function in mammals but, there is a paucity of information about structure of human ST3GAL family. Herein, we investigated the structure, glycosylation and CMP binding site of human ST3GAL family using computational methods. We found for the first time N-glycosylation positions in ST3Gal IV and VI, mucin type glycosylation in ST3Gal III and O-GlcNAcylation in ST3Gal V and their relation with sialylmotifs. In addition, we predicted CMP binding positions of human ST3GAL enzyme family on three-dimensional structure using molecular docking and first demonstrated the sialylmotifs relation with the CMP binding positions in ST3Gal III-VI subfamilies.

NMR and molecular modeling reveal specificity of the interactions between CXCL14 and glycosaminoglycans

CXCL14, chemokine (C-X-C motif) ligand 14, is a novel highly conserved chemokine with unique features. Despite exhibiting the typical chemokine fold, it has a very short N-terminus of just two amino acid residues responsible for chemokine receptor activation. CXCL14 actively participates in homeostatic immune surveillance of skin and mucosae, is linked to metabolic disorders and fibrotic lung diseases and possesses strong anti-angiogenic properties in early tumor development. In this work, we investigated the interaction of CXCL14 with various glycosaminoglycans (GAGs) by nuclear magnetic resonance spectroscopy, microscale thermophoresis, analytical heparin (HE) affinity chromatography and in silico approaches to understand the molecular basis of GAG-binding. We observed different GAG-binding modes specific for the GAG type used in the study. In particular, the CXCL14 epitope for HE suggests a binding pose distinguishable from the ones of the other GAGs investigated (hyaluronic acid, chondroitin sulfate-A/C, −D, dermatan sulfate). This observation is also supported by computational methods that included molecular docking, molecular dynamics and free energy calculations. Based on our results, we suggest that distinct GAG sulfation patterns confer specificity beyond simple electrostatic interactions usually considered to represent the driving forces in protein–GAG interactions. The CXCL14–GAG system represents a promising approach to investigate the specificity of GAG–protein interactions, which represents an important topic for developing the rational approaches to novel strategies in regenerative medicine.

An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein-monosaccharide complexes

Unraveling the structure of lectin-carbohydrate complexes is vital for understanding key biological recognition processes and development of glycomimetic drugs. Molecular Docking application to predict them is challenging due to their low affinity, hydrophilic nature and ligand conformational diversity. In the last decade several strategies, such as the inclusion of glycan conformation specific scoring functions or our developed solvent-site biased method, have improved carbohydrate docking performance but significant challenges remain, in particular, those related to receptor conformational diversity. In the present work we have analyzed conventional and solvent-site biased autodock4 performance concerning receptor conformational diversity as derived from different crystal structures (apo and holo), Molecular Dynamics snapshots and Homology-based models, for 14 different lectin-monosaccharide complexes. Our results show that both conventional and biased docking yield accurate lectin-monosaccharide complexes, starting from either apo or homology-based structures, even when only moderate (45%) sequence identity templates are available. An essential element for success is a proper combination of a middle-sized (10-100 structures) conformational ensemble, derived either from Molecular dynamics or multiple homology model building. Consistent with our previous works, results show that solvent-site biased methods improve overall performance, but that results are still highly system dependent. Finally, our results also show that docking can select the correct receptor structure within the ensemble, underscoring the relevance of joint evaluation of both ligand pose and receptor conformation.

In silico investigation of the prion protein glycosylation profiles in relation to scrapie disease resistance in domestic sheep (Ovis aries)

The prion protein is a membrane-bound glycoprotein which consists mainly α-helix structure. In contrast, the infectious prion protein shows the beta-sheet structure. The prion-associated diseases are all lethal neurodegenerative abnormalities, called transmissible spongiform encephalopathies. Scrapie is the most common type of these illnesses affecting sheep, goats, and moufflon. The VRQ, AHQ, ARR and N146S polymorphisms in the sheep prion gene have been found to be associated with resistance to scrapie disease. So far, the relationship of polymorphisms to three-dimensional protein structures, post-translational modifications, and scrapie resistance has not been studied. In this study, the potential N- and O-glycosylation positions of sheep prion protein polymorphisms were analyzed, the secondary and three-dimensional protein structure models were predicted, three-dimensional glycoprotein models were constructed and the role of glycosylation positions in protein interactions was investigated. Here, we found that protein secondary and three-dimensional structures vary among polymorphisms. Moreover, we found wild-type prion and all polymorphic variants show N-glycosylation at Asn¹⁸⁴ and Asn²⁰⁰ positions, while O-glycosylation profiles are variant-specific. We also found that structural changes among prion polymorphisms leads to the formation of variant spesific O-glycosylation profiles and these positions are associated with protein interactions. Based on these findings, we suggest that O-glycosylation may be effective on resistance/susceptibility of sheep prion polymorphisms to scrapie disease.

Predicting the Structures of Glycans, Glycoproteins, and Their Complexes

Complex carbohydrates are ubiquitous in nature, and together with proteins and nucleic acids they comprise the building blocks of life. But unlike proteins and nucleic acids, carbohydrates form nonlinear polymers, and they are not characterized by robust secondary or tertiary structures but rather by distributions of well-defined conformational states. Their molecular flexibility means that oligosaccharides are often refractory to crystallization, and nuclear magnetic resonance (NMR) spectroscopy augmented by molecular dynamics (MD) simulation is the leading method for their characterization in solution. The biological importance of carbohydrate-protein interactions, in organismal development as well as in disease, places urgency on the creation of innovative experimental and theoretical methods that can predict the specificity of such interactions and quantify their strengths. Additionally, the emerging realization that protein glycosylation impacts protein function and immunogenicity places the ability to define the mechanisms by which glycosylation impacts these features at the forefront of carbohydrate modeling. This review will discuss the relevant theoretical approaches to studying the three-dimensional structures of this fascinating class of molecules and interactions, with reference to the relevant experimental data and techniques that are key for validation of the theoretical predictions.

Insights into the structure and dynamics of lysyl oxidase propeptide, a flexible protein with numerous partners

Lysyl oxidase (LOX) catalyzes the oxidative deamination of lysine and hydroxylysine residues in collagens and elastin, which is the first step of the cross-linking of these extracellular matrix proteins. It is secreted as a proenzyme activated by bone morphogenetic protein-1, which releases the LOX catalytic domain and its bioactive N-terminal propeptide. We characterized the recombinant human propeptide by circular dichroism, dynamic light scattering, and small-angle X-ray scattering (SAXS), and showed that it is elongated, monomeric, disordered and flexible (D_max: 11.7 nm, R_g: 3.7 nm). We generated 3D models of the propeptide by coarse-grained molecular dynamics simulations restrained by SAXS data, which were used for docking experiments. Furthermore, we have identified 17 new binding partners of the propeptide by label-free assays. They include four glycosaminoglycans (hyaluronan, chondroitin, dermatan and heparan sulfate), collagen I, cross-linking and proteolytic enzymes (lysyl oxidase-like 2, transglutaminase-2, matrix metalloproteinase-2), a proteoglycan (fibromodulin), one growth factor (Epidermal Growth Factor, EGF), and one membrane protein (tumor endothelial marker-8). This suggests new roles for the propeptide in EGF signaling pathway.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

Applying Pose Clustering and MD Simulations To Eliminate False Positives in Molecular Docking

In this work, we developed a computational protocol that employs multiple molecular docking experiments, followed by pose clustering, molecular dynamic simulations (10 ns), and energy rescoring to produce reliable 3D models of antibody-carbohydrate complexes. The protocol was applied to 10 antibody-carbohydrate co-complexes and three unliganded (apo) antibodies. Pose clustering significantly reduced the number of potential poses. For each system, 15 or fewer clusters out of 100 initial poses were generated and chosen for further analysis. Molecular dynamics (MD) simulations allowed the docked poses to either converge or disperse, and rescoring increased the likelihood that the best-ranked pose was an acceptable pose. This approach is amenable to automation and can be a valuable aid in determining the structure of antibody-carbohydrate complexes provided there is no major side chain rearrangement or backbone conformational change in the H3 loop of the CDR regions. Further, the basic protocol of docking a small ligand to a known binding site, clustering the results, and performing MD with a suitable force field is applicable to any protein ligand system.

Three mutations switch H7N9 influenza to human-type receptor specificity

The avian H7N9 influenza outbreak in 2013 resulted from an unprecedented incidence of influenza transmission to humans from infected poultry. The majority of human H7N9 isolates contained a hemagglutinin (HA) mutation (Q226L) that has previously been associated with a switch in receptor specificity from avian-type (NeuAcα2-3Gal) to human-type (NeuAcα2-6Gal), as documented for the avian progenitors of the 1957 (H2N2) and 1968 (H3N2) human influenza pandemic viruses. While this raised concern that the H7N9 virus was adapting to humans, the mutation was not sufficient to switch the receptor specificity of H7N9, and has not resulted in sustained transmission in humans. To determine if the H7 HA was capable of acquiring human-type receptor specificity, we conducted mutation analyses. Remarkably, three amino acid mutations conferred a switch in specificity for human-type receptors that resembled the specificity of the 2009 human H1 pandemic virus, and promoted binding to human trachea epithelial cells.

Influenza A virus of the H7N9 subtype continues to cross the species barrier from poultry to humans. This zoonotic ability is remarkable as the virus retains specificity to avian-type receptors. To effectively transmit between humans, the virus needs to acquire human-type receptor specificity. In this study, we show that recombinant H7 proteins need three amino acid mutations to change specificity to human-type receptors. Although we are not allowed to assess if these mutations would lead to efficient transmission in the ferret model, this knowledge will aid in surveillance. If these amino acid mutations are observed to arise during natural selection in humans, timely actions could be taken.

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta's success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.

The Tetrameric Plant Lectin BanLec Neutralizes HIV through Bidentate Binding to Specific Viral Glycans

Select lectins have powerful anti-viral properties that effectively neutralize HIV-1 by targeting the dense glycan shield on the virus. Here, we reveal the mechanism by which one of the most potent lectins, BanLec, achieves its inhibition. We identify that BanLec recognizes a subset of high-mannose glycans via bidentate interactions spanning the two binding sites present on each BanLec monomer that were previously considered separate carbohydrate recognition domains. We show that both sites are required for high-affinity glycan binding and virus neutralization. Unexpectedly we find that BanLec adopts a tetrameric stoichiometry in solution whereby the glycan-binding sites are positioned to optimally target glycosylated viral spikes. The tetrameric architecture, together with bidentate binding to individual glycans, leads to layers of multivalency that drive viral neutralization through enhanced avidity effects. These structural insights will prove useful in engineering successful lectin therapeutics targeting the dense glycan shield of HIV.

Glycans Confer Specificity to the Recognition of Ganglioside Receptors by Botulinum Neurotoxin A

The highly poisonous botulinum neurotoxins, produced by the bacterium Clostridium botulinum, act on their hosts by a high-affinity association to two receptors on neuronal cell surfaces as the first step of invasion. The glycan motifs of gangliosides serve as initial coreceptors for these protein complexes, whereby a membrane protein receptor is bound. Herein we set out to characterize the carbohydrate minimal binding epitope of the botulinum neurotoxin serotype A. By means of ligand-based NMR spectroscopy, X-ray crystallography, computer simulations, and isothermal titration calorimetry, a screening of ganglioside analogues together with a detailed characterization of various carbohydrate ligand complexes with the toxin were accomplished. We show that the representation of the glycan epitope to the protein affects the details of binding. Notably, both branches of the oligosaccharide GD1a can associate to botulinum neurotoxin serotype A when expressed as individual trisaccharides. It is, however, the terminal branch of GD1a as well as this trisaccharide motif alone, corresponding to the sialyl-Thomsen-Friedenreich antigen, that represents the active ligand epitope, and these compounds bind to the neurotoxin with a high degree of predisposition but with low affinities. This finding does not correlate with the oligosaccharide moieties having a strong contribution to the total affinity, which was expected to be the case. We here propose that the glycan part of the ganglioside receptors mainly provides abundance and specificity, whereas the interaction with the membrane itself and protein receptor brings about the strong total binding of the toxin to the neuronal membrane.

Recent H3N2 Viruses Have Evolved Specificity for Extended, Branched Human-type Receptors, Conferring Potential for Increased Avidity

Human and avian influenza viruses recognize different sialic acid-containing receptors, referred to as human-type (NeuAcα2-6Gal) and avian-type (NeuAcα2-3Gal), respectively. This presents a species barrier for aerosol droplet transmission of avian viruses in humans and ferrets. Recent reports have suggested that current human H3N2 viruses no longer have strict specificity toward human-type receptors. Using an influenza receptor glycan microarray with extended airway glycans, we find that H3N2 viruses have in fact maintained human-type specificity, but they have evolved preference for a subset of receptors comprising branched glycans with extended poly-N-acetyl-lactosamine (poly-LacNAc) chains, a specificity shared with the 2009 pandemic H1N1 (Cal/04) hemagglutinin. Lipid-linked versions of extended sialoside receptors can restore susceptibility of sialidase-treated MDCK cells to infection by both recent (A/Victoria/361/11) and historical (A/Hong Kong/8/1968) H3N2 viruses. Remarkably, these human-type receptors with elongated branches have the potential to increase avidity by simultaneously binding to two subunits of a single hemagglutinin trimer.

Residue-centric modeling and design of saccharide and glycoconjugate structures

The RosettaCarbohydrate framework is a new tool for modeling a wide variety of saccharide and glycoconjugate structures. This report describes the development of the framework and highlights its applications. The framework integrates with established protocols within the Rosetta modeling and design suite, and it handles the vast complexity and variety of carbohydrate molecules, including branching and sugar modifications. To address challenges of sampling and scoring, RosettaCarbohydrate can sample glycosidic bonds, side-chain conformations, and ring forms, and it utilizes a glycan-specific term within its scoring function. Rosetta can work with standard PDB, GLYCAM, and GlycoWorkbench (.gws) file formats. Saccharide residue-specific chemical information is stored internally, permitting glycoengineering and design. Carbohydrate-specific applications described herein include virtual glycosylation, loop-modeling of carbohydrates, and docking of glyco-ligands to antibodies. Benchmarking data are presented and compared to other studies, demonstrating Rosetta's ability to predict glyco-ligand binding. The framework expands the tools available to glycoscientists and engineers. © 2016 Wiley Periodicals, Inc.

Combining 3D structure with glycan array data provides insight into the origin of glycan specificity

Defining how a glycan-binding protein (GBP) specifically selects its cognate glycan from among the ensemble of glycans within the cellular glycome is an area of intense study. Powerful insight into recognition mechanisms can be gained from 3D structures of GBPs complexed to glycans; however, such structures remain difficult to obtain experimentally. Here an automated 3D structure generation technique, called computational carbohydrate grafting, is combined with the wealth of specificity information available from glycan array screening. Integration of the array data with modeling and crystallography allows generation of putative co-complex structures that can be objectively assessed and iteratively altered until a high level of agreement with experiment is achieved. Given an accurate model of the co-complexes, grafting is also able to discern which binding determinants are active when multiple potential determinants are present within a glycan. In some cases, induced fit in the protein or glycan was necessary to explain the observed specificity, while in other examples a revised definition of the minimal binding determinants was required. When applied to a collection of 10 GBP-glycan complexes, for which crystallographic and array data have been reported, grafting provided a structural rationalization for the binding specificity of >90% of 1223 arrayed glycans. A webtool that enables researchers to perform computational carbohydrate grafting is available at www.glycam.org/gr (accessed 03 March 2016).

Integrated Omics and Computational Glycobiology Reveal Structural Basis for Influenza A Virus Glycan Microheterogeneity and Host Interactions

Despite sustained biomedical research effort, influenza A virus remains an imminent threat to the world population and a major healthcare burden. The challenge in developing vaccines against influenza is the ability of the virus to mutate rapidly in response to selective immune pressure. Hemagglutinin is the predominant surface glycoprotein and the primary determinant of antigenicity, virulence and zoonotic potential. Mutations leading to changes in the number of HA glycosylation sites are often reported. Such genetic sequencing studies predict at best the disruption or creation of sequons for N-linked glycosylation; they do not reflect actual phenotypic changes in HA structure. Therefore, combined analysis of glycan micro and macro-heterogeneity and bioassays will better define the relationships among glycosylation, viral bioactivity and evolution. We present a study that integrates proteomics, glycomics and glycoproteomics of HA before and after adaptation to innate immune system pressure. We combined this information with glycan array and immune lectin binding data to correlate the phenotypic changes with biological activity. Underprocessed glycoforms predominated at the glycosylation sites found to be involved in viral evolution in response to selection pressures and interactions with innate immune-lectins. To understand the structural basis for site-specific glycan microheterogeneity at these sites, we performed structural modeling and molecular dynamics simulations. We observed that the presence of immature, high-mannose type glycans at a particular site correlated with reduced accessibility to glycan remodeling enzymes. Further, the high mannose glycans at sites implicated in immune lectin recognition were predicted to be capable of forming trimeric interactions with the immune-lectin surfactant protein-D.