Development of a sugar-binding residue prediction system from protein sequences using support vector machine

Highly accurate carbohydrate-binding site prediction with DeepGlycanSite

As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5'-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins.

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.

A Surface Exposed, Two-Domain Lipoprotein Cargo of a Type XI Secretion System Promotes Colonization of Host Intestinal Epithelia Expressing Glycans

The only known required component of the newly described Type XI secretion system (TXISS) is an outer membrane protein (OMP) of the DUF560 family. TXISS_OMPs are broadly distributed across proteobacteria, but properties of the cargo proteins they secrete are largely unexplored. We report biophysical, histochemical, and phenotypic evidence that Xenorhabdus nematophila NilC is surface exposed. Biophysical data and structure predictions indicate that NilC is a two-domain protein with a C-terminal, 8-stranded β-barrel. This structure has been noted as a common feature of TXISS effectors and may be important for interactions with the TXISS_OMP. The NilC N-terminal domain is more enigmatic, but our results indicate it is ordered and forms a β-sheet structure, and bioinformatics suggest structural similarities to carbohydrate-binding proteins. X. nematophila NilC and its presumptive TXISS_OMP partner NilB are required for colonizing the anterior intestine of Steinernema carpocapsae nematodes: the receptacle of free-living, infective juveniles and the anterior intestinal cecum (AIC) in juveniles and adults. We show that, in adult nematodes, the AIC expresses a Wheat Germ Agglutinin (WGA)-reactive material, indicating the presence of N-acetylglucosamine or N-acetylneuraminic acid sugars on the AIC surface. A role for this material in colonization is supported by the fact that exogenous addition of WGA can inhibit AIC colonization by X. nematophila. Conversely, the addition of exogenous purified NilC increases the frequency with which X. nematophila is observed at the AIC, demonstrating that abundant extracellular NilC can enhance colonization. NilC may facilitate X. nematophila adherence to the nematode intestinal surface by binding to host glycans, it might support X. nematophila nutrition by cleaving sugars from the host surface, or it might help protect X. nematophila from nematode host immunity. Proteomic and metabolomic analyses of wild type X. nematophila compared to those lacking nilB and nilC revealed differences in cell wall and secreted polysaccharide metabolic pathways. Additionally, purified NilC is capable of binding peptidoglycan, suggesting that periplasmic NilC may interact with the bacterial cell wall. Overall, these findings support a model that NilB-regulated surface exposure of NilC mediates interactions between X. nematophila and host surface glycans during colonization. This is a previously unknown function for a TXISS.

In-silico Studies Calculated a New Chitin Oligomer Binding Site Inside Vicilin: A Potent Antifungal and Insecticidal Agent

Vicilins are major seed storage proteins and show differential binding affinities toward sugar moieties of fungal cell wall and insect gut epithelium. Hence, purpose of study is the thorough in-silico characterization of interactions between vicilin and chitin oligomer followed by fungal and insecticidal bioassays. This work covers the molecular simulation studies explaining the interactions between Pisum sativum vicilin (PsV) and chitin oligomer followed by protein bioassay against different pathogens. LC-MS/MS of purified PsV (∼50 kDa) generated residual data along high pea vicilin homology (UniProtKB ID; P13918). Predicted model (PsV) indicated the characteristic homotrimer joined through head-to-tail association and each monomer is containing a bicupin domain. PsV site map analysis showed a new site (Site 4) into which molecular docking confirmed the strong binding of chitin oligomer (GlcNAc)₄. Molecular dynamics simulation data (50 ns) indicated that chitin-binding site was comprised of 8 residues (DKEDRNEN). However, aspartate and glutamate significantly contributed in the stability of ligand binding. Computational findings were further verified via significant growth inhibition of Aspergillus flavus, A. niger, and Fusarium oxysporum against PsV. Additionally, the substantial adult population of Brevicoryne brassicae was reduced and different life stages of Tribolium castaneum also showed significant mortality.

Novel Few-Shot Learning Neural Network for Predicting Carbohydrate-Active Enzyme Affinity Toward Fructo-Oligosaccharides

The enzymatic activity of the microbiome toward carbohydrates in the human digestive system is of enormous health significance. Predicting how carbohydrates through food intake may affect the distribution and balance of gut microbiota remains a major challenge. Understanding the enzyme/substrate specificity relationship of the carbohydrate-active enzyme (CAZyme) encoded by the vast gut microbiome will be an important step to address this question. In this study, we seek to establish an in silico approach to studying the enzyme/substrate binding interaction. We focused on the key CAZyme and established a novel Poisson noise-based few-shot learning neural network (pFSLNN) for predicting the binding affinity of indigestible carbohydrates. This approach achieved higher accuracy than other classic FSLNNs, and we have also formulated new algorithms for feature generation using only a few amino acid (AA) sequences. Sliding bin regression is integrated with minimum redundancy maximum relevance for feature selection. The resulting pFSLNN is an efficient model to predict the binding affinity between CAZyme and common oligosaccharides. This model can be potentially applied to the binding affinity prediction of other protein/ligand interactions based on limited AA sequences.

Chemical Modifications of Vicilins Interfere with Chitin-Binding Affinity and Toxicity to Callosobruchus maculatus (Coleoptera: Chrysomelidae) Insect: A Combined In Vitro and In Silico Analysis

Vicilins are related to cowpea seed resistance toward Callosobruchus maculatus due to their ability to bind to chitinous structures lining larval midgut. However, this binding mechanism is not fully understood. Here, we identified chitin binding sites and investigated how in vitro and in silico chemical modifications interfere with vicilin chitin binding and insect toxicity. In vitro assays showed that unmodified vicilin strongly binds to chitin matrices, mainly with acetylated chitin. Chemical modifications of specific amino acids (tryptophan, lysine, tyrosine), as well as glutaraldehyde cross-linking, decreased the evaluated parameters. In silico analyses identified at least one chitin binding site in vicilin monomer, the region between Arg208 and Lys216, which bears the sequence REGIRELMK and forms an α helix, exposed in the 3D structure. In silico modifications of Lys223 (acetylated at its terminal nitrogen) and Trp316 (iodinated to 7-iodine-L-tryptophan or oxidized to β-oxy-indolylalanine) decreased vicilin chitin binding affinity. Glucose, sucrose, and N-acetylglucosamine also interfered with vicilin chitin binding affinity.

Structural and Functional Studies of a Klebsiella Phage Capsule Depolymerase Tailspike: Mechanistic Insights into Capsular Degradation

Capsule polysaccharide is a major virulence factor of Klebsiella pneumoniae, a nosocomial pathogen associated with a wide range of infections. It protects bacteria from harsh environmental conditions, immune system response, and phage infection. To access cell wall-located receptors, some phages possess tailspike depolymerases that degrade the capsular polysaccharide. Here, we present the crystal structure of a tailspike against Klebsiella, KP32gp38, whose primary sequence shares no similarity to other proteins of known structure. In the trimeric structure of KP32gp38, each chain contains a flexible N-terminal domain, a right-handed parallel β helix domain and two β sandwiches with carbohydrate binding features. The crystal structure and activity assays allowed us to locate the catalytic site. Also, our data provide experimental evidence of a branching architecture of depolymerases in KP32 Klebsiella viruses, as KP32gp38 displays nanomolar affinity to another depolymerase from the same phage, KP32gp37. Results provide a structural framework for enzyme engineering to produce serotype-broad-active enzyme complexes against K. pneumoniae.

Rational identification and characterisation of peptide ligands for targeting polysialic acid

The alpha-2,8-linked form of the polysaccharide polysialic acid (PSA) has widespread implications in physiological and pathological processes, ranging from neurological development to disease progression. Though the high electronegativity and excluded volume of PSA often promotes interference of biomolecular interactions, PSA-binding ligands have important implications for both biological processes and biotechnological applications. As such, the design, identification, and characterisation of novel ligands towards PSA is critical for expanding knowledge of PSA interactions and achieving selective glycan targeting. Here, we report on a rational approach for the identification of alpha-2,8-PSA-binding peptides, involving design from the endogenous ligand Siglec-11 and multi-platform characterisation of peptide binding. Microarray-based examination of peptides revealed charge and sequence characteristics influencing peptide affinity to PSA, and carbohydrate-peptide binding was further quantified with a novel fluorescence anisotropy assay. PSA-binding peptides exhibited specific binding to polymeric SA, as well as different degrees of selective binding in various conditions, including competition with PSA of alternating 2,8/9-linkages and screening with PSA-expressing cells. A computational study of Siglec-11 and Siglec-11-derived peptides offered synergistic insight into ligand binding. These results demonstrate the potential of PSA-binding peptides for selective targeting and highlight the importance of the approaches described herein for the study of carbohydrate interactions.

ProCarbDB: a database of carbohydrate-binding proteins

Carbohydrate-binding proteins play crucial roles across all organisms and viruses. The complexity of carbohydrate structures, together with inconsistencies in how their 3D structures are reported, has led to difficulties in characterizing the protein-carbohydrate interfaces. In order to better understand protein-carbohydrate interactions, we have developed an open-access database, ProCarbDB, which, unlike the Protein Data Bank (PDB), clearly distinguishes between the complete carbohydrate ligands and their monomeric units. ProCarbDB is a comprehensive database containing over 5200 3D X-ray crystal structures of protein-carbohydrate complexes. In ProCarbDB, the complete carbohydrate ligands are annotated and all their interactions are displayed. Users can also select any protein residue in the proximity of the ligand to inspect its interactions with the carbohydrate ligand and with other neighbouring protein residues. Where available, additional curated information on the binding affinity of the complex and the effects of mutations on the binding have also been provided in the database. We believe that ProCarbDB will be an invaluable resource for understanding protein-carbohydrate interfaces. The ProCarbDB web server is freely available at http://www.procarbdb.science/procarb.