A computational approach for exploring carbohydrate recognition by lectins in innate immunity

Sequence variety in the CC' loop of Siglec-8/9/3 determines the recognitions to sulfated oligosaccharides

Siglecs are important lectins found in different types of immune cells and function as regulatory molecules by recognizing self-associated glycans and converting extracellular interactions into signals for inhibiting immune cell functions. Although many Siglecs have been found to show broad specificities and recognize different types of sulfated oligosaccharides, Siglec-8 and Siglec-9 displayed a high degree of specificity for sialyl N-acetyllactosamine (sLacNAc) with sulfations at O6-positions of the galactose (6'-sulfation) and N-acetylglucosamine (6-sulfation), respectively. Siglec-3 was recently discovered to bind sLacNAc both sulfations. In addition to a conserved arginine residue for binding to sialic acid residue, the sequence variety in the CC' loop may provide binding specificities to sulfated oligosaccharides in Siglecs. Thus, the present study employed molecular models to study the impact of different residues in the CC' loops of Siglec-8/9/3 to the recognitions of 6-sulfations in Gal and/or GlcNAc of sLacNAc. The negatively charged residues in the CC' loop of Siglec-9 formed unfavorable electrostatic repulsions with the 6-sulfate in Gal and resulted no recognitions, in contrast to the favorable interactions formed between the positively charged residues in the CC' loop of Siglec-8 and the 6-sulfate in Gal resulting strong specificity. A two-state binding model was proposed for Siglec-3 recognizing 6-sulfations in Gal and GlcNAc of sLacNAc, as the neutral residues in the CC' loop of Siglec-3 could not form strong favorable interactions to lock the 6-sulfate in Gal within a single binding pose or strong unfavorable interactions to repel the 6-sulfate in Gal. The oligosaccharide adopted two distinctive binding poses and oriented the sulfate groups to form interactions with residues in the CC' loop and G-strand. The present study provided a structural mechanism for the sequence variety in the CC' loop of Siglec-8/9/3 determining the recognitions to the sulfated oligosaccharides and offered insights into the binding specificities for Siglecs.

Plant lectins and their many roles: Carbohydrate-binding and beyond

Lectins are ubiquitous proteins that reversibly bind to specific carbohydrates and, thus, serve as readers of the sugar code. In photosynthetic organisms, lectin family proteins play important roles in capturing and releasing photosynthates via an endogenous lectin cycle. Often, lectin proteins consist of one or more lectin domains in combination with other types of domains. This structural diversity of lectins is the basis for their current classification, which is consistent with their diverse functions in cell signaling associated with growth and development, as well as in the plant's response to biotic, symbiotic, and abiotic stimuli. Furthermore, the lectin family shows evolutionary expansion that has distinct clade-specific signatures. Although the function(s) of many plant lectin family genes are unknown, studies in the model plant Arabidopsis thaliana have provided insights into their diverse roles. Here, we have used a biocuration approach rooted in the critical review of scientific literature and information available in the public genomic databases to summarize the expression, localization, and known functions of lectins in Arabidopsis. A better understanding of the structure and function of lectins is expected to aid in improving agricultural productivity through the manipulation of candidate genes for breeding climate-resilient crops, or by regulating metabolic pathways by applications of plant growth regulators.

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.

Comprehensive analysis of carbohydrate-protein recognition in the Protein Data Bank

Carbohydrate-protein interactions underpin wide-ranging aspects of biology. However, such interactions remain relatively unexplored in pharmaceutical and biotechnological applications, in part due to the challenges associated with their structural characterisation, both experimentally and computationally. Knowledge-based approaches have shown great success in the prediction of drug-protein and protein-protein interactions, although have not been comprehensively investigated for carbohydrate-protein interactions. In this work, carbohydrate-protein complexes from the Protein Data Bank were comprehensively obtained and analysed to identify patterns in how carbohydrate-protein interactions are mediated.

Molecular Simulations of Carbohydrates with a Fucose-Binding Burkholderia ambifaria Lectin Suggest Modulation by Surface Residues Outside the Fucose-Binding Pocket

Burkholderia ambifaria is an opportunistic respiratory pathogen belonging to the Burkholderia cepacia complex, a collection of species responsible for the rapidly fatal cepacia syndrome in cystic fibrosis patients. A fucose-binding lectin identified in the B. ambifaria genome, BambL, is able to adhere to lung tissue, and may play a role in respiratory infection. X-ray crystallography has revealed the bound complex structures for four fucosylated human blood group epitopes (blood group B, H type 1, H type 2, and Le^x determinants). The present study employed computational approaches, including docking and molecular dynamics (MD), to extend the structural analysis of BambL-oligosaccharide complexes to include four additional blood group saccharides (A, Le^a, Le^b, and Le^y) and a library of blood-group-related carbohydrates. Carbohydrate recognition is dominated by interactions with fucose via a hydrogen-bonding network involving Arg15, Glu26, Ala38, and Trp79 and a stacking interaction with Trp74. Additional hydrogen bonds to non-fucose residues are formed with Asp30, Tyr35, Thr36, and Trp74. BambL recognition is dominated by interactions with fucose, but also features interactions with other parts of the ligands that may modulate specificity or affinity. The detailed computational characterization of the BambL carbohydrate-binding site provides guidelines for the future design of lectin inhibitors.

Antibody-Carbohydrate Recognition from Docked Ensembles Using the AutoMap Procedure

Carbohydrate-protein recognition is vital to many processes in health and disease. In particular, elucidation of the structural basis of carbohydrate binding is important to the development of oligosaccharides and oligosaccharide mimetics as vaccines for infectious diseases and cancer. Computational structural techniques are valuable for the study of carbohydrate-protein recognition due to the challenges associated with experimental determination of carbohydrate-protein complexes. AutoMap is a computer program that we have developed to study protein-ligand recognition. AutoMap determines the interactions taking place in a set of highly ranked poses obtained from molecular docking and processes these to identify the protein residues most likely to be involved in interactions. In this protocol, we describe the use of AutoMap and illustrate its suitability for studying antibody recognition of the Lewis Y tetrasaccharide, which is a potential cancer vaccine antigen.

Carbohydrate-protein interactions: molecular modeling insights

The article reviews the significant contributions to, and the present status of, applications of computational methods for the characterization and prediction of protein-carbohydrate interactions. After a presentation of the specific features of carbohydrate modeling, along with a brief description of the experimental data and general features of carbohydrate-protein interactions, the survey provides a thorough coverage of the available computational methods and tools. At the quantum-mechanical level, the use of both molecular orbitals and density-functional theory is critically assessed. These are followed by a presentation and critical evaluation of the applications of semiempirical and empirical methods: QM/MM, molecular dynamics, free-energy calculations, metadynamics, molecular robotics, and others. The usefulness of molecular docking in structural glycobiology is evaluated by considering recent docking- validation studies on a range of protein targets. The range of applications of these theoretical methods provides insights into the structural, energetic, and mechanistic facets that occur in the course of the recognition processes. Selected examples are provided to exemplify the usefulness and the present limitations of these computational methods in their ability to assist in elucidation of the structural basis underlying the diverse function and biological roles of carbohydrates in their dialogue with proteins. These test cases cover the field of both carbohydrate biosynthesis and glycosyltransferases, as well as glycoside hydrolases. The phenomenon of (macro)molecular recognition is illustrated for the interactions of carbohydrates with such proteins as lectins, monoclonal antibodies, GAG-binding proteins, porins, and viruses.

Solvent structure improves docking prediction in lectin-carbohydrate complexes

Recognition and complex formation between proteins and carbohydrates is a key issue in many important biological processes. Determination of the three-dimensional structure of such complexes is thus most relevant, but particularly challenging because of their usually low binding affinity. In silico docking methods have a long-standing tradition in predicting protein-ligand complexes, and allow a potentially fast exploration of a number of possible protein-carbohydrate complex structures. However, determining which of these predicted complexes represents the correct structure is not always straightforward. In this work, we present a modification of the scoring function provided by AutoDock4, a widely used docking software, on the basis of analysis of the solvent structure adjacent to the protein surface, as derived from molecular dynamics simulations, that allows the definition and characterization of regions with higher water occupancy than the bulk solvent, called water sites. They mimic the interaction held between the carbohydrate -OH groups and the protein. We used this information for an improved docking method in relation to its capacity to correctly predict the protein-carbohydrate complexes for a number of tested proteins, whose ligands range in size from mono- to tetrasaccharide. Our results show that the presented method significantly improves the docking predictions. The resulting solvent-structure-biased docking protocol, therefore, appears as a powerful tool for the design and optimization of development of glycomimetic drugs, while providing new insights into protein-carbohydrate interactions. Moreover, the achieved improvement also underscores the relevance of the solvent structure to the protein carbohydrate recognition process.

Antibody recognition of cancer-related gangliosides and their mimics investigated using in silico site mapping

Modified gangliosides may be overexpressed in certain types of cancer, thus, they are considered a valuable target in cancer immunotherapy. Structural knowledge of their interaction with antibodies is currently limited, due to the large size and high flexibility of these ligands. In this study, we apply our previously developed site mapping technique to investigate the recognition of cancer-related gangliosides by anti-ganglioside antibodies. The results reveal a potential ganglioside-binding motif in the four antibodies studied, suggesting the possibility of structural convergence in the anti-ganglioside immune response. The structural basis of the recognition of ganglioside-mimetic peptides is also investigated using site mapping and compared to ganglioside recognition. The peptides are shown to act as structural mimics of gangliosides by interacting with many of the same binding site residues as the cognate carbohydrate epitopes. These studies provide important clues as to the structural basis of immunological mimicry of carbohydrates.