Dataset Information

Understanding the specificity of human Galectin-8C domain interactions with its glycan ligands based on molecular dynamics simulations.

ABSTRACT: Human Galectin-8 (Gal-8) is a member of the galectin family which shares an affinity for ?-galactosides. The tandem-repeat Gal-8 consists of a N- and a C-terminal carbohydrate recognition domain (N- and C-CRD) joined by a linker peptide of various length. Despite their structural similarity both CRDs recognize different oligosaccharides. While the molecular requirements of the N-CRD for high binding affinity to sulfated and sialylated glycans have recently been elucidated by crystallographic studies of complexes with several oligosaccharides, the binding specificities of the C-CRD for a different set of oligosaccharides, as derived from experimental data, has only been explained in terms of the three-dimensional structure for the complex C-CRD with lactose. In this study we performed molecular dynamics (MD) simulations using the recently released crystal structure of the Gal-8C-CRD to analyse the three-dimensional conditions for its specific binding to a variety of oligosaccharides as previously defined by glycan-microarray analysis. The terminal ?-galactose of disaccharides (LacNAc, lacto-N-biose and lactose) and the internal ?-galactose moiety of blood group antigens A and B (BGA, BGB) as well as of longer linear oligosaccharide chains (di-LacNAc and lacto-N-neotetraose) are interacting favorably with conserved amino acids (H53, R57, N66, W73, E76). Lacto-N-neotetraose and di-LacNAc as well as BGA and BGB are well accommodated. BGA and BGB showed higher affinity than LacNAc and lactose due to generally stronger hydrogen bond interactions and water mediated hydrogen bonds with ?1-2 fucose respectively. Our results derived from molecular dynamics simulations are able to explain the glycan binding specificities of the Gal-8C-CRD in comparison to those of the Gal-8N -CRD.

SUBMITTER: Kumar S

PROVIDER: S-EPMC3612102 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Understanding the specificity of human Galectin-8C domain interactions with its glycan ligands based on molecular dynamics simulations.

Kumar Sonu S Frank Martin M Schwartz-Albiez Reinhard R

PloS one 20130329 3

Human Galectin-8 (Gal-8) is a member of the galectin family which shares an affinity for β-galactosides. The tandem-repeat Gal-8 consists of a N- and a C-terminal carbohydrate recognition domain (N- and C-CRD) joined by a linker peptide of various length. Despite their structural similarity both CRDs recognize different oligosaccharides. While the molecular requirements of the N-CRD for high binding affinity to sulfated and sialylated glycans have recently been elucidated by crystallographic stu ...[more]

PMID: 23555773

Similar Datasets

Project description:gal-1 (galectin-1) mediates cell-cell and cell-extracellular matrix adhesion, essentially by interacting with beta-galactoside-containing glycans of cell-surface glycoconjugates. Although most structural studies with gal-1 have investigated its binding to simple carbohydrates, in particular lactose and N-acetyl-lactosamine, this view is limited, because gal-1 functions at the cell surface by interacting with more complex glycans that are heterogeneous in size and composition. In the present study we used NMR spectroscopy to investigate the interaction of human gal-1 with a large (120 kDa) complex glycan, GRG (galactorhamnogalacturonate glycan), that contains non-randomly distributed mostly terminal beta(1-->4)-linked galactose side chains. We used 15N-1H-HSQC (heteronuclear single quantum coherence) NMR experiments with 15N-enriched gal-1 to identify the GRG-binding region on gal-1 and found that this region covers a large surface area on gal-1 that includes the quintessential lactose-binding site and runs from that site through a broad valley or cleft towards the dimer interface. HSQC and pulsed-field-gradient NMR diffusion experiments also show that gal-1 binds GRG with a gal-1:GRG stoichiometry of about 5:1 (or 6:1) and with average macroscopic and microscopic equilibrium dissociation constants (Kd) of 8 x 10(-6) M and 40 x 10(-6) M (or 48 x 10(-6) M) respectively, indicating stronger binding than to lactose (Kd=520 x 10(-6) M). Although gal-1 may bind GRG in various ways, the glycan can be competed for by lactose, suggesting that there is one major mode of interaction. Furthermore, even though terminal motifs on GRG are Gal-beta(1-->4)-Gal rather than the traditional Gal-beta(1-->4)-Glc/GlcNAc (where GlcNAc is N-acetylglucosamine), we show that the disaccharide Gal-beta(1-->4)-Gal can bind gal-1 at the lactose-binding domain. In addition, gal-1 binding to GRG disrupts inter-glycan interactions and decreases glycan-mediated solution viscosity, a glycan decongestion effect that may help explain why gal-1 promotes membrane fluidity and lateral diffusion of glycoconjugates within cell membranes. Overall, our results provide an insight into the function of galectin in situ and have potential significant biological consequences.

Project description:The predictable engineering of well-behaved transcriptional circuits is a central goal of synthetic biology. The artificial attachment of promoters to transcription factor genes usually results in noisy or chaotic behaviors, and such systems are unlikely to be useful in practical applications. Natural transcriptional regulation relies extensively on protein-protein interactions to insure tightly controlled behavior, but such tight control has been elusive in engineered systems. To help engineer protein-protein interactions, we have developed a molecular dynamics simulation framework that simplifies features of proteins moving by constrained Brownian motion, with the goal of performing long simulations. The behavior of a simulated protein system is determined by summation of forces that include a Brownian force, a drag force, excluded volume constraints, relative position constraints, and binding constraints that relate to experimentally determined on-rates and off-rates for chosen protein elements in a system. Proteins are abstracted as spheres. Binding surfaces are defined radially within a protein. Peptide linkers are abstracted as small protein-like spheres with rigid connections. To address whether our framework could generate useful predictions, we simulated the behavior of an engineered fusion protein consisting of two 20,000 Da proteins attached by flexible glycine/serine-type linkers. The two protein elements remained closely associated, as if constrained by a random walk in three dimensions of the peptide linker, as opposed to showing a distribution of distances expected if movement were dominated by Brownian motion of the protein domains only. We also simulated the behavior of fluorescent proteins tethered by a linker of varying length, compared the predicted Förster resonance energy transfer with previous experimental observations, and obtained a good correspondence. Finally, we simulated the binding behavior of a fusion of two ligands that could simultaneously bind to distinct cell-surface receptors, and explored the landscape of linker lengths and stiffnesses that could enhance receptor binding of one ligand when the other ligand has already bound to its receptor, thus, addressing potential mechanisms for improving targeted signal transduction proteins. These specific results have implications for the design of targeted fusion proteins and artificial transcription factors involving fusion of natural domains. More broadly, the simulation framework described here could be extended to include more detailed system features such as non-spherical protein shapes and electrostatics, without requiring detailed, computationally expensive specifications. This framework should be useful in predicting behavior of engineered protein systems including binding and dissociation reactions.

Dataset Information

Understanding the specificity of human Galectin-8C domain interactions with its glycan ligands based on molecular dynamics simulations.

Publications

Understanding the specificity of human Galectin-8C domain interactions with its glycan ligands based on molecular dynamics simulations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets