The Interaction of Fluorinated Glycomimetics with DC-SIGN: Multiple Binding Modes Disentangled by the Combination of NMR Methods and MD Simulations

Abstract

Fluorinated glycomimetics are frequently employed to study and eventually modulate protein–glycan interactions. However, complex glycans and their glycomimetics may display multiple binding epitopes that enormously complicate the access to a complete picture of the protein–ligand complexes. We herein present a new methodology based on the synergic combination of experimental 19F-based saturation transfer difference (STD) NMR data with computational protocols, applied to analyze the interaction between DC-SIGN, a key lectin involved in inflammation and infection events with the trifluorinated glycomimetic of the trimannoside core, ubiquitous in human glycoproteins. A novel 2D-STD-TOCSYreF NMR experiment was employed to obtain the experimental STD NMR intensities, while the Complete Relaxation Matrix Analysis (CORCEMA-ST) was used to predict that expected for an ensemble of geometries extracted from extensive MD simulations. Then, an in-house built computer program was devised to find the ensemble of structures that provide the best fit between the theoretical and the observed STD data. Remarkably, the experimental STD profiles obtained for the ligand/DC-SIGN complex could not be satisfactorily explained by a single binding mode, but rather with a combination of different modes coexisting in solution. Therefore, the method provides a precise view of those ligand–receptor complexes present in solution.

Kinetic Studies of Acetyl Group Migration between the Saccharide Units in an Oligomannoside Trisaccharide Model Compound and a Native Galactoglucomannan Polysaccharide

“…[53][54][55] Its binding preference for d-mannose and l-fucose sugar units has been extensively studied. [56][57][58][59] Herein, the evolution of compounds 1 a, 1 b and 2 in the presence of DC-SIGN was followed over time by acquiring 1 H NMR and 1 H- 13 C HSQC spectra, and the interaction with the lectin was investigated through Saturation Transfer Difference-NMR (STD-NMR) experiments.…”

Investigation of the Molecular Details of the Interactions of Selenoglycosides and Human Galectin-3

“…In summary, seleno analogues of natural ligands of h Gal-3 offer a unique, new set of compounds to become versatile tools in the interaction analysis, particularly by NMR spectroscopy, X crystallography or fluorescence analysis. Teaming up selenium labelling [ 30 , 76 , 77 ] with substitutions at strategic sites of a glycan by 19 F [ 78 , 79 ] offers further intriguing possibilities, via different NMR time scales due to their individual chemical shift ranges, to analyze the structures and dynamics of the glycan ligand at a new level.…”

Fast Validation of Static and Dynamic 3D Models of Weak Protein Ligand Complexes from STD NMR Spectroscopy

“…[25] The PsLBP reverse reaction (synthesis of glycosidic linkages) is of high significance nowadays as it can be used as an alternative way of enzymatic glycosylation using sugar 1-phosphates as donor substrates. [21,29] For the RedMat calculation, we used a rotational correlation time of the protein of 68.5 ns, estimated with HYDRONMR [27] (GH94 has a molecular weight of ≈ 102 kDa), and a dissociation constant of 2000 μM. The concentrations of ligand and protein were 5000 μM and 50 μM, respectively, as per experimental conditions.…”

“…To reduce the computational burden, the application of these full matrix approaches in the literature has been customarily limited to the case of "one saturation time" analysis of MD trajectories. [21] However, this type of analysis is extremely prone to false optimum structures when scoring is based on a best fitting factor value (NOE R-factor), as the full matrix calculation is not used to predict the whole build-up of saturation. Thus, whereas a particular protein-ligand complex can give a bad (high) R-factor when considering the whole STD build-up curve, a relatively good (low) R-factor might be obtained for the same complex when using a single saturation time, hence misleading the model validation.…”