bioRxivBiochemistry
Exploration, representation and rationalization of the conformational phase-space of N-glycans
Abstract
Despite their fundamental biological relevance, structure-property relationships in N-glycans are fundamentally lacking, and their highly multidimensional compositional and conformational phase-spaces remain largely unexplored. The torsional flexibility of the glycosidic linkages and the ring dynamics result in wide, rugged free-energy landscapes that are difficult to sample in molecular dynamics simulations. We show that a novel enhanced-sampling scheme combining replica-exchange with solute and collective-variable tempering, enabling transitions over all relevant energy barriers, delivers converged distributions of solvated N-glycan conformers. Several dimensionality-reduction algorithms are compared and employed to generate conformational free-energy maps in two-dimensions. Together with an originally developed conformation-based nomenclature scheme that uniquely identify glycan conformers, our modelling procedure is applied to reveal the effect of chemical substitutions on the conformational ensemble of selected high-mannose-type and complex glycans. Moreover, the structure-prediction capabilities of two commonly used glycan force fields are assessed via the theoretical prediction of experimentally available NMR J-coupling constants. The results confirm the key role of especially{omega} and {psi} torsion angles in discriminating between different conformational states, and suggest an intriguing correlation between the torsional and ring-puckering degrees of freedom that may be biologically relevant. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/496605v2_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@1cca03org.highwire.dtl.DTLVardef@12c789org.highwire.dtl.DTLVardef@ea2298org.highwire.dtl.DTLVardef@18554b5_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: structural biology
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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