bioRxivBiochemistry
Ligand Induced Conformational and Dynamical Changes in a GT-B Glycosyltransferase:Molecular Dynamic Simulations of Heptosyltransferase I Apo, Binary and Ternary Complexes
Abstract
Understanding the dynamical motions and ligand recognition motifs of specific glycosyltransferase enzymes, like Heptosyltransferase I (HepI), is critical to discerning the behavior of other carbohydrate binding enzymes. Prior studies in our lab demonstrated that glycosyltransferases in the GT-B structural class, which are characterized by their connection of two Rossman-like domains by a linker region, have conservation of both structure and dynamical motions, despite low sequence conservation, therefore making discoveries found in HepI transferable to other GT-B enzymes. Through a series of 100 nanosecond Molecular Dynamics simulations of HepI in apo enzyme state, and also in the binary and ternary complexes with the native substrates/products. Ligand free energy analysis allowed determination of an anticipated enzymatic path for ligand binding and release. Principle component, dynamic cross correlation and network analyses of the simulation trajectories revealed that there are not only correlated motions between the N- and C-termini, but also that residues within the N-terminal domain communicate via a path that includes substrate proximal residues of the C-terminal domain. Analysis of structural changes, energetics of substrate/products binding and changes in pKa have elucidated a variety of inter- and intradomain interactions that are critical for catalysis. These data corroborate and allow visualization of previous experimental observations of protein conformational changes of HepI. This study has provided valuable insights into the regions involved in HepI conformational rearrangement upon ligand binding, and are likely to enhance efforts to develop new dynamics disrupting enzyme inhibitors for GT-B structural enzymes in the future.
- assay: protein structure
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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