Characterization of Heparin's Conformational Ensemble by Molecular Dynamics Simulations and Nuclear Magnetic Resonance Spectroscopy.
J Joel JankeYanlei YuVitor H PominJing ZhaoChunyu WangRobert J LinhardtAngel E GarciaPublished in: Journal of chemical theory and computation (2022)
Heparin is a highly charged, polysulfated polysaccharide and serves as an anticoagulant. Heparin binds to multiple proteins throughout the body, suggesting a large range of potential therapeutic applications. Although its function has been characterized in multiple physiological contexts, heparin's solution conformational dynamics and structure-function relationships are not fully understood. Molecular dynamics (MD) simulations facilitate the analysis of a molecule's underlying conformational ensemble, which then provides important information necessary for understanding structure-function relationships. However, for MD simulations to afford meaningful results, they must both provide adequate sampling and accurately represent the energy properties of a molecule. The aim of this study is to compare heparin's conformational ensemble using two well-developed force fields for carbohydrates, known as GLYCAM06 and CHARMM36, using replica exchange molecular dynamics (REMD) simulations, and to validate these results with NMR experiments. The anticoagulant sequence, an ultra-low-molecular-weight heparin, known as Arixtra (fondaparinux, sodium), was simulated with both parameter sets. The results suggest that GLYCAM06 matches experimental nuclear magnetic resonance three-bond J -coupling values measured for Arixtra better than CHARMM36. In addition, NOESY and ROESY experiments suggest that Arixtra is very flexible in the sub-millisecond time scale and does not adopt a unique structure at 25 C. Moreover, GLYCAM06 affords a much more dynamic conformational ensemble for Arixtra than CHARMM36.
Keyphrases
- molecular dynamics
- venous thromboembolism
- molecular dynamics simulations
- density functional theory
- magnetic resonance
- growth factor
- convolutional neural network
- atrial fibrillation
- neural network
- molecular docking
- healthcare
- single molecule
- machine learning
- room temperature
- computed tomography
- deep learning
- amino acid
- health information
- social media
- water soluble