Different Physical Parameters in the Bonds between Platelet Glycoprotein Ibalpha with von Willebrand factor and with Coagulant Factor XI -Results from the Molecular Dynamic Simulation-
Nakayama, M.; Goto, S.; Takemoto, S.; Oka, H.; Yokota, H.; Takagi, S.; Goto, S.
Show abstract
BackgroundBoth von Willebrand factor (VWF) and coagulation factor XI (FXI) bind with platelet membrane glycoprotein (GP) Ib. However, the differences in the physical parameters in the bonds between VWF-GPIb and FXI-GPIb mediating different biological functions are unclear. MethodsThe FXI molecule was arranged in 9 different initial positions around the structure of GPIb bound to VWF. The position coordinate and velocity vectors of all atoms constructing VWF, GPIb, and FXI were calculated in each 2 femto (10-15) second using the Chemistry at HARvard Macromolecular Mechanics (CHARMM) force field. The physical parameters of VWF-GPIb and FXI-GPIb bonds were calculated by molecular dynamic (MD) simulations. ResultsMD calculation revealed 2.8 to 11.3 times greater positional fluctuations in atoms constructing FXI-GPIb as compared to those constructing VWF-GPIb (RMSDs: 5.9{+/-}1.5 to 18.1{+/-}7.9 [A] for FXI-GPIb vs 1.6{+/-}0.1 to 2.1{+/-}0.3 [A] for VWF-GPIb). The absolute value of non-covalent binding energy generated in FXI-GPIb (65.5{+/-}79.7 to 517.6{+/-}54.2 kcal/mol) was smaller than that generated in VWF-GPIb (678.5{+/-}58.3 to 1000.4{+/-}75.1 kcal/mol). The binding structure of VWF-GPIb was stable and was only minimally influenced by the presence of FXI-GPIb binding. ConclusionsOur MD calculation results revealed that atoms constructing the VWF-GPIb bond are physically more stable and produce more non-covalent binding energy than the bond of FXI-GPIb. The physical parameters in the VWF-GPIb bond were not largely influenced by FXI binding with GPIb.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Natural variants of von Willebrand factor R1205 causing von Willebrand disease with accelerated von Willebrand factor clearance: in silico docking models and energetics of the interaction with both LRP1 and GpIb A1 domain 94%
- Large-scale, dynamin-like motions of the human guanylate binding protein 1 revealed by multi-resolution simulations 92%
- Pathfinder: protein folding pathway prediction based on conformational sampling 92%
Similar papers in this journal
- The Hidden Potential of PDE4 Inhibitor Rolipram: A Multifaceted Examination of its Inhibition of MMP2/9 Reveals Therapeutic Implications 96%
- Mechanistic insights into the deleterious role of nasu-hakola disease associated TREM2 variants 96%
- S494 O-glycosylation site on the SARS-COV-2 RBD Affects the Virus Affinity to ACE2 and its Infectivity; A Molecular Dynamics Study 96%
Similar papers in this journal
- Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8 95%
- Computational Analysis of the Metal Selectivity of Matrix Metalloproteinase 8 95%
- Molecular Dynamics Study on the Effects of Charged Amino Acid Distribution Under low pH Condition to the Unfolding of Hen Egg White Lysozyme and Formation of Beta Strands. 94%
Similar papers in this journal
- Dynamic conformational states of apo and cabozantinib bound TAM kinases to differentiate active-inactive kinetic models 95%
- Investigating the role of N-terminal domain in phosphodiesterase 4B-inhibition by molecular dynamics simulation 95%
- An insight into SARS-CoV-2 Membrane protein interaction with Spike, Envelope, and Nucleocapsid proteins 95%
Similar papers in this journal
- DeepSCM: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity 95%
- Comparative effects of oncogenic mutations G12C, G12V, G13D, and Q61H on local conformations and dynamics of K-Ras 93%
- Molecular mechanism of SARS-CoV-2 inactivation by temperature 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.