ABSTRACT
KIF1A is a kinesin family protein that moves over a long distance along the microtubule (MT) to transport synaptic vesicle precursors in neurons. A single KIF1A molecule can move toward the plus-end of MT in the monomeric form, exhibiting the characteristics of biased Brownian motion. However, how the bias is generated in the Brownian motion of KIF1A has not yet been firmly established. To elucidate this, we conducted a set of molecular dynamics simulations and observed the binding of KIF1A to MT. We found that KIF1A exhibits biased Brownian motion along MT as it binds to MT. Furthermore, we show that the bias toward the plus-end is generated by the ratchet-like energy landscape for the KIF1A-MT interaction, in which the electrostatic interaction and the negatively-charged C-terminal tail (CTT) of tubulin play an essential role. The relevance to the post-translational modifications of CTT is also discussed.
Subject(s)
Biophysical Phenomena , Kinesins/chemistry , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Interaction Domains and Motifs , Tubulin/chemistry , Algorithms , Axonal Transport , Kinesins/metabolism , Microtubules/chemistry , Microtubules/metabolism , Models, Theoretical , Neurons/metabolism , Protein Binding , Tubulin/metabolismABSTRACT
The generalize Born (GB) model is frequently used in MD simulations of biomolecular systems in aqueous solution. The GB model is usually based on the so-called Coulomb field approximation (CFA) for the energy density integration. In this study, we report that the GB model with CFA overdestabilizes the long-range electrostatic attraction between oppositely charged molecules (ionic bond forming two-helix system and kinesin-tubulin system) when the energy density integration cutoff, rmax, which is used to calculate the Born energy, is set to a large value. We show that employing large rmax, which is usually expected to make simulation results more accurate, worsens the accuracy so that the attraction is changed into repulsion. It is demonstrated that the overdestabilization is caused by the overestimation of the desolvation penalty upon binding that originates from CFA. We point out that the overdestabilization can be corrected by employing a relatively small cutoff (rmax = 10-15 Å), affirming that the GB models, even with CFA, can be used as a powerful tool to theoretically study the protein-protein interaction, particularly on its dynamical aspect, such as binding and unbinding.
