Linking 3D and 2D binding kinetics of membrane proteins by multiscale simulations

Abstract

Membrane proteins are among the most functionally important proteins in cells. Unlike soluble proteins, they only possess two translational degrees of freedom on cell surfaces, and experience significant constraints on their rotations. As a result, it is currently challenging to characterize the in situ binding of membrane proteins. Using the membrane receptors CD2 and CD58 as a testing system, we developed a multiscale simulation framework to study the differences of protein binding kinetics between 3D and 2D environments. The association and dissociation processes were implemented by a coarse-grained Monte-Carlo algorithm, while the dynamic properties of proteins diffusing on lipid bilayer were captured from all-atom molecular dynamic simulations. Our simulations show that molecular diffusion, linker flexibility and membrane fluctuations are important factors in adjusting binding kinetics. Moreover, by calibrating simulation parameters to the measurements of 3D binding, we derived the 2D binding constant which is quantitatively consistent with the experimental data, indicating that the method is able to capture the difference between 3D and 2D binding environments. Finally, we found that the 2D dissociation between CD2 and CD58 is about 100-fold slower than the 3D dissociation. In summary, our simulation framework offered a generic approach to study binding mechanisms of membrane proteins.

Cadherin clusters stabilized by a combination of specific and nonspecific cis-interactions

“…The two-dimensional translational diffusion constant of a single E-cad protein on a lipid bilayer is taken as 10 μm 2 /s and the rotational coefficient as 1° per ns. The values of these parameters were derived from our previous all-atom molecular dynamic simulation results for the diffusions of a cell-surface protein on the lipid bilayer ( Xie et al, 2014b ).…”

Computational simulations of TNF receptor oligomerization on plasma membrane

“…However, current applications of all‐atom (AA) MD simulations to more complicated behaviors of cell surface proteins in their membrane environments such as oligomerization are not allowed due to the intense consumption for computational resources. As a result, coarse‐grained (CG) force fields have been developed to probe the dynamics of larger molecular systems with longer timescales by sacrificing the information of interactions on the atomic level . Among a large variety of CG approaches, the Martini force field was constructed by extensively calibrating the nonbonded interactions between CG building blocks of basic chemical units against the experimental data .…”

“…As a result, coarse-grained (CG) force fields have been developed to probe the dynamics of larger molecular systems with longer timescales by sacrificing the information of interactions on the atomic level. [27][28][29][30][31][32][33][34][35][36] Among a large variety of CG approaches, the Martini force field was constructed by extensively calibrating the nonbonded interactions between CG building blocks of basic chemical units against the experimental data. 37 The CG simulations with the Martini force field were successfully applied to reproduce the dynamic processes of lipid systems such as self-assembly of lipid bilayers.…”

A Computational Model for Kinetic Studies of Cadherin Binding and Clustering

“…Finally, the binding constants of cadherin's cis interactions are not experimentally measurable. These values can be derived from computational simulations on a higher-resolution level (74)(75)(76)(77) and fed into the RB model by a multiscale framework (78). As such, our model would allow more quantitative comparisons with experiments.…”