Exploring the role of structural heterogeneity in transmembrane protein dimerization
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Citation
Abstract
The accurate simulation of realistic biological membranes is a long-term goal in the field of membrane biophysics. Efforts to simulate increasingly complex lipid bilayers, consisting of multiple lipid types and proteins, have been hindered by the shortcomings of current force fields, both coarse-grained and all-atom, in the modeling of membrane proteins and protein lipid interactions. Due to the fundamental importance of protein dimerization to cellular signaling and protein trafficking, the study of protein-protein association and the related dimerization free energies has received significant attention in both simulation and experiment. Detailed comparisons of simulation results with both protein dimer structures, derived from NMR and crystallography, and equilibrium constants, derived from experimental FRET assays, have been fruitful. They have served as a test of the accuracy of simulation methods and provided insight into the underlying structural distributions and thermodynamic driving forces defining the interactions. These comparisons have led to the conclusion that existing state-of-the-art simulation methods have failed to effectively sample the equilibrium between associated and dissociated states, leading to inaccurate estimates of binding constants and the misrepresentation of the associated structural ensembles. Here we discuss the drawbacks of previously used protocols and our systematic development of effective computational methods for the identification of collective variable and their use in enhanced sampling simulations that exhaustively sample the native and non-native dimer conformations and lead to precise estimates of the associated equilibrium binding constants. Our proposed protocols, using both intuition-derived and machine learning-based collective variables, were validated using unbiased simulations. These developed protocols are widely applicable, computationally efficient, and offer a proven approach to converged sampling of the free energy landscapes for membrane protein association, providing a standard to be used for future simulations of membrane proteins.
Description
2024
License
Attribution 4.0 International