Identification of Motions in Membrane Proteins by Elastic Network Models and Their Experimental Validation

Abstract

Identifying the functional motions of membrane proteins is difficult because they range from large-scale collective dynamics to local small atomic fluctuations at different timescales that are difficult to measure experimentally due to the hydrophobic nature of these proteins. Elastic Network Models, and in particular their most widely used implementation, the Anisotropic Network Model (ANM), have proven to be useful computational methods in many recent applications to predict membrane protein dynamics. These models are based on the premise that biomolecules possess intrinsic mechanical characteristics uniquely defined by their particular architectures. In the ANM, interactions between residues in close proximity are represented by harmonic potentials with a uniform spring constant. The slow mode shapes generated by the ANM provide valuable information on the global dynamics of biomolecules that are relevant to their function. In its recent extension in the form of ANM-guided molecular dynamics (MD), this coarse-grained approach is augmented with atomic detail. The results from ANM and its extensions can be used to guide experiments and thus speedup the process of quantifying motions in membrane proteins. Testing the predictions can be accomplished through (a) direct observation of motions through studies of structure and biophysical probes, (b) perturbation of the motions by, e.g., cross-linking or site-directed mutagenesis, and (c) by studying the effects of such perturbations on protein function, typically through ligand binding and activity assays. To illustrate the applicability of the combined computational ANM-experimental testing framework to membrane proteins, we describe-alongside the general protocols-here the application of ANM to rhodopsin, a prototypical member of the pharmacologically relevant G-protein coupled receptor family.