Quantitative Assessment of Changes in Intrinsic Molecular Motion using Support Vector Machines

The ensemble of 3-d configurations exhibited by a biomolecule, that is, its intrinsic motion, can be altered by several environmental factors, and also by the binding of other molecules. Quantification of such induced changes in intrinsic motion is important because it provides a basis for relating thermodynamic changes to changes in molecular motion. This task is, however, challenging because it requires comparing two high-dimensional datasets. Traditionally, when analyzing molecular simulations, this problem is circumvented by first reducing the dimensions of the two ensembles separately, and then comparing summary statistics from the two ensembles against each other. However, since dimensionality reduction is carried out prior to ensemble comparison, such strategies are susceptible to artifactual biases from information loss. Here we introduce a method based on support vector machines that yields a normalized quantitative estimate for the difference between two ensembles after comparing them directly against one another. While this method can be applied to any molecular system, including non-biological molecules and crystals, here we show how it can be applied to identify the specific regions of a paramyxovirus G-protein that are affected by the binding of its preferred human receptor, Ephrin-B2. This protein-protein interaction essentially regulates viral fusion. Specifically, for every residue in the G-protein, we obtain separately a quantitative difference between the ensemble of configurations they sample in the presence and in the absence of Ephrin-B2. These ensembles were generated using molecular dynamics simulations. Rank-ordering and then mapping the residues that undergo the greatest change in motion on to the 3-d structure of the G-protein reveals that they are clustered primarily on a single contiguous facet of the protein, and include the set that is known experimentally to play a vital role in regulating viral fusion.