The ensemble of 3-D configurations exhibited by a molecule, 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 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 data sets. 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 (JCTC 2013 9:868-875) that yields a normalized quantitative estimate for the difference between two ensembles after comparing them directly against one another. Such ensemble-wide comparisons become critical especially in cases where no particular single mode of molecular motion, like translation or fluctuation, dominates, and a proper quantitative assessment requires simultaneous consideration of all modes of motions. While this method can be applied to any molecular system, including nonbiological molecules and crystals, here we will discuss its application to identifying allosteric mechanisms underlying the fusion of paramyxoviruses with host cells. Fusion of the paramyxovirus Nipah is mediated by its fusion protein, which is activated allosterically through Nipah's attachment protein when the attachment protein binds to host cell receptors. The changes in the motion of the Nipah attachment protein induced by the binding of its host receptor are small, and an ensemble-wide comparison is necessary. The set of residues identified by this method that are involved in allosteric fusion activation include those implicated by alanine-scanning mutagenesis experiments.

Beyond comparison of two structures: Using machine learning to quantify changes in molecular motion

246th National Meeting (2013)

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