Variational Selection of Features for Molecular Kinetics
This addresses a computational bottleneck in molecular kinetics modeling for researchers, though it is incremental as it optimizes an existing pipeline step.
The paper tackles the problem of selecting interpretable features for building kinetic models from molecular dynamics simulations, presenting a method that directly optimizes feature choice without constructing the full model, and demonstrates it leads to more efficient model selection on twelve fast-folding protein simulations.
The modeling of atomistic biomolecular simulations using kinetic models such as Markov state models (MSMs) has had many notable algorithmic advances in recent years. The variational principle has opened the door for a nearly fully automated toolkit for selecting models that predict the long-time kinetics from molecular dynamics simulations. However, one yet-unoptimized step of the pipeline involves choosing the features, or collective variables, from which the model should be constructed. In order to build intuitive models, these collective variables are often sought to be interpretable and familiar features, such as torsional angles or contact distances in a protein structure. However, previous approaches for evaluating the chosen features rely on constructing a full MSM, which in turn requires additional hyperparameters to be chosen, and hence leads to a computationally expensive framework. Here, we present a method to optimize the feature choice directly, without requiring the construction of the final kinetic model. We demonstrate our rigorous preprocessing algorithm on a canonical set of twelve fast-folding protein simulations, and show that our procedure leads to more efficient model selection.