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Path Collective Variables (PCVs) are a set of path-like variables that have been successfully used to investigate complex chemical and biological processes and compute their associated free energy surfaces and kinetics. Their current implementation relies on general, but at times inefficient, metrics (such as RMSD or DRMSD) to evaluate the distance between the instantaneous conformational state during the simulation and the reference coordinates defining the path. In this work, we present a new algorithm to construct optimal PCVs metrics as linear combinations of different CVs weighted through a spectral gap optimization procedure. The method was tested first on a simple model, trialanine peptide, in vacuo and then on a more complex path of an anticancer inhibitor binding to its pharmacological target. We also compared the results to those obtained with other path-based algorithms. We find that not only our proposed approach is able to automatically select relevant CVs for the PCVs metric but also that the resulting PCVs allow for reconstructing the associated free energy very efficiently. What is more, at difference with other path-based methods, our algorithm is able to explore nonlocally the reaction path space.

Defining an Optimal Metric for the Path Collective Variables