  Dimensionality reduction techniques have found great success in a wide range
of fields requiring analysis of high-dimensional datasets. Time-lagged
independent components analysis (TICA), which finds independent components
(TICs) with maximal autocorrelation, is often applied to atomistic biomolecular
simulations, where the full molecular configuration can be projected onto only
a few TICs describing the slowest modes of motion. Recently, Sultan and Pande
have proposed the use of TICs as collective variables for enhanced sampling.
However, it is unclear what the best strategy for estimating the TICs of a
system is a priori. In order to evaluate the utility of TICs calculated on one
system to describe the slow dynamics of similar systems, we develop a
methodology for measuring the transferability of TICs and apply it to a wide
range of systems. We find that transferred TICs can approximate the slowest
dynamics of some systems surprisingly well, while failing to transfer between
other sets of systems, highlighting the inherent difficulties of predicting TIC
transferability. Additionally, we use two dimensional Brownian dynamics
simulations on similar potential surfaces to gain insight into the relationship
between TIC transferability and potential surface changes.
