  We present a maximum-caliber method for inferring transition rates of a
Markov State Model (MSM) with perturbed equilibrium populations, given
estimates of state populations and rates for an unperturbed MSM. It is similar
in spirit to previous approaches but given the inclusion of prior information
it is more robust and simple to implement. We examine its performance in simple
biased diffusion models of kinetics, and then apply the method to predicting
changes in folding rates for several highly non-trivial protein folding systems
for which non-native interactions play a significant role, including (1)
tryptophan variants of GB1 hairpin, (2) salt-bridge mutations of Fs peptide
helix, and (3) MSMs built from ultra-long folding trajectories of FiP35 and GTT
variants of WW domain. In all cases, the method correctly predicts changes in
folding rates, suggesting the wide applicability of maximum-caliber approaches
to efficiently predict how mutations perturb protein conformational dynamics.
