@article {48828,
	title = {Non-Markov models of single-molecule dynamics from information-theoretical analysis of trajectories},
	journal = {Journal of Chemical Physics},
	volume = {159},
	year = {2023},
	month = {AUG },
	pages = {064104},
	type = {Article},
	abstract = {<p>
	Whether single-molecule trajectories, observed experimentally or in molecular simulations, can be described using simple models such as biased diffusion is a subject of considerable debate. Memory effects and anomalous diffusion have been reported in a number of studies, but directly inferring such effects from trajectories, especially given limited temporal and/or spatial resolution, has been a challenge. Recently, we proposed that this can be achieved with information-theoretical analysis of trajectories, which is based on the general observation that non-Markov effects make trajectories more predictable and, thus, more {\textquoteleft}{\textquoteleft}compressible{\textquoteright}{\textquoteright} by lossless compression algorithms. Toy models where discrete molecular states evolve in time were shown to be amenable to such analysis, but its application to continuous trajectories presents a challenge: the trajectories need to be digitized first, and digitization itself introduces non-Markov effects that depend on the specifics of how trajectories are sampled. Here we develop a milestoning-based method for information-theoretical analysis of continuous trajectories and show its utility in application to Markov and non-Markov models and to trajectories obtained from molecular simulations.</p>
},
	issn = {0021-9606},
	doi = {10.1063/5.0158930},
	author = {Song, Kevin and Park, Raymond and Das, Atanu and Makarov, Dmitrii E. and Vouga, Etienne}
}
