Single-Molecule Test for Markovianity of the Dynamics along a

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Spectroscopy and Photochemistry; General Theory

Single-Molecule Test for Markovianity of the Dynamics Along a Reaction Coordinate Alexander M. Berezhkovskii, and Dmitrii E. Makarov J. Phys. Chem. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.jpclett.8b00956 • Publication Date (Web): 12 Apr 2018 Downloaded from http://pubs.acs.org on April 12, 2018

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The Journal of Physical Chemistry Letters

Single-molecule Test for Markovianity of the Dynamics Along a Reaction Coordinate Alexander M. Berezhkovskii1, and Dmitrii E. Makarov2,3,* 1

Mathematical and Statistical Computing Laboratory, Office of Intramural Research, Center for

Information Technology, National Institutes of Health, Bethesda, Maryland 20892, USA 2

Department of Chemistry, University of Texas at Austin, Austin, Texas 78712

3

Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin,

Texas 78712

AUTHOR INFORMATION Corresponding Author *Email: [email protected]

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ABSTRACT. In an effort to answer the much debated question of whether or not the time evolution of common experimental observables can be described as one-dimensional diffusion in the potential of mean force, we propose a simple criterion that allows one to test whether the Markov assumption is applicable to a single-molecule trajectory x(t). This test does not involve fitting of the data to any presupposed model and can be applied to experimental data with relatively low temporal resolution.

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The Journal of Physical Chemistry Letters

Kinetic measurements of protein folding and other complex biomolecular process are often interpreted using the model that assumes simple diffusion along some reaction coordinate x1-2 with a (generally coordinate-dependent) diffusion coefficient D(x). This is a Markov process that lacks memory. Although this model and its low-dimensional extensions successfully account for numerous experimental3-6 and simulational7-9 observations, it remains a phenomenological description. On theoretical grounds, however, memory effects are expected to be ubiquitous10-11 and to have important consequences on the observed dynamics and rates12-21. Moreover, they are commonly observed in computer simulations, and, less commonly, in experimental studies of biomolecules22-32. Single-molecule measurements that directly probe the time evolution x(t ) of an experimentally measurable quantity (such as a protein’s extension in single-molecule pulling studies) offer an opportunity to estimate the magnitude of memory effects18,

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, but no consensus still exists

regarding their importance. On one hand, several studies report that the dynamics of protein and DNA extension, as measured by single-molecule force spectroscopy, is consistent with the onedimensional (1D) diffusion model with no memory5, 33. On the other hand, this model appears inadequate as a global fit of both rates and transition path times, while describing each of these data sets separately34 – this finding was interpreted as evidence that a one-dimensional model without memory is insufficient24, 35. A key difficulty in estimating memory effects from single-molecule trajectories is their relatively low time resolution. For this reason, previous comparisons of the 1D Markov diffusion model with experimental data have been indirect and employed fitting certain experimental observables (e.g. transition rates, distributions of transition path times etc.). This is in contrast to trajectories obtained from molecular simulations, for which, for example, the memory kernel in the

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generalized Langevin equation can be directly computed36-38, or the assumption of diffusive motion can be tested by examining short-time evolution of mean-square displacements23. In what follows we propose a simple and direct test of the Markov assumption that can be applied to any sufficiently long single-molecule trajectory measured experimentally or simulated on a computer. The test does not make any assumptions about the specific equations of motion governing the system and – importantly – does not require high temporal resolution of the observed trajectory x(t ) . We expect that this test would be straightforward to apply to existing single-molecule data and, in anticipation of such applications, illustrate how it works by analyzing (i) a trajectory obtained by numerically integrating a generalized Langevin equation with memory and (ii) a trajectory of a protein obtained from molecular simulations39.

y

a

x

b

Figure 1. Examples of trajectories crossing some intermediate point x that are transition paths from a to b (the combined trajectory composed of the green and red lines) and loops (green and red lines). A transition path from a to b can be thought of as a combination of a forward

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The Journal of Physical Chemistry Letters

trajectory originating from some intermediate point x and proceeding to b and a time-reversed backward trajectory originating in x and going to a.

We consider a long trajectory x(t ) that is bounded in space, as is usually the case for common reaction coordinates considered in protein folding and other intramolecular processes. We would like to establish whether x(t ) is a Markov process. To this end we pick an interval (a,b) (where a