In single-molecule force spectroscopy experiments, a biomolecule is attached to a force probe via polymer linkers and the total extension of the molecule plus apparatus is monitored as a function of time. In a typical unfolding experiment at constant force, the total extension jumps between two values that correspond to the folded and unfolded states of the molecule. For several biomolecular systems, the committor, which is the probability to fold starting from a given extension, has been used to extract the molecular activation barrier (a technique known as “committor inversion”). In this work, we study the influence of the force probe, which is much larger than the molecule being measured, on the activation barrier obtained by committor inversion. We use a two-dimensional framework in which the diffusion coefficient of the molecule and of the pulling device can differ. We systematically study the free energy profile along the total extension obtained from the committor by numerically solving the Onsager equation and using Brownian dynamics simulations. We analyze the dependence of the extracted barrier on the linker stiffness, molecular barrier height, and diffusion anisotropy and, thus, establish the range of validity of committor inversion. Along the way, we showcase the committor of 2-dimensional diffusive models and illustrate how it is affected by barrier asymmetry and diffusion anisotropy.

1.
W. J.
Greenleaf
,
M. T.
Woodside
, and
S. M.
Block
,
Annu. Rev. Biophys. Biomol. Struct.
36
,
171
(
2007
).
2.
K. C.
Neuman
and
A.
Nagy
,
Nat. Methods
5
,
491
(
2008
).
4.
J. D.
Chodera
and
V. S.
Pande
,
Phys. Rev. Lett.
107
,
098102
(
2011
).
5.
A. P.
Manuel
,
J.
Lambert
, and
M. T.
Woodside
,
Proc. Natl. Acad. Sci. U. S. A.
112
,
7183
(
2015
).
6.
M. T.
Woodside
,
P. C.
Anthony
,
W. M.
Behnke-Parks
,
K.
Larizadeh
,
D.
Herschlag
, and
S. M.
Block
,
Science
314
,
1001
(
2006
).
7.
M.
Hinczewski
,
Y.
von Hansen
, and
R. R.
Netz
,
Proc. Natl. Acad. Sci. U. S. A.
107
,
21493
(
2010
).
8.
G.
Hummer
and
A.
Szabo
,
Proc. Natl. Acad. Sci. U. S. A.
107
,
21441
(
2010
).
9.
H.
Yu
,
M. G. W.
Siewny
,
D. T.
Edwards
,
A. W.
Sanders
, and
T. T.
Perkins
,
Science
355
,
945
(
2017
).
10.
D. E.
Makarov
,
J. Chem. Phys.
141
,
241103
(
2014
).
11.
C.
Hyeon
,
G.
Morrison
, and
D.
Thirumalai
,
Proc. Natl. Acad. Sci. U. S. A.
105
,
9604
(
2008
).
12.
P.
Cossio
,
G.
Hummer
, and
A.
Szabo
,
Proc. Natl. Acad. Sci. U. S. A.
112
,
14248
(
2015
).
13.
P.
Cossio
,
G.
Hummer
, and
A.
Szabo
,
J. Chem. Phys.
148
,
123309
(
2018
).
14.
T. E.
Oliphant
,
Guide to NumPy
, 2nd ed. (
Create Space Independent Publishing Platform
,
USA
,
2015
), ISBN: 151730007X; 9781517300074.
15.
E.
Jones
,
T.
Oliphant
,
P.
Peterson
 et al, SciPy: Open source scientific tools for Python, URL: http://www.scipy.org/.
16.
F.
Perez
and
B. E.
Granger
,
Comput. Sci. Eng.
9
,
21
(
2007
).
17.
S. K.
Lam
,
A.
Pitrou
, and
S.
Seibert
, in
Proceedings of Second Workshop on the LLVM Compiler Infrastructure HPC–LLVM’15
(
ACM Press
,
New York, New York, USA
,
2015
), pp.
1
6
, ISBN: 9781450340052.
18.
J. D.
Hunter
,
Comput. Sci. Eng.
9
,
90
(
2007
).
19.
K.
Neupane
and
M. T.
Woodside
,
Biophys. J.
111
,
283
(
2016
).
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