(PDF) Computer simulations study of biomolecules in non-aqueous solutions.

Computer Simulations Study of Biomolecules

in Non-Natural Medium

Danilo Roccatano1

Jacobs University Bremen

Campus Ring 1, 28759 Bremen (Germany)

Abstract

Pure organic solvents or their mixtures with water are commonly used as artificial media for

biotechnological application and for fundamental research on protein stability and folding mechanism.

The molecular interactions between these environments and the biomolecules are complex and

variegated, and only recently we started to shade a light on their nature. A powerful tool to address the

atomistic detail of these processes is the use of molecular modeling methods. In particular, Molecular

Dynamics simulation is one of the most powerful and versatile tools to investigate the solvation of

complex molecules. In the last few years, the number of publications on peptide and protein simulations

in non-natural environments has rapidly increased. These studies are providing important information to

shed light on the nature of non-aqueous solvent effects. In this chapter, the achievements and the

future prospects in this field of computational biochemistry are reviewed by summarizing the most

important theoretical results published in the last 15 years.

Keywords: organic solvents and cosolvents, preferential solvation, protein stability, peptide

folding, peptide aggregation, enzymatic catalysis.

1 Address correspondence to this author at School of Engineering and Science, Jacobs University Bremen,

Campus Ring 1, 28759 Bremen, Germany; Tel: +49-421-200-3144; Fax: +49-421-200-3249; E-mail:

d.roccatano@jacobs-university.de.

INTRODUCTION

Computer simulations provide detailed atomic information of processes that would be difficult

(or even impossible) to study experimentally. One of those processes is the solvation

mechanism of biomolecules in aqueous and non-aqueous environments. Among the different

computational methods, Molecular Dynamics simulation has been used to study the effect of

solvent ever since its early infancy [1]. In contrast to the slow conformational dynamics of the

protein, solvent molecules can equilibrate faster around the protein surface and, in the time

scale of the simulation, relevant properties such as preferential solvation, diffusion constants

or reorientation dynamics, can be quantitatively estimated [2-4].

Among the common solvents, water was, for obvious reasons, the first solvent

molecule to be studied using computer simulation methods. Since then, many different models

have been proposed and used in simulations of biomolecular systems [5-8]. The systematic

study of the effect of non-aqueous solvents on protein thermodynamics properties begin in the

middle 1950 [2]. These studies thrived the interest to study proteins and nucleic acids in these

milieus with the aim to prove the effect of non-natural solvent conditions on the stability and

folding, and hence to use these knowledge for biotechnological applications [10-12]. For the

numerous solvents used in these experimental studies, although not in copious abundance as

for water, different computational models are available in the literature. Nevertheless, the use

of these solvents for simulation studies of proteins still remains very limited. Two main reasons

can be adducted to justify this lack of interest. The larger size and complexity of some of these

solvents, compared to water, can challenge the modelling of the interaction with biological

molecules and the required computational resources to conduct simulations in the presence of

macromolecules. The other reason is the lack of experimental data at atomic level on the

interaction of the solvent with the protein that can be used to validate simulation results. In the

past few years, availability of low-cost high performing computer clusters and advancement of

numerous experimental techniques boasted the number of theoretical studies of biomolecules

in pure organic solvent and their mixtures with water. This growing interest is also sustained

by the perspective of unrevealing the Holy Grail [3] of the protein folding mechanism with the

obviously tremendous implication in biomedical and biotechnological applications.

Actually, non-aqueous environment can stabilize/destabilize thermodynamically or

kinetically the folding of both secondary structure forming peptides and proteins. However, it is

not yet clear how biomolecules change their structural, dynamical and functional properties in

the presence of stabilizing or destabilizing solvents molecules and theoretical models could

offer important clues to understand the nature of the phenomenon. Furthermore, the

increasing use of enzymes in organic synthesis, as green and efficient replacement of

traditional synthetic approaches, has urged the improvement of catalytic activity of enzymes in

non-aqueous conditions. In this way, the reaction yield can be increased as effect of the

excess of substrate (when the solvent is also the substrate of the enzyme) and/or of increased

solubility of both reagents and products. The studies of Klibanov and co-workers have

recognized steadily that many enzymes can still be active in anhydrous or semi-anhydrous

conditions [4, 5] as well as in the presence of hydrophobic solvents. In addition,

crystallographic and spectroscopic studies of enzymes in non-aqueous solvents have

reinforced the hypothesis on the conservation of the aqueous structure of the enzyme in these

conditions [6, 7]. Interestingly, although it is still matter of debate, the conservation of the

solution three-dimensional structure has been also suggested for the extreme case of protein

in vacuum by the combination mass-spectrometry experiments and molecular dynamics

simulations [18-20].

In the case of mixture of organic solvents with or without water, the effect of the

cosolvents on the stability and activity of the protein become even more unpredictable and

dependent by the nature and concentration of the cosolvents.

The complexity of the matter is evident by the fact that despite the large amount of

experimental data accumulated over the years [14, 15, 21], a general unifying theory on

solvent effects on biomolecules still does not exist. Recently, we begin to shed light on the

microscopic nature of this complex phenomenon thanks to the synergy of novel experimental

methods and extensive and refined theoretical models based on MD simulations.

This chapter is an update and extension of a previous review on the topics published

by the same author in 2008 [8]. It contains a review of the progress made in the last 15 years

on protein and peptide simulations in organic solvent and their mixture with water by

addressing the following questions:

1) What is the state of art of MD simulations of protein and peptide in non-natural solvent

conditions?

2) How results from the theoretical studies can give support to experimental data of

protein solvation?

3) What kind of solvation mechanism has been proposed on the bases of these

theoretical simulations for the most common solvents?

4) In retrospect, what can we expect from this growing sector of computational chemistry

and biochemistry?

The chapter is divided into two parts. The first part will provide a general introduction of

the MD techniques and of the current theories of cosolvent effects on biomolecular systems. It

is followed by a summary of current knowledge on structure, thermodynamic, dynamic and

functional properties of peptides and proteins in non-aqueous solvents. The second part of the

chapter will provide a summary of simulations studies of biomolecules in the most common

organic solvents (see Table I for a list). Finally, in the perspective section, a short outline of

the future development of MD simulations in this field will be given.

I. BASIC PRINCIPLES OF MOLECULAR DYNAMICS SIMULATIONS

Comprehensive introductions on the MD techniques can be found in several reviews

[23-26] and textbooks [27-31]. In this section, the basic principles and the most common

methods to analyse the solvation properties of macromolecules from MD trajectories are

shortly introduced.

Molecular dynamics simulation is a useful tool for exploring the atomic properties of

large biomolecular systems. It is based on classical mechanics description of atomic motion.

The trajectory, e.g. the position in space and time, for a given atom i can be derived from the

integration of the Newton’s equation

(1)

where t is the time, ri, the position vector and mi the mass of the ith atom, respectively. The

force (Fi) acting on the ith atom, and generated by the interaction with the N-1 atoms of the

system, is obtained by the potential energy, V(r1,r2,r3, … rN) that is a function of the

coordinates of the interacting atoms. The latter is a mathematical expression modeling, in

approximate manner, interactions among atoms in the system. In this approximation, atoms

are normally considered spherical particles without an internal structure. The missing

electronic degrees of freedom are implicitly included in the potential energy V(r1,r2,r3, … rN)

that approximate, using analytical functions, the quantum nature of atomic interactions. The

use of analytical functions determines an enormous speeding up of the intermolecular

interactions calculation allowing the simulations of large molecular systems. In fact, the

absence of the electronic degree of freedom makes the required calculation proportional only

to the square of number of atoms but not to the 3rd or 5th power of number of electrons in the

system as for quantum mechanics methods [9, 10]. In this way, MD simulations can be used

to study systems with thousand to million atoms for time scale of nanoseconds to

microseconds [32-34]. The payback of this simplification is the restriction encountered when

studying electron-mediated process (like chemical reactions). Nevertheless, for the

investigation of solvation dynamics, this approximation does not reduce the precious value of

the possible outcomes.

The potential energy function is usually called empirical force field since it contains

several parameters (e.g. force constants, reference distances and angles, partial charges, van

der Waals parameters) that are normally derived from experimental data. In Fig. (1), a

€

mi

∂2ri

∂t2=Fi

Fi=−∂V(r

1,r2,r3…rN)

∂ri

; i=1…N

schematic representation of the most common energetic terms contributing to the force field is

reported. The different force fields currently available for MD simulations share similar

functional forms. The energetic terms can be grouped as bonded and non-bonded one. The

former is usually represented by harmonic function describing bond and bond angles

vibrations, and by cosine functions for torsional interaction terms. For non-bonded

interactions, the van der Waals energetic contributions are normally modelled using the

Lennard-Jones function; while for the electrostatic ones, a Columbic potential with fixed partial

atomic charges is generally used.

As mentioned before, force field parameters are evaluated from experimental data or

from quantum mechanical calculations on small model molecules. Force constants for the

harmonic potential of bond and bond angle interactions are usually obtained from

Infrared/Raman spectroscopy and/or from QM calculations [11]. Partial charges are derived

from molecular electron densities obtained from ab-initio calculations. Finally, the σ and ε

parameters for the Lennard-Jones potential (see Fig. (1)) are derived from experimental data

or by fitting energy potential derived from QM calculations [11]. For solvent models, the initial

set of non-bonded parameters is usually adjusted to reproduce the physico-chemical

properties of the pure liquid. Density, enthalpy of vaporization, heat capacities and free energy

of solvation are the properties typically targeted in the optimization process. This optimization

can be extended to mixtures with a water model and their properties are normally investigated

over a complete range of concentrations (see, for example, Refs. [36-38]). For amino acids or

nucleic acids, the force field optimization relies on both thermodynamics and structural data

from crystallography, NMR and other spectroscopic techniques. An example of force field

parameterization based on thermodynamics data is the recent improvement of the GROMOS

force field (GROMOS 53A6 and 54A7 versions) [39, 40]. The new force field parameters were

first optimized to reproduce the thermodynamic properties of pure liquids of a range of small

polar molecules and solvation free enthalpies of amino acid analogous in cyclohexane. The

partial charges were then further modified to reproduce also the hydration free energies of the

same analogous [39, 40]. The new force field resulted in an improved accuracy with respect

the older one (G43A1) in simulations of proteins in solution [12].

A very important approximation underlying force field optimization procedure is the

transferability. For biomolecules, this assumption states that the parameters of a force field,

obtained by optimization of each isolated amino acids, are suitable also for the simulation of

the same amino acids in proteins. Fortunately, this approximation works in most of the case

very well. Indeed, this is quite surprising if one considers that the same amino acid into

proteins can be surrounded by completely diverse and not homogeneous environment

conditions than in the bulk solvent. The fortunate occurrence of this robustness in the force

field model makes MD simulations a wonderful tool for theoretical study of complex molecular

systems. Different types of force fields have been developed. These force fields consist of

libraries of parameters optimized for different classes of macromolecules (usually proteins or

nucleic acids) and for different organic solvent molecules. Commonly used force fields are the

following: GROMOS [13, 14] , OPLS [43, 44], CHARMM [45, 46] and AMBER [47-49].

The success of MD simulations is also strongly connected to the exponential increase

of computer performance. In this way, the upper limit of simulation scale is continuously

pushed towards larger systems [15] and longer simulation time [16]. Unfortunately, not all the

processes involving solvent diffusions have time scale accessible to the current performance

of computer simulations study [17]. For example, diffusion of small molecules into active sites

[53-55] (or in other protein cavities and tunnels) or across membrane [18] can be rare events

even for nanoseconds time scale. For these slow diffusive processes, different MD techniques

can be applied to speed up them in the time scale of the simulation. Three examples are the

replica exchange molecular dynamics [19], the steered molecular dynamics simulations [20]

and umbrella sampling methods [59, 60] (see also Ref. [21] for a general review on these

methods). The first method is based on multiple simulations of the same system but at

different temperatures with the possibility of switching the generated conformations between

the different runs according to an assigned Boltzmann criteria [19]. In this way, the

biomolecule is able to overcome higher conformational barriers and populate larger regions of

its conformational space. The original method and the variants have gained popularity

especially for peptide simulations [61-64]. In fact, for these small molecules, an extended

analysis of conformational space allows to study solvent effects from a thermodynamic

perspective [22].

Steered molecular dynamics is used to apply an external force on the system to move

a subset of atoms in a given direction. In this sense, it can be used to emulate atomic force

microscopy experiments and to perform in-silico nano-manipulation experiments. The method

is normally used by applying and a pulling velocity in a given direction by connecting single

atoms or molecules to a harmonic spring the elongation of which return the value of the mean

force exercised by the overall system on the moving atoms along the pulling direction [20].

The mean force acting on the pulled atoms is measured by the extension of a spring and it is

used to qualitatively and quantitatively calculate the potential mean force (PMF) of the process

[31, 59]. The method can be used to speed up the diffusion of ligands or solvent molecules

into buried active site [53, 65], along channel [23], membrane [67, 68] and estimate free

energy barrier of the processes [24].

Accurate determination of the PMF can be calculated using the umbrella sampling method

[25, 26]. In this method, a set of N separate MD simulations, in which a harmonic potential

(umbrella potential),

€

V(

ξ

)=1

2

Ki

ξ

−

ξ

i

( )

2

(2)

is applied between the centre of mass of the target molecule and a reference group of atoms

are performed. This restrains the molecule at a distance ξi with a force constant of Ki. In each

simulation, the value of the distance ξi is changed from a maximum value corresponding to the

molecule in the bulk solvent phase to a minimum one at which the molecule is in the solvation

shell of the solute. From each simulation a histogram is calculated, representing the

probability distribution (Pi) along the reaction coordinate (ξ) biased by the umbrella potential.

The PMF from umbrella sampling histograms is then calculated using the weighted histogram

analysis method (WHAM) [27]. This method computes PMF based on all the MD trajectories

obtained along the reaction coordinate (ξ) sampling points. PMF are usually calculated along

distances but also angular coordinates can be used. Each trajectory provides, at sampled

position i along this coordinate, a distribution histogram (Pi) as result of the large set of

observations from the simulation. The PMF is calculated by weighting the contribution of all

these distributions at each point

PMF(ξi)= –kBT log(Pi), (3)

where kB is the Boltzmann constant and T is the temperature. The integral of the PMF curves

provides the free energies of the process under consideration. Depending by the length of the

sampling, the method can provide free energy estimations in quantitative agreement with

experimental data [26].

Another way to speed up the simulation of large biomolecules is the use of coarse-

grained MD methods. These methods consist in removing computational intensive atomic

degrees of freedom of the system by either clumps together group of atoms or using special

potential that provide a mean field effect of the removed atoms or molecules [10]. The mean

field is normally used to obtain implicit solvent models [72, 73]. However, in this review, only

MD simulations in explicit solvent will be considered.

Analysis of MD trajectories

After a MD simulation, trajectories of the atoms (coordinates and velocities over the

simulation time) are analysed to extract relevant structural and dynamical properties of the

simulated system. Thereafter, these results are directly or indirectly compared with

experimental data from different sources (see Fig. (2)). For structural properties, the root

mean square deviation about a reference structure, the radius of gyration, the secondary

structure and the clustering analysis of the generated conformations are some of the most

useful indicators for characterizing the folding state of peptides and proteins. The visualization

of single conformations or the entire MD trajectory is also a fast and effective way to analyze

the overall structural behaviour of the system. For this purpose, many programs for molecular

visualization (for an updated list of them see at http://molvis.sdsc.edu/visres/) are available. All

molecular structures showed in the figures of this review are generated using the public

domain programs VMD (http://www.ks.uiuc.edu/Research/vmd) and ChemSketch

(http://www.acdlabs.com).

Dynamical properties are normally analysed using root mean squared fluctuations, and

principal component analysis [28]. The latter technique is used to separate the different

independent modes of the protein motion and evidences large scale slow motions from fast

local fluctuations of the molecule [28]. Readers can find more detailed information on the

different type of analyses of MD trajectories in other reviews [23, 74, 75]. Here, some of the

most common methods used to analyse solute/solvent interactions are shortly described.

Solvent Structure

At the atomic level, the dynamics of the liquid state is characterized by a continuous

diffusive tumbling of molecules. Despite the apparent chaotic motion of system, the presence

of atomic centred interaction potentials generates, on average, well-defined distance (and

angular) based structural correlations among the molecules. These correlations are measured

using different experimental techniques (e.g. X-ray diffraction, neutron scattering [29]). Using

the statistical mechanics theory of liquid, the experimental information can be mathematically

related to the solvent organization around a given atomic centre in term of pair correlation

functions. These functions describe the positional correlation of the centre a about the centre

b where the two centres are atoms as well as molecular centre of masses, and they are

related to intermolecular interaction potentials by the following expression [30]:

€

gab (r

1,r2)=Nb(Nb−1) dr3…rNa e

−U(r,,…rNa )

kBT

∫

ρ

2dr3e

−U(r,,…rNa )

kBT

∫

(4)

where the integral at the denominator is the partition function of the system and the one at the

nominator excludes coordinates r1 and r2 of the two centres (a, b) from the integration. For

homogenous and isotropic system as liquid solvents, only relative distances between two

molecules are meaningful. In this way, the Eqn. 4 is reduced to just a function of the distance

r between the two centres:

€

gab (r)=

ρ

b(r+dr)

ρ

(5)

where dr is the thickness of a spherical sector centred on the reference centre a,

ρ(ρ+δρ)

the

local density of b in the spherical sector of thickness dr and radius r, and

ρ

the total density of

the liquid. The resulting distribution function gab(r) describes the local organization of the

molecules b around the center a in the system (see Fig. (3) for a schematic description).

Since, the definition of g(r) implies that 2

π

g(r)dr is proportional to the probability of

finding an atom in the volume element dr at the distance r from a given atom, for a

homogenous and isotropic fluid, n(dr) = 4

π

g(r)r2dr represents the average number of atoms (or

molecules) within a shell of radius r and thickness dr that surrounds the atom (see Fig. (3)).

The centres a and b can be single atoms of the centre of mass of a molecule.

However, for large solutes, as proteins, the organization of the solvents with respect the

centre of mass of the molecules provides little clues to the local solute solvation. Therefore, it

is more useful and informative to analyse the organization of solvent molecules around group

of atoms as each amino acids. In this case, the g(r) functions of the solvent are calculated with

respect representative atoms (usually the Cα atom) or about the centre of mass of the

residues.

Density distribution of solvent structure around proteins

A more convenient description of the solvent distribution of around large and complex

solute is the use of the spatial solvent density distribution. This function is obtained by

calculating the average number (number density) of water molecules present in each cell of a

three-dimensional grid fixed to the reference frame of the protein. By cumulating the number

of solvent molecules present in each grid point along the trajectory and, then, by averaging for

the values for the number of frames, volumetric density data of the space surrounding the

protein are obtained [31]. In Fig. (4), an example of spatial water density distribution

surrounding a peptide is shown. On each slice of the volumetric data, the local density in the

form of three-dimensional elevation is reported [32]. From these data, it is possible to localize

preferential hydration sites and correlate them with, for example, position of crystallographic

waters [77, 79, 80]. In this way, MD simulations data can complement information from

crystallographic [33] and NMR data [34] to identify ligand binding site on protein surfaces [35].

Residence time and diffusion coefficients

Dynamical properties of solvent molecules close to protein surfaces can be measured

using a wide range of techniques. Multidimensional NMR techniques are the most common

methods to measure the solvent relaxation times and diffusion coefficients [17, 36]. In 2-D

NMR experiments, a pair of spins (e.g., a protein hydrogen and a selected paramagnetic

nucleus of the solvent molecules) can interact by dipolar coupling if the distance between

them is less than 0.5 nm. The decay of the excitation will depend, among other things, on the

exchange of the bound solvent molecule with the bulk phase. This relaxation process can be

studied only if it is slower than the characteristic rotational correlation time (molecular

tumbling) of the protein in solution. For a protein of small to medium size, this time is about

few nanosecond [37]. This restriction limits the investigation to only closely bound (internal)

solvent molecules excluding more mobile ones located on the protein surface [17]. However,

surface solvation, occurring at shorter time scale (less than tens of picoseconds), can still be

addressed by magnetic relaxation dispersion [4, 52] and by time-resolved spectroscopic

techniques [17] both providing relaxation dynamics with femtoseconds to picoseconds

resolution [4, 86].

Both short and long time scale relaxation processes can be directly calculated from

MD trajectories. The residence time of solvent molecules in protein solvation shells provides

precious information about the solvation mechanisms and dynamics. These data can be

obtained from MD trajectories in different ways [38]. A common approach is to use the survival

time correlation function [87, 88]:

€

Pa(t)=pa,j(t,t+t')

t'

∑

j=1

N

∑

(6)

where the probability function, p

α

,j (t, t + t') is a binary function that adopts a value of one if the

solvent molecule labelled j has been in the referred solvation shell around site a from time t' to

time t + t', without escaping between this time interval (or leaving during this interval the shell

for a time not longer than a t*), and zero otherwise. The value of p

α

,j(t, t + t') is averaged over

time and over all solvents molecules. Since this function is a time correlation function, P

α

,j(t =0)

gives the average number of solvent molecules belonging to the solvation shell of site (i.e. the

coordination number), and P

α

,j(t) gives the average number of solvent molecules that still

remain in the solvation shell after a time t from the time they first entered the shell. The

relaxation trend of P

α

,j(t) provides information about the local dynamics of the solvation

molecules. The value of P

α

,j(t) is usually approximated to single or double exponential function

[38]:

€

C(t)=Ae−t/

τ

or

C(t)=Ae−t/

τ

s+Be−t/

τ

l

(7)

where

τα

is the relaxation time constant and the subscript symbols s and l referring to a short

and long relaxation time, respectively. The relaxation time for the water shows a non-

exponential behaviour that can be fitted using the Kohlraush-Williams-Watt stretched

exponential function [39] :

€

C(t)=Ae

−t

τ

⎛

⎝

⎜ ⎞

⎠

⎟

β

(8)

This function is commonly used to describe relaxation process involving diffusion in

corrugated self-similar energy landscapes. The self-similarity of the protein surface provides

an interpretative key to this anomalous diffusion process [39]. An example of residence time

study for non-aqueous solvents (methanol, DMSO and chloroform) on protein surface is given

by Kovacs et al. [40].

Hydrogen bond dynamics between solvent molecules and proteins is also used to

estimate the residence time. In this case, the function h(t), having value 1 or 0 according to the

presence or absence of a hydrogen bond between the solvent molecule and the protein

surface, is used. The correlation function of h(t) is then defined as :

€

CH(t)=h(t)h(0)

h

(9)

where the brackets indicate the average along the trajectory. The fitting of CH(t) by an

exponential function will provide the relaxation time for the hydrogen bond network [41]. This

approach is used only for molecules having donor or acceptor atoms (for example alcohols or

urea) forming hydrogen bonds.

Another description of the solvent mobility on the protein surface is the calculation of

its self-diffusion coefficient. This dynamical property is directly correlated to diffusivity

measurements from NMR and other spectroscopic techniques [17]. The diffusion coefficient

on the protein surface from MD simulation trajectories is calculated using the Einstein relation

[30]:

€

6Dt =1

N

r

i(t)−r

i(0)

[ ]

2

i=1

N

∑

(10)

where ri(t) is the coordinate vector of the particle ith at time t, ri(0) the coordinate vector of the

particle ith at time t=0 and N the total number of particles [30]. The effect of the different

regions of the protein surface on solvent diffusion coefficient can be also calculated with

respect the nodes of a grid in a reference frame moving with the protein. In each grid node

(with indices u, v, w), the diffusion coefficient is then given by

€

6Duvw =1

t2−t1

!

r (t2)−!

r (0) 2−!

r (t1)−!

r (0) 2

( )

(10b)

where the values of t1 and t2 are fixed (for water molecules) to 1 ps and 2 ps, respectively, on

the assumption that a normal diffusion regime is reached by the solvent molecules after 1 ps

of simulation [31, 42]. The average contact time of solvent with proteins is of the order of few

tens of picoseconds. Therefore, several nanosecond long MD trajectories are required for a

good estimation of the diffusion from simulations [43].

Besides translational diffusion, rotational diffusion provides important information on

the orientation of the molecule with respect to the protein surface. For polar molecules, this

property is calculated by the autocorrelation function of the dipole moment of the solvent

molecules:

€

Γ

l(t)=P

l(ˆ

µ

(0)⋅ˆ

µ

(t)

(11)

where Pl is the Legendre polynomial of the order l, µ is the unit dipolar vector and the brackets

indicate the average along the trajectory [38]. The studies of diffusion properties of water on

protein surfaces have shown anomalous behaviour due to the corrugated nature of this protein

region (for a detailed discussion see [38, 39]).

Several MD studies on water diffusion around proteins and nucleic acids have been

reported in literature [79, 80, 87, 92, 94]. Contrary, the dynamics properties of non-aqueous

solvents around biomolecules have been less extensively analysed (see examples [4, 90, 91,

95]).

Preferential solvation

The variegated molecular surfaces of peptides and proteins create complex selective

interactions with the solvent and the different cosolutes present in the same solution. This

different interaction propensity is referred to as “preferential binding” or “preferential solvation”

[44]. This is a thermodynamics property and it is related to the activity coefficient of the

cosolvent molecules in the proximity of the solute surface. At equilibrium, it is related to the

variation of the chemical potential of the protein when transferred from the water to the

mixture:

(12)

where

tr

P

µ

Δ

is the transfer free energy of the protein from pure water into the mixed solvent

system, m is the molarity, and subscripts X and P identify the cosolvent and the protein,

respectively. The first term in the relation can be determined experimentally for mixture of

water and cosolvent. It measures the variation of the chemical potential at mp→0, thus it

describes the perturbative effect of the solvent on the water structure. The second term is the

“preferential binding” coefficient:

(13)

This coefficient measures the excess binding of the cosolvent on the protein surface. Using

the molecular statistical mechanics theory of liquids, it is possible to connect this coefficient

with the effective number of cosolvent molecules present on the protein surface:

€

Γ

XP =nx

S−nw

Snx

B

nw

B

⎛

⎝

⎜

⎞

⎠

⎟

(14)

In this expression, n indicates the number of specific molecules named in the subscripts, and

the superscripts S and B indicate the regions (S = surface, B= bulk) where the molecules are

located. Baynes and Trout have proposed a practical approach to calculate the values of ΓXP

from MD simulations [43] based on two approximations introduced by Smith {Pierce, 2008

#942;Smith, 2004 #941}. They defined the instantaneous preferential binding coefficient as:

€

Γ

XP =∂mX

∂mP

⎛

⎝

⎜

⎞

⎠

⎟

µ

X

(15)

where ΓXP(t’) is the instantaneous preferential binding coefficient at the simulation time t’ as:

€

Γ

XP =nx

S−nw

Snx

B

nw

B

⎛

⎝

⎜

⎞

⎠

⎟

(16)

The number of molecules present in the two regions are evaluated from the simulation by

using radial distribution functions of the water gw(r) and cosolvent molecules gX(r) with respect

to protein centres (e.g. side chains, backbone atoms). In this way, the relation (14) can be

transformed as:

€

Γ

XP =∂mX

∂mP

⎛

⎝

⎜

⎞

⎠

⎟

µ

X

€

Γ

XP =nx

S−nw

Snx

B

nW

B

⎛

⎝

⎜

⎞

⎠

⎟

=

ρ

x(∞)gXdV

∫−

ρ

x(∞)

ρ

W(∞)

⎛

⎝

⎜

⎞

⎠

⎟

ρ

W(∞)gWdV

∫

=

ρ

X(∞)gX−gW

( )

dV

∫

(17)

where the last integral is calculated relative to a surface patch region of the protein and until a

distance r* from the surface (in the bulk region) at which gx(r*)=gW(r*)=1 and the contribution to

ΓXP become null. Baynes and Trout have applied their method to the RNAse T1 and RNAse A

with glycerol and urea as cosolvents [43]. The results of their studies showed that the effect of

cosolvents is mainly given by the first solvation shell but also the second shell can give a

significant contribution to the preferential binding coefficients [43]. Furthermore, for the two

cosolvents preferential binding is larger on more hydrophobic regions of the protein.

The preferential solvation can be also evaluated as local concentration of cosolvent.

The concentration (% v/v) of the cosolvent molecules around the peptide residues, named

local cosolvent concentration (LCC), is evaluated from the cumulative number of water nw(r)

and cosolvent nc(r) molecules within a distance r from the Cα's of each residue using the

following relation:

€

LCC(r)=Vm

cnH(r)

Vm

cnH(r)+Vm

wnw(r)

[ ]

100

(18)

where Vmc and Vmw are the average excluded volumes for cosolvent and water molecules,

respectively. In Fig. (5), an example of local density distribution of HFIP around the peptide

melittin is shown [45].

Thermodynamics properties

Computer simulations are also used to calculate thermodynamics properties of protein

and peptides in solution [98-101]. However, accurate determinations of thermodynamic data

rely on multiple and extended simulations and, for protein, even small one, this task can

require expensive calculations [46]. For peptides, the situation is considerably better because

their size is smaller compared to proteins and, therefore, the required computation effort is

more affordable. Nevertheless, exhaustive sampling of the conformation space of small

peptides can still be a challenging issue [75, 102].

An alternative approach is the use of the extended Kirkwood-Buff theory of mixture

solutions by combining experimental and theoretical data {Shimizu, 2006 #323;Smith, 2004

#49;Pierce, 2008 #942}. This theory, based on the statistical mechanics, connects

thermodynamics properties to atomic structural correlation functions by the Kirkwood-Buff

integrals [47]:

€

Gij =4

π

r2gij (r)−1

[ ]

dr

0

x

∫

(19).

These integrals contain pair distribution functions gij(r) of each atomic species (i) with respect

to other ones (j) present in solution. The gij(r) functions are estimated using Monte Carlo or

MD simulations. Once the value of Gij is known then it is possible to derive thermodynamics

properties as, for example, partial molar volume, activity coefficients and free energy of

transfer [47]. This approach has been used to garner insights into the nature of

cosolvent/water mixtures of different osmolytes. The method has been used to relate the

different terms of the free energy to structural properties and to study the enthalpy-entropy

compensation in the solvation of small solutes by solvent mixtures [48, 49]. Another example

of application is the study of the solvation free energy of the Leu-enkephalin, a opioid

pentapeptide of sequence YGGFL [50]. In this study, the salting-out effect has been

reproduced and quantified by calculating the population shift to the unfolded state of the

peptide conformation in the presence of NaCl 2M [50].

The three-dimensional reference interaction site model (3D-RISM) is another recently

developed statistical mechanics approach based on the integral equation theory of molecular

liquids [51]. It also combines MD simulations and the integral equation theory to predict

thermodynamics properties of solutes in solution. The method successfully predicted the

hydration structure and thermodynamic quantities of different peptides and proteins [51].

Recently, it has also been applied to study cosolvent (urea and glycerol) effects on the partial

molar volume change of Staphylococcal nuclease associated with pressure denaturation [52].

II. EFFECT OF SOLVENTS ON BIOLOGICAL MOLECULES

Non-aqueous solvents can be classified according to their effects on the protein and,

for mixtures, on the water structure (see Fig. (6)). In the first case, we have two general

classes:

• Compatible solvents. This class represent all solvents and/or cosolutes that can

stabilize folded protein structure. These types of molecules are also called compatible

osmolytes because they have an important role in the regulation of osmotic balance in

the cell. Actually, they are present at high concentrations in living cells and the function

is to protect them by external protein denaturating agents (pressure, temperature,

salts). Typical examples of this class of solvents or cosolvents are polyols (e.g.,

glycerol), sugars (e.g., trehalose), polymers (e.g., polyethylene glycol), amino acids

(e.g. arginine or glycine betaine) .

• Incompatible solvents. This class of molecules comprises all protein denaturants as,

for example, urea and guanidinium cation.

The second classification is based on the effect of solvents on the water structure:

1) Chaotropic solvents. Water structure perturbing solvents are called chaotropes

(from Greek: cháos (χάος), confusion and tropos (τρόπος), to turn) that means

order-breakers. Those solvents interfere with the formation of the hydrogen bond

network among water molecules.

2) Kosmotropic solvents. Water structure promoting solvents are called

kosmotropes (from Greek kósmos (κόσµος), order and tropos (τρόπος), to turn) that

means order-makers. Those solvents are able to promote ordering in the hydrogen

bond networks of water.

The protein stabilization/destabilization mechanism(s) of both solvent types is not yet

completely clarified. Experimental techniques usually provide indirect clue on the molecular

process. For this reason, molecular simulations are used to complement the experimental

data and provide realistic models of the molecular mechanisms.

Pure solvents

The proposed solvation mechanism of proteins in pure organic solvents requires the

(partial) dehydration of the molecule and the substitution of water molecules with the non-

aqueous solvent [4]. However, in these conditions, the protein can fully retain its structural and

functional properties only if lubricant water molecules (mainly structural water molecules) are

conserved in the protein structure. The importance of structural water molecules was recently

evidenced by the recent electrospray ionization mass spectrometry experiments [53]. The

results of these experiments and of MD simulations studies [18] have shown that proteins,

evaporated in vacuum by matrix-assisted laser desorption/ionization (MALDI) techniques,

retain their solution/crystal-like three-dimensional structure if they conserve part of the

hydration water molecules [110, 111].

It was also proposed that pure organic solvents could influence the protein dynamics by

constraining the conformational space accessible to the molecule [4]. This hypothesis is based

on the so-called ligand memory effect that consists in an enhanced catalytic activity of the

lyophilized protein in the presence of substrate upon solubilisation in organic solvent [4]. The

proposed mechanism for this effect supposes that the substrate binds the protein in the

lyophilized state forming an activated conformation state. Upon the solubilisation, the reduced

conformational flexibility in the organic solvent constraints the protein in this reactive state

enhancing the substrate binding affinity and activity [4]. A recent MD simulation study [54]

have partially evidenced the role of the water as lubricant for catalytic part of the proteins

although the results are still far being conclusive.

In this review, I will limit to report the most relevant results of protein in pure solvents from

computer simulations perspectives. The reader interested to have more general information

can refer to several excellent reviews published on this topics [4, 5, 55].

Mixed solvents

As reported in the previous paragraphs, the differential interaction of the protein with

the components of mixture is described in terms of preferential solvation. Preferential solvation

can be studied with different experimental techniques such as vapour pressure osmometry

[56], time-resolved fluorescence measurement [115], ultrafast two-dimensional infrared (2D-

IR) spectroscopy [57], X-ray crystallography, and time-relaxation NMR spectrometry [4, 52].

The latter technique measures the local solvent concentration in contact with the magnetized

nuclei. X-ray diffraction studies can also provide information on the preferential binding of

cosolvent molecules in the crystal structure. This approach is also used to localize preferential

binding of small molecules for drug design [117, 118]. In fact, the binding sites localized in the

crystal structure are usually the same as in solution (see for example [119-122]).

Preferential solvation can affect structural, dynamical and functional properties of

proteins and peptides in solutions (see Fig. (7)). For the sake of simplicity, the different effects

on biomolecular systems are classified into three intertwined groups: 1) effects on stability and

folding; 2) effects on aggregations; 3) effects on enzymatic activity. For the last one, we

consider more specifically preferential solvent binding that can interfere with enzymatic

mechanisms.

Effect on stability and folding

Despite the large amount of experimental data cumulated in the last decades, the

mechanism by which organic solvents or cosolvents stabilize or destabilize proteins or

peptides remains a matter of debate [4, 55]. The chemical environment is an important factor

in driving protein folding. Many proteins and peptides, being poorly soluble in water, are stable

in their folded state only inside the lipid bilayer of cellular membranes. The different

mechanisms, formulated to explain the stabilizing effect, advocate direct and indirect effects.

The latter ones are generally due to the nature of interaction between the solvent and water.

These interactions can either reinforce the hydrogen bond network of the water (kosmotropic

solvents) or destabilize them (chaotropic solvents). Recently, it has been shown that

traditionally chaotropic molecules (e.g. urea [58]) does not necessarily perturb the water

structure and the claimed indirect mechanism for their action seems not to be a valid option.

Direct effects are owing to the binding of solvent molecules on the surface of peptides

or proteins. The direct interactions can favour the stabilization/destabilization of the secondary

structure of the biomolecule in different ways. The enhancement of stabilizing interactions

(e.g. hydrogen bonds) by coating the biopolymer with a cosolvent layer, the reduction of the

dielectric constant on molecular surface and the consequent stabilization of hydrophobic

interactions between side chains, are some of the most reasonable explanations [59].

To find clues to support these hypotheses, many experimental studies on biopolymers

have been performed in aqueous mixtures with different organic cosolvents. Cosolvents such

as DMSO, acetonitrile, DMF and short chain alcohols (in particular fluorinated ones), are

commonly used as stabilizing or denaturing agents. Their stabilizing properties are a

consequence of the peculiar physico-chemical properties of the mixtures they form with water.

For example, computer simulations of the fluorinated alcohol TFE and HFIP (both promoting

stabilization effects on isolated α-helix or β−hairpins forming peptides in solution) have

provided strong evidence in support of a coating mechanism as one of the most important

stabilizing factor [97, 124].

For proteins, although several MD simulation studies in pure solvents have been

performed [60, 61], relatively smaller number of publications have addressed effects on the

stability or enzymatic reactivity in non-natural solvent mixtures. Cosolvents can indeed

dramatically affect also the structural properties of proteins and, therefore, theoretical

investigations can help to understand their role in the protein stability and catalysis.

Protein aggregation

Peptide and protein destabilizing cosolvents are used to induce protein misfolding [62].

This process is accompanied by the formation of highly structured thread-like aggregates

termed amyloid or amyloid-like fibrils. The study of protein aggregation is very important for

understanding a variety of human diseases that are now thought to be associated with

formation of highly organized protein aggregates [62]. Among cosolvents, fluorinated alcohols

(TFE and HFIP) highly promote fibrils formation by promoting change and/or stabilization of β-

strands conformations in the secondary structure of different proteins [63].

Several MD studies have been conducted to investigate the misfolding and

aggregation mechanism [64]. Isolated amyloid beta and prion H1 peptides have been studied

in fluorinated alcohols to gain insights on the effect of solvent induced secondary structure

stabilizations [65]. However, computational studies of peptide aggregation are by far quite

limited to pure water solutions [66].

Catalysis

The following general conclusions summarize the experimental achievements in non-

aqueous enzymology: 1) single or clusters water molecules in the interior or on the protein

surface are very important for enzymatic catalysis; 2) polar solvents tend to strip these water

molecules off, thereby reducing the catalytic activity; 2) more hydrophobic solvents tend on the

contrary to improve the catalytic activity by retaining the molecules bound to the proteins

cause of their hydrophobicity.

The main interpretation of these phenomena is related to the increased rigidity of the

enzyme having most of the lubricant water molecules removed. In pure hydrophobic solvents

(e.g. hexane), the structure of the enzyme is retained but the flexibility can be reduced. This

effect promotes the ligand-induced memory effect (see above): the protein remains kinetically

trapped in a configuration favourable for the catalytic function of the enzyme [4]. On the other

hand, mixtures of polar organic solvents with water can be very complex and, as consequence

of the point 2 process, deplete the catalytic activity and denature the protein [67]. Therefore,

for these solvents a delicate equilibrium between the amount of water and cosolvents is

required for tuning both catalysis and protein stability.

Theoretical studies of the protein catalytic activity in pure organic solvents support the

experimental evidences on the role of these solvents in changing enzymatic activity, and of

conserved water layers in maintaining the catalytic efficiency of the enzyme [60]. Combination

of classical MD simulations with QM methods [125, 132] has been used for these studies. In

this approach, the active site geometries of enzyme-substrate complex generated using

classical MD simulations in explicit solvents, are used to study the mechanism of reaction by

QM methods. This approach has been successfully used to study, for example, the

regioselectivity of subtilisin in DMF [68] or more recently the γ-Chymotrypsin in acetonitrile

media [69].

Finally, MD simulations studies of enzymes in water/cosolvent mixtures have provided

molecular evidences in support of other solvent mediated effects, as water stripping [70],

reduced protein motilities and the pH memory effect [71, 72].

SOLVENT USED TO STUDY PROTEINS AND PEPTIDES

The solvents used in biochemical and protein bioengineering studies are numerous.

They have different properties and they are selected according to their effects on protein

stability and activity. In the latter case, the polarity is used to enhance hydrophobic condition

of the solution that in turn increases the solubility of reagents and products.

As mention before, computer simulation studies of biomolecules in non- aqueous

environments is limited by the complexity of solvent structure and the amount of physical data

available to assess and refine the quality of the solvent model. In Table I, a list of organic

solvents used to study using MD simulations their effects on protein or peptides is reported.

The most investigated solvents and cosolvents are: methanol, DMSO, fluorinated alcohols,

urea, trehalose, glycerol, TMAO and more recently ionic liquids. The other solvents like

tetrahydrofuran, ethanol, acetone, DMF, acetonitrile are also studied but so far in less

extensive way.

Methanol and ethanol

Pure methanol and mixture with water are commonly used to increase hydrophobicity

of aqueous solution and to solubilise the structure of hydrophobic amino acid rich peptides

and proteins. It was one of the first non-aqueous solvents after water to be deeply studied with

MD not only for the availability of experimental data but also for the advantage due to its low

density that permits to reduce the number of solvent molecules per unit of volume compared

to water and, therefore, to extend the time length of MD simulations. This property is

particularly useful to study peptide folding mechanisms on long simulations time scale [73].

Pure methanol is also used to mimic membrane environment and study membrane

soluble peptides. For example, membrane soluble α-helix forming peptides (e.g. alamethicin

[138-140], melittin [138, 141, 142] and pulmonary surfactant lipoprotein C [90, 143]) have

been studied in pure methanol. For these peptides, simulations have shown that methanol

molecules have a slight tendency to stabilize the α-helix contents compared to pure water. An

alcohol chain length dependence of Melittin secondary structure stabilization was also

observed using very short time scale simulation in pure methanol, ethanol, n-propanol and n-

butanol and at different temperatures [74]. These results are consistent with the larger

stabilization of α-helix observed when peptides are embedded in lipid bilayers [139, 142, 144,

145].

An interesting comparative MD NMR study of the hormone [Val5]-Angiotensin in 25%

(v/v) water/methanol has been recently published [75]. The results of the simulations

confirmed the micro-heterogeneity of the methanol/water mixture observed in the NMR

experiments. In this condition, most peptide hydrogen atoms are preferentially solvated by

interactions with methanol molecules. The peptide BBA5, a family of peptides designed to

exhibit a ββα fold in water, was also recently studied in methanol/water solutions using a

combination of NMR and molecular dynamics. The results evidenced the dual tendency of

methanol to reinforce the secondary structure of the peptide and also, by weakening

hydrophobic core interactions, to expand the hydrophobic core [76]. A comparative MD

simulation study of the antimicrobial hlF1-11 derived from the first 11 N-terminal amino acids

of the human lactoferrin protein in different solvent has been also recently reported [77]. The

peptide has been studied in pure methanol as well as in the membrane mimetic 4:4:1

methanol–chloroform–water mixture. Paschek al. have study the effect of the changing

methanol/water mixture composition on the shift of the amide I band of a 20 amino acid alpha-

helical AK peptide [78].

Several simulation studies of non-natural peptides (in particular β-peptides) in methanol

solution have been published [150-158] (see also reviews [73, 79]). They are mainly focused

on the dynamic behaviour (for example, the reversible folding) of these peptides in methanol

and other non-aqueous environments. Fluorinated β-peptides have been also recently

investigated with MD simulations in pure methanol [80].

Pure ethanol and mixture with water have been used to study proteins [160, 161] and

different peptides [162-164]. Fioroni et al. have performed an interesting study by combining

NMR data and MD simulation of a tetra-peptide in EtOH/water 30 % v/v. The results of

simulations confirm the experimental evidences that ethanol molecules (in contrast to TFE

molecules) do not show direct interactions with the peptide surface [81]. Lousa et al. have

recently reported an interesting comparative MD study of two proteases (pseudolysin and

thermolysin) in water and in mixtures of water/ethanol at 25 % (v/v). The MD simulations were

extended up to 1 µs. The results of the simulations evidenced a differential stability (with

pseudolysin more stable then thermolysin) in ethanol/water mixtures of the two proteins in

good agreement with experimental data. The different stability seems related to a higher

preferential interaction of ethanol molecules with the thermolysin than with the pseudolysin.

Fluorinated alcohols

Fluorinated alcohols such as HFIP and TFE are commonly used to study

conformational states of small peptides and proteins [82]. These molecules have the capability

of increasing the content of secondary structure (α-helix, β-hairpins) in proteins and secondary

structure forming peptides [82]. The stabilizing effects of these alcohols change according to

their size and chemical structure [82]. Recent studies [83, 84] have demonstrated a stronger

secondary structure stabilizing effect of HFIP in comparison to TFE. One reason for this effect

is related to the low polarity of the solvent [85]. Among the possible mechanisms, preferential

solvation of the folded state by fluorinated alcohols is the most accredited [86]. According to

this hypothesis, fluorinated alcohols act within the context of a pre-existing helix-coil

equilibrium, and the preferential interaction of TFE with the folded state shifts the equilibrium

toward more structured conformations [86]. Alternative mechanisms have been proposed to

explain the stabilizing effect, for instance reinforcement of hydrogen bonds between carbonyl

and amidic NH groups by the removal of water molecules in the proximity of the solute [87]

and/or lowering of the dielectric constant of the medium [82]. Furthermore, the low polarity of

the cosolvent allows formation of micro-clusters in aqueous solution. The evidence for micro-

heterogeneity in TFE/HFIP water mixtures comes primarily from NMR and FTIR spectroscopic

studies [167, 172-175]. In addition, SAXS studies indicate that cluster formation is a function

of the concentration of both TFE [83] and HFIP [176, 177]. In both solvents, the micro-

heterogeneity (see for the TFE Fig. (9)) reaches a maximum value at a cosolvent

concentration of 30% v/v [166, 167], that is the concentration normally used for the study of

biopolymers. HFIP and TFE clusters in water have been proposed to assist the folding of

secondary-structure elements by providing a solvent matrix that promotes hydrophobic

interactions between amino acid side chains [83, 88]. This hypothesis has found experimental

support in several NMR studies involving small peptides in TFE and HFIP mixtures with water

[4, 165, 179, 180].

Different models of fluorinated solvents are available for studying biomolecules in

solutions [37, 181, 182]. We have proposed models of TFE and HFIP specifically optimized to

reproduce the physico-chemical properties of the neat liquid and mixtures with water [89, 90].

For the HFIP model, recent X-ray diffraction, small angle neutron scattering and NMR

spectroscopy measurements have further validated the good quality of this model [91]. In

particular, our model was able to predict the structural transition between the two regimes of

predominant solvent clusterization observed in the experiments [91]. Both the TFE and HFIP

models have been used for simulations of secondary structure forming peptides. In particular,

α-helix forming peptide melittin was studied in mixtures at 30% v/v of TFE [59] and HFIP [45]

with water. Melittin is a 26 amino acids amphiphilic peptide from honey bee venom, which is

largely unstructured in water but adopts a α-helical structure upon addition of TFE [92] or

HFIP [93, 94]. The circular dichroism ellipticity at 222 nm reaches a maximum at around 10%

v/v for a mixture of HFIP/water, with a plateau between 10 and 40% v/v [83]. This stabilizing

effect has been theoretical investigated using comparative simulations in water and in 30% v/v

TFE/water mixture [59]. These studies have provided for the first time a theoretical support to

the experimental hypothesis of the coating effect by the fluorinated solvents on the structure of

α-helix forming peptides in solution [165, 179, 180]. The coating of the peptide surface

reduces its interaction with water, stabilizing the helical conformation. A similar study on

Melittin in 30% v/v HFIP/water mixture showed that the main mechanism to limit the unfolding

of the peptide in water is the preferential solvation of the peptide surface by cosolvent

molecules (see Fig. (5)) [94].

The stabilization effect induced by fluorinated solvents has also been shown in other

MD simulation studies of different peptides [129, 185-193] and proteins [194-196].

Dimethylsulfoxide

Another broadly used organic solvent in non-aqueous enzymology and protein

bioengineering [95] is the dimethylsulfoxide. For example, DMSO/water mixtures are used to

increase the solubility of hydrophobic protein substrates. Pure DMSO or in its mixture with

water is also used as cryo-protective agent for X-ray crystallography, to study protein or

peptide folding, to enhance membrane permeability, and to promote cell fusion. DMSO solvent

is also a good and convenient solvent to mimic the lipid bilayer hydrophobic environment.

Several studies of peptides and proteins have been performed in pure DMSO and

mixtures with water (see Table I and II). The simulation results have been compared with

experimental data to provide insight on the structure and folding mechanism of peptides and

proteins (see also [73] for a review).

The heme domain of Cytochrome P450 BM-3 from Bacillus megaterium [198] has

been used as protein model for theoretical and experimental investigations in DMSO/water

mixture. The study of hP450 in aqueous solution has been investigated by various research

groups using MD simulations [199-204] (see also the comprehensive review on P450 BM3

enzyme by Whitehouse et al. [96]). Most of these studies have been focused on the access of

natural substrates into the active site cavity [97, 98]. In a recent article, we have analyzed the

structural and dynamical properties of hP450 in pure water and in a 14 % v/v DMSO/water

mixture on nanosecond time scale using MD simulations. We have found that DMSO, at this

concentration, does not unfold the protein during the simulation time. However, the structural

and dynamical behaviour of the protein shows significant differences in regions important for

regulating catalytic activity. In particular, DMSO tends to modify the structure in

correspondence to helices F and G, especially in the FG loop. In pure water, these regions

showed less variation form the crystal structure but larger fluctuations [99]. Furthermore,

DMSO molecules distributed on the protein surface (see Fig. (10)) and preferentially

associated to concave protein regions with no evident correlation with properties of surface

residues. This might be due to the size and dipolar aprotic character of the solvent, which

tends to maximize the interaction with amino acids present in cavities and grooves of the

protein surface. In addition, the DMSO diffusion into the active site was kinetically very slow; in

a simulation time of 15 ns, none of the DMSO molecule was able to access the active site

tunnel. Longer simulations and/or use of enhanced sampling of the conformational space of

P450 BM-3 might allow the study this diffusion process. The effect of DMSO molecules on the

active site of P450 BM-3 heme domain was also investigated to elucidate the possible

mechanisms of cosolvent induced inactivation [100]. In fact, different experimental clues have

suggested that the protein inactivation may be determined by a possible interaction between

DMSO molecules with residues in the P450 BM-3’s active site. Therefore, MD simulations with

different DMSO molecules positioned inside the access channel to the active site of the

enzyme were performed. The results of these simulations supported the hypothesis that

activity inhibition might result from the displacement of the heme coordinating water by DMSO

molecules [100]. Experimental measurements of protein activity have shown that the

displacement mechanism is less effective for the wild type than for the F87A mutant. The

F87A mutation replaces the bulky phenyl group of F87 with a methyl increasing the DMSO

accessibility to the heme iron (see Fig. (11)).

Recently, these hypotheses, formulated from the results of MD simulations, have been

validated by the determination of the X-ray structure for the hP450 wild type and its F87A

mutant at co-crystallized at different DMSO concentrations [101, 102]. The presence of a

DMSO molecule coordinating the heme iron at the 6th position and the absence of structural

unfolding in the crystal structure clearly indicate an inactivation mechanism of P450 BM-3

mediated by the direct interaction of the DMSO with the heme iron.

Urea and Guanidinium salts

Urea and Guanidinium molecules are common denaturating cosolvents for proteins.

The nature of their effect is still controversial and direct and indirect mechanisms have been

proposed for these molecules [207].

Concerning the indirect, chaotropic mechanism, recent studies have shown that urea

molecules, even at large concentration typically used for protein denaturation, do not perturb

the water structure as proposed by the indirect mechanism [58]. These results indicate that a

direct interaction mechanism of urea with the protein is more likely to occur. The direct

mechanism hypothesis is based on the amphiphilic nature of urea that permits interactions

with hydrophobic and hydrophilic parts of biomolecules. This dual effect of the urea is

supported by results of several MD studies on peptides and proteins (see Table II and III). The

preferential interaction of urea, with more hydrophobic solvent exposed amino acids of

proteins, increases the polarity of the protein surface that would weaken protein stabilizing

hydrophobic interactions [208-211]. On the other hand, the charged urea amino groups can

compete in the hydrogen bonds with pivotal residues for the protein structure with the

consequence of altering their structural functions and thereby compromising the stability of the

protein structure [104, 212-216].

Gmd+ ion is normally used as chloride or tiocianate salt. As for urea, the mechanism

of protein denaturation induced by these salts is still unclear, though in the last few years a lot

of experimental and theoretical efforts have been dedicated to solve this hurdle [207].

Experimental SANS studies of GdmSCN showed very poor hydration of both Gdm+ and

tiocianate, supporting a direct interaction mechanism [103]. In addition, differently from Urea,

Gmd+ cations have a low tendency to form aggregate in solution. Gdm+ ion are characterized

by a rather hydrophobic surfaces that may interact with similar protein surfaces to enable

protein denaturation [104] and a positive charge that can form strong ion pair with negatively

charged carboxylate groups of side chain amino acids. These effects have been investigated

with simulation of different proteins and peptides (see Table II and III).

Glycerol, trehalose, glycine betaine and TMAO

These molecules are stabilizing osmolytes. They are present in living organism and

their function is to protect them from extreme conditions such as dehydration and/or freezing.

In particular, trehalose is a non-reducing disaccharide composed of two D-glucopyranose

units in α,α(1→1) linkage (see Fig. (8)). It is naturally abundant, especially in the hemolymph

of insects and into the cells of other organisms, which are subjected to the anhydrobiosis

phenomenon. Trehalose under this condition plays a role in promoting a protecting action on

the cellular membrane and on proteins. The mechanism of this stabilization effect is yet to be

clarified. Three hypotheses have been suggested based on experimental data available [105]:

1) water-replacement hypothesis: trehalose substitute the hydration water molecules and

stabilize the protein with hydrogen bonds; 2) water-layer: the sugar forms a protecting layer

that prevent the dehydration of the water hydration layer and 3) mechanical-entrapment

hypothesis: trehalose forms a glass surrounding the protein and trapping it in a stable

conformational state. Several molecular dynamics simulations studies have been reported in

the literature on different proteins to prove the three different hypotheses. MD Simulations of

carboxy-myoglobin in glassy state (89% w/w trehalose/water) and lysozyme, in moderately

concentrated solution, (see Table II) have evidenced on short simulation time scales (up to 2.5

ns) a reduced mobility of the enzymes supporting the third hypothesis.

Polyols (as glycerol, sorbitol) are also very important kosmotropic cosolvents. In

particular, glycerol is frequently used as cryoprotectant. Also for these osmolytes, several MD

simulations of different peptides and proteins have been performed (see Table II and III). As

an example, for cytochrome c in glycerol/water mixtures, the results of these studies evidence

the presence of a damping effect on molecular fluctuations induced by the glycerol [106]. In

addition, a reduction of the protein compressibility in good agreement with experimental data

was observed. This effect has been ascribed to the decoupling of the protein motion from the

solvent motion induced by the presence of glycerol. In fact, it suppresses the exchange

between water molecules bind to the protein surface and those in the hydration shell. In this

way, volume fluctuations of the protein core become disconnected from those of the solvation

shell [106]. A probable reason for this effect is the increased viscosity of the solvent in

solvation shell due to the presence of glycerol. This hypothesis is consistent with experimental

and theoretical evidences of the viscosity effect on protein and peptide dynamics [91, 96, 221-

225]. For a recent account on the viscosity effect on protein folding see also the review by

Hagen [107].

TMAO is another common natural osmolyte or chemical chaperone. It accumulates in the

intracellular environment at relatively high concentration, in response to osmotic or hydric

stress produced by the exposure to high salinity level, hydrostatic pressure, and anhydrobiosis

processes. MD simulations have evidenced the direct interaction between TMAO and protein

as possible mechanism of stabilization [108]. In this study, the protein γ-chymotrypsin inhibitor

was simulated in a solution of TMAO 4 M, urea 8 M and water. In the presence of TMAO, the

protein reduced considerably its mobility and retains its initial fold compared to a 8M

urea/water simulation [108]. The simulations evidenced a tendency of TMAO to reinforce

water-water and water-urea hydrogen bond interactions. In this way, urea does not interact

with the protein surface and, therefore, does not favour the exposure of hydrophobic internal

residues that can initiate the protein unfolding. A similar trend was observed in a MD study on

the prion protein [109].

Betaines and in particular the glycine betaine (N,N,N-trimethylglycine) are also

common natural osmolytes that help cells to survive osmotic stresses. So far few MD studies

have been reported on the effect of this osmolytes on peptides and proteins [110].

Finally, the behaviour of these osmolytes has been also generalized in the framework

of a statistical mechanic model that considers only the interaction of osmolytes with the protein

backbone. The model captures many common features of different osmolytes [111],

reinforcing the relevance of the direct effects in the osmolyte stabilization mechanism.

Ionic liquids

Biocatalysis in conventional organic solvents often suffers from reduced activity,

selectivity or stability of the enzyme [95]. Furthermore, organic solvents are normally toxic and

not environmentally friendly. For these reasons, the search for greener solvents is rapidly

becoming of utterly importance in industrial biocatalysis. Since the last few years, the interest

in ionic liquid as green solvent for biotechnological applications is growing exponentially [112].

This novel class of solvents composes of an organic cation (mostly an alkyl-substituted

imidazolium or pyridinium cation) and an anion, which can be organic and inorganic. They are

not flammable, thermally stable and do not have a measurable vapour pressure, which makes

them interesting from safety and environmental standpoints. Moreover, their physico-chemical

properties (for example polarity, hydrophobicity or miscibility) can be tailored to specific

applications. These capabilities are particularly attractive for protein bioengineering to

overcome environmental and safety hurdles associated with the use of conventional organic

solvents/water solutions. Therefore, it is not surprising rapid raising of the study and

applications in enzymatic catalysis [112].

The modelling studies of pure ionic liquids and their water solution are also rapidly

increasing [232-238], though MD studies of protein in ionic liquids are still quite limited [239-

242]. This emerging field is undoubtedly an interesting area of theoretical investigation.

However, the peculiar properties of ionic liquids will probably require more accurate treatment

of the electrostatic interactions in the simulations.

Other solvents

Hexane, DMF, ACN, diisopropyl ether, 3-pentanone, toluene, chloroform and carbon

tetrachloride are typical solvents for bioengineering applications [5]. They are also used in MD

studies of peptides and proteins to understand effects of organic solvents on protein stability

and activity [60]. Several MD simulation studies of different enzymes in these solvents are

reported in literature (see Table III). The general outcome of these simulations has shown that

they reduce protein flexibility (especially in the loop regions) with the retention of their initial

conformations. This result is in agreement with the hypothesis, based on experimental

observations that organic solvents tend to stiffen protein structure by reducing conformational

dynamics (see the first part of the Chapter). This effect is strongly related to the number of

water molecules nested in the enzyme structure [136, 137]. A comprehensive MD simulation

study on this effect for hexane, 3-pentanone, ethanol, diisopropyl ether, and ACN on cutinase

(a serine protease from Fusarium solani pisi) has been recently reported [113]. For each

solvent, different protein hydration levels ranging from 5% to 100 % (weight of water/weight of

protein) have been considered. The results of this study evidenced a solvent dependence of

the structural and dynamical properties of the protein during the simulations. The structure of

this enzyme maintains its native-like conformation until a certain extent of hydration beyond

that the protein structure start to unfold [136, 160]. This effect shows the importance of water

as lubricant for the protein function. As the flexibility of the protein is recovered, cosolvent

molecules promote stabilizing interactions with hydrophobic regions of the protein that

otherwise would not be exposed in pure water and, thereby, shifting the conformational

equilibrium toward the unfolded state. In addition, the polarity of the cosolvent influences

clustering properties of water molecules. In fact, non-polar solvents favour water segregation

on the protein surface with formation of large clusters. On the contrary, polar solvents promote

the formation of small water clusters loosely bound to the protein. Finally, water molecules

distribution in specific regions of the protein surface, seems to be unrelated to the type of

organic solvent used in the simulation [113].

Pure hexane, octane, decane, chloroform and carbon tetrachloride have also been

used to study the effect of hydrophobic environments on protein stability [134, 152, 243-247].

The interface with water of the same hydrophobic solvents has been used as a model for

membrane/water interfaces to study membrane permeation by peptides or hydrophobic

surface-induced peptide folding [248-252].

Finally, an interesting MD study of the Candida Antarctica Lipase B in supercritical

carbon dioxide has been very recently published [114]. This is a promising green-chemistry

solvent for many enzyme-catalyzed chemical reactions [115] but still little is known on the

molecular mechanism that trigger stability of some enzymes in such unconventional

environments.

III. SUMMARY AND PERSPECTIVES

At the end of this survey on computer simulation studies of proteins and peptides in

non-aqueous or in cosolvent/water mixtures, the questions posed in the Introduction should

have found a clear answer. In particular, we have introduced the MD simulations method and

evidenced its powerful capabilities to complement experimental techniques by providing

detailed information of biomolecular solvation phenomena at molecular model. Indeed, we

have also learnt that several aspects of non-aqueous enzymology related to the peptide and

protein dynamics and preferential solvation start to clarify with the support of MD simulation

studies. Still, many fascinating unsolved questions remain to be addressed and the study of

biomolecules in non-aqueous solvents using computational method is a growing field with of

fundamental (e.g. understanding the nature of protein stability and folding) and applied

research (protein bioengineering). Therefore, future endeavours (and this bring us to the

answer of the other question posed in the Introduction) are directed to deeper understanding

of cosolvent effects on molecular structures and to study new important solvent classes. In

this respect, for example, green solvents, as ionic liquids and polymers, are a promising area

of future theoretical investigations [10-12]. For this purpose, the constant increase of computer

performances is extending the time scale of MD simulations to get closer those of experiments

(microseconds to milliseconds) providing more accurate model of these phenomena. In

addition, more accurate force fields parameterized to better describe thermodynamics

properties of the solvents in different conditions are constantly under development [254, 255].

Some of these new force fields include improved treatment of electrostatic interactions by

directly accounting the atomic polarizability that will significantly improve the quality of both

solvent and protein models [256-258].

ABBREVIATIONS

ACN = Acetonitrile.

AMBER = Assisted Model Building with Energy Refinement.

BAGUA = acyclic butylpentamethylguanidinium.

BMIM = 1-butyl-3-methylimidazolium.

CHARMM = Chemistry at HARvard Molecular Mechanics.

CI = γ-chymotrypsin inhibitor.

DCGUA = cyclic decyltrimethylguanidinium.

DMF = Dimethyl formamide.

DMSO = Dimethyl sulfoxide.

EMIM = Ethylmethyl imidazolium.

EtOH = Ethanol.

FTIR = Fast Transform InfraRed.

GROMOS = GROningen Molecular Simulation

HFIP = 1,1,1,3,3,3-hexafluoro-propan-2-ol.

hP450 = heme domain of cytochrome monooxygenase P450 BM-3

(CYP102A1).

MD = Molecular dynamics.

MCGUA = cyclic tetramethylguanidinium.

MeOH = Methanol.

MOEMIM = 1-monoethoxy-3-methylimidazolium.

NMR = Nuclear magnetic resonance.

OPLS = Optimized Potentials for Liquid Simulations.

QM = Quantum mechanics.

SAXS = Small-Angle X-ray Scattering.

SANS = Small-Angle Neutron Scattering.

TFE = 2,2,2-Trifluoroethanol.

THF = Tetrahydrofuran.

TMAO = Trimethylamine-N-oxide.

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Figure Captions

Fig. (1). Description of the energetic terms present in the force field for biomolecular

simulations. The molecules in the picture are: at the centre, the Ace-Ala-NH2; on the right,

a TFE molecule and on the top left side, a water molecule. Only polar hydrogen atoms are

represented.

Fig. (2). Summary of the main sources of experimental data of solvent effects that can be

used to compare with the results of MD simulations of biomolecular systems.

Fig. (3). Correspondence of radial distribution function of a TFE/water mixture with the

physical distribution of molecules in solution. Rings in yellow and cyan correspond to

section views of two spherical sectors centred on the blue atom.

Fig. (4). Example of representation of the volumetric data of water density surrounding a

β-hairpin forming peptide in solution. On each slice of the volumetric data, the local density

in the form of three-dimensional elevation is shown. In the centre of the figure, the

average structure of the peptide is reported. The analysis was performed on a 5 ns long

MD simulation [32].

Fig. (5). Calculation of the local concentration of cosolvent around a peptide. On the left:

averaged local concentration of HFIP (LHC, %v/v) around each residue of the peptide

Melittin [45]. On the right: density distribution of HFIP molecules around the Melittin

calculated from the last 5 ns of the simulations in Ref. [45].

Fig. (6). Classification of solvent according to their effect on the protein structure.

Fig. (7). Summary of the effects of non-natural solvent environment on biomolecules. The

different effects are intertwined.

Fig. (8). Structures of some of the solvent molecules commonly used in MD simulations of

biomolecular systems. A) hexane; B) carbon tetrachloride; C) chloroform; D) acetonitrile; E)

2,2,2-trifluoroethanol; F) 1,1,1,3,3,3-hexafluoro-propan-2-ol; G) ethanol; H) methanol; I)

dimethyl sulfoxide; J) urea; K) trimethylamine-N-oxide; L) trehalose; M) glycerol; N) dimethyl

formamide.

Fig. (9). Snapshots of conformations sampled every 100 ps from a simulation of a 30% (v/v)

TFE/water mixture box. The water molecules are shown in cyan and TFE in red colors.

Fig. (10). Top views of P450 BM-3 heme domain conformation taken from a

DMSO/water MD simulation [116]. Left view: secondary structure representation. Right

view: DMSO surface accessible area (probe radius 0.24 nm) representation. In both

cases, the protein structure is coloured according to the local DMSO concentration

(LDC, calculated at 0.5 nm from the Ca atom of each residue). The lowest and highest

DMSO concentration correspond to regions represented in red and blue colours,

respectively [116].

Fig. (11). Interactions of DMSO with the active site of hP450. (A) Active sites of the crystal

structure (pdb-code: 2J1M) crystallized in 28 % v/v DMSO/water mixture [101]. A DMSO

molecule is coordinating to the heme iron. Anomalous scattering data have shown that the

coordination occurs via the sulphur atom [101]. (B) Snapshot from the simulation of the F87A

mutant of hP450 in DMSO/water mixture [100]. The figure shows two out of three DMSO

molecules that are present in the active site. The DMSO molecules approach the iron and

displace the coordinating water molecule from its position.

Table I: Summary of the most common solvents used to study different properties of proteins

and peptides using MD simulations.

SOLVENT

Name

Preferential

Solvation

Stability,

Folding,

aggregation

Catalysis

Hexane/Octane/Decane/

Cyclohexane/Isopentane/

Toluene.

X

Carbon tetrachloride

X

Chloroform

X

Acetonitrile

X

TFE/water

X

HFIP/water

X

EtOH

X

MeOH

X

DMSO

X

Urea/water

X

TMAO

X

Trehalose

X

Glycerol/Sorbitol

X

DMF

X

THF

X

Ionic liquids

X

TABLE II: Selection of peptides studied in different organic solvent and mixtures. Time indicates the

longest MD simulation reported in the paper.

Peptide

Peptide sequence

Solvent

Time/ns

Force

field

Ref.

Melittin

(monomer)

GIGAVLKVLTTGLPA

LISWIKRKRQQNH2

MeOH

60

GROMOS

[117]

Melittin

(monomer)

GIGAVLKVLTTGLPA

LISWIKRKRQQNH2

30 %HFIP/water

100

GROMOS

[45]

Melittin

(monomer)

GIGAVLKVLTTGLPA

LISWIKRKRQQNH2

30%TFE/water

30

GROMOS

[59]

Melittin

(monomer)

GIGAVLKVLTTGLPA

LISWIKRKRQQNH2

GmdCl 3 M

8

CHARMM

[118]

Melittin

(monomer)

GIGAVLKVLTTGLPA

LISWIKRKRQQNH2

Tetrapropylamm

onium Sulphate

0.1 M,

GmdCl 3 M

10

AMBER 99

[118]

Betanova

RGWSVQNGKYTNNGKTTE

GR

30%TFE/water

20

GROMOS

[59]

β-hairpin from

Protein G B1

GEWTYDDATKTFTVTE

30%TFE/water

20

GROMOS

[59]

β-hairpin from

Protein G B1

GEWTYDDATKTFTVTE

Trehalose 0-

0.26 M

20

GROMOS96

[119]

Alamethicin

Ac-

Aib-P-Aib-A-Aib-A-Q-Aib-V-

Aib-G-L-Aib-P-V-Aib-Aib-Q-

Q-Phol

MeOH

1

GROMOS

CVFF [120]

[121]

[122]

Aib-rich

peptide II

(Aib)5-Leu-(Aib)2

DMSO

0.15

GROMOS

[123]

Leu/Met-

enkephalins

Y-G-G-F-[L/M]

DMSO

2

GROMOS

[124]

β-hexapeptide

H(β2-hVal)(β3-hAla)(β2-

hLeu)(β3-hVal)(β2-hAla)(β3-

MeOH

100

GROMOS

[150,

154,

hLeu)-OH

262]

β-heptapeptide

H(β3-hVal)(β3-hAla)(β3-

hLeu)((S,S)-β3-hAla(αMe))

(β3-hVal)(β3-hAla)-(β3-hLeu)-

OH

MeOH

50

GROMOS

[150,

154,

158,

263,

264]

1: β3-

octapeptide.

2: tethered

1:

(β3‐V)(β3‐K)(β3‐R)BSP(β3

‐F)(β3‐E)(β3‐R)BSP(β3‐Y)

(β3‐I)‐OH

2:

(β3‐V)(β3‐K)(β3‐R)BSE(β3

‐F)(β3‐E)(β3‐R)BSE(β3‐Y)

(β3‐I)‐OH

MeOH

100

GROMOS

[125]

Mixed α/β

Peptides

1: (β2-(S)-F)H(β2-(S)-L)(β3-

(R)-V)I(β3-(S)-Y)

2: Aib(β3-(S)-Y)Aib(β3-(S)-

K)Aib(β3-(S)-D)Aib(β3-(R)-V)

MeOH

200

GROMOS

53A6

[126]

Fluorinated β-

peptides

1: H-(β-hVal)(β-hAla)(β-

hLeu)((S,S)-β-H)Ala(α-F)(β-

hVal)(β-hAla)(β-hLeu)-OH

2: H(β-hVal)(β-hAla)(β-

hLeu)((S,R)-β-hAla(α-F))(β-

hVal)(β-hAla)(β-hLeu)-OH

3: H(β-hVal)(β-hAla)(β-hLeu)-

((S)-β-hAla(di-α-F))(β-

hVal)(β-hAla)(β-hLeu)-OH

MeOH

100

GROMOS

45A3

[80]

E7

EPAEAAK

ETH/TFE/HFIP

0.02

CVFF

[127]

Galanin

GWTLNSAGYLLGPHAIDNH

RSFSDKHGLT-NH2

TFE

0.12

CHARMM

[128]

Designed

Peptide

Ac-DTESILRZAFELHNK-NH2

50% TFE/water

0.5

AMBER

[129]

Bombesin

ZRLGNZWAVGHLM-NH2

30% TFE/water

10

GROMOS

[130]

Channel

forming peptide

KKKKPARVGLGITTVLTMTT

QS

30% TFE/water

20

GROMOS

[131]

Myoglobin

C-term

NKALNLFRKDIAAKYKELGY

NG

30% TFE/water

0.2

GROMOS

[132]

PrP106-126

Ac-KTNMKHMAGAAAAG

AVVGGLG-NH2

TFE

100

GROMOS

[133]

PrP106-126

Ac-KTNMKHMAGAAAAG

AVVGGLG-NH2

Hexane

100

GROMOS

[133]

PrP106-126

Ac-KTNMKHMAGAAAAG

AVVGGLG-NH2

DMSO

100

GROMOS

[133]

Prion H1 peptide

MKHMAGAAAAGAVV

30% TFE/water

50

GROMOS

[65]

H1 helix from

the mouse prion

protein

DWEDRYYREN

Urea 3.94M

GdmCl 3.05 M

~35

CHARMM22

[216]

Amyloid-β

peptide

GSNKGAIIGLM

80% HFIP/water

640 a)

GROMOS

[134]

Blocked valine

peptide

Ace-Val-CONHCH3

Urea 8M

4

AMBER/

OPLS

[135]

Derivative

of C-peptide

1) Suc- AETAAAKFLRNHA-

NH2

2) Suc- AKERAFTANAHLA-

NH2

Urea 8M

10

CHARMM

[136]

Aβ16-22

KLVFFAE

Urea 8M

10

AMBER/

OPLS

[137]

Aβ16-22 and Aβ40

Ace-KLVFFAE-NH2 ;

DAEFRHDSGYEVHHQKLVF

FAEDVGSNKGAIIGLMVGGV

V

Trehalose (0-

0.18 mol/L)

300

GROMOS96

[138]

S-peptide

analogue

AETAAAKFLREHMDS

Urea 8M

20

GROMOS

[139]

Fibrillogenic

Binding B18

LGLLLRHLRHHSNLLANI

30 % TFE/water

50

GROMOS

[140]

[Val5]-

DRVYVHPF

25% methanol/

water

300

AMBER99S

B-ILDN

[75]

Angiotensin II

BB5 Peptide

YRVDPSYDFSRSDELAKLLR

QHAG

50% methanol/

water

224

AMBER

FF99

[76]

Peptide hlF1-11

GRRRSVQWCA

Pure methanol,

4:4:1 methanol–

chloroform–

water

100

Amber

FF99SB

[77]

AK peptide

Ac-AA(AAKAA)3AAY-NMe

(0-100)%

methanol/water

40

AMBER-GS

[78]

Anoplin

GLLKRIKTLL

30% TFE/water

8

GROMOS

96

[141]

Peptide sMTM7

EFCLNCVSHTASYLRLWAL

SLAHAQ

DMSO

10

GROMOS96

[142]

Aβ(1-42) peptide

DAEFRHDSGYEVHHQKLVF

FAEDVGSNKGAIIGLMVGGV

VIA

HFIP, TFE,

DMSO

20

GROMOS96

[143]

Magainin 2

GIGKFLHSAKKFGKAFVGEI

MNS

Various water

mixtures of

Urea, GmdCl,

TFE

40

GROMOS96

[144]

aurein 1.2

GLFDIIKKIAESF-NH2

50 % TFE/water

200

GROMOS

54a7

[145]

maculatin 1.1

GLFGVLAKVAAHVVPAIAEH

F-NH2

50 % TFE/water

200

GROMOS

54a7

[145]

citropin 1.1

GLFDVIKKVASVIGGL-NH2

50 % TFE/water

200

GROMOS

54a7

[145]

caerin 1.1

GLLSVLGSVAKHVLPHVVPV

IAEHL-NH2

50 % TFE/water

200

GROMOS

54a7

[145]

Magainin 2

GIGKFLHSAKKFGKAFVGEI

MNS

TFE/water

(10M)

Sorbitol/water(0.

5 M)

Glycerol/water

100

GROMOS96

[146]

(4M)

BB5 Peptide

YRVDPSYDFSRSDELAKLLR

QHAG

Various 30-40%

(v/v) water

Mixture of TFE,

MeOH, Glycerol

170

AMBER

FF99

[147]

β-hairpin

CLN025

YYDPETGTWY

TFE, MeOH,

and DMSO

200

GROMOS

(53A6)

[148]

β-hairpin

CLN025

YYDPETGTWY

GmdCl 3,6 M

Urea 4,8 M

200

GROMOS

(53A6)

[275,

276]

Trp-Cage

NLYIQWLKDGGPSSGRPPP

S

Urea 1.9-5.8 M

32500 a)

Amber (ff94)

[208]

Trp-Cage

NLYIQWLKDGGPSSGRPPP

S

GmdCl 2M

Urea 2 M

1000

Amber (ff99)

[277]

Olygoglycines

G2–5

Urea 2M

TMAO 2M

2

CHARMM-

27

[278]

Triglycine

G3

Glycine betaine

Urea

300

CHARMM27

[110]

Peptide 1

SESYINPDGTWTVTE

Urea 5 M

200

AMBER99

[149]

TRPZIP4

GEWTWDDATKTWTWTE

Urea 5M

200

AMBER99

[149]

Polyglycine and

polyGly-Ser

peptides

G15 and (GS)8

Urea 8M

50

OPLS-AA/L

[150]

a) Replica exchange MD simulation study, in this case the cumulative time is reported.

TABLE III: Selection of proteins studied in different organic solvent and mixtures. Time indicates the

longest MD simulation reported in the paper.

Protein

Solvent

Force field

Time/ns

Reference

Ubiquitin

Hexane

GROMOS

5

[136, 137]

Ubiquitin

60% MeOH/water

ENCAD [151]

0.5

[152]

Cutinase

Pure diisopropyl

ether and water

mixtures

GROMOS

10

[113]

Cutinase

Pure 3-pentanone

and water mixtures

GROMOS

10

[113]

Cutinase

Pure EtOH and

water mixtures

GROMOS

10

[113]

Pseudolysin

25 % water/EtOH

GROMOS 53A6

1000

[153]

Thermolysin

25 % water/EtOH

GROMOS 53A6

1000

[153]

Cutinase

Pure ACN and

water mixtures

GROMOS

10

[113]

Cutinase

Pure Hexane and

water mixtures

GROMOS

4/10

[136, 137, 160]

Cutinase

Pure [BMIM][PF6]

and [BMIM][NO3]

GROMOS96

(43A1)

10

[154]

Barnase

Urea 8 M

CHARMM and

OPLS

0.8/2

[155, 156]

CI2

Hexane

AMBER

0.3

[157]

CI2

Urea 8 M

ENCAD

20

[158]

CI2

Urea 4 M

ENCAD

10

[108]

CI2

4 M TMAO/

8 M Urea

ENCAD

10

[108]

CI2

10 M Urea

GROMOS

G53a6

150-800

[159]

CI2

8 M Urea;

8M Urea/1 M

Trehalose

GROMOS 431

100

[160]

Cytochrome

P450 BM3

14% (v/v)

DMSO/water

GROMOS

15

[54, 55]

cytochrome c

60% (v/v) Glycerol/

water

CHARMM

1

[106]

Subtilisin BPN’

Octane

AMBER

0.45

[70]

Subtilisin BPN’

THF

AMBER

0.45

[70]

Subtilisin BPN’

ACN

AMBER

3.6/0.45

[135, 286]

Subtilisin Carlsberg

DMF

AMBER

0.30

[161]

Subtilysin Carlsberg

DMSO

AMBER

0.74

[162]

Subtilysin Carlsberg

Hexane

GROMOS 53A6

10

[163]

Subtilysin Carlsberg

ACN

GROMOS 53A6

10

[164]

Acylphospatase

25% (v/v)

TFE/water

GROMOS

80

[165]

Carboxy-myoglobin

Trehalose/

water glass

CHARMM

0.3

[223, 290]

Carboxy-myoglobin

Glycerol glass

CHARMM

10

[166]

Lysozyme

Trehalose

GROMOS

2.5

[105]

Lysozyme

Glycerol

AMBER

2

[41]

Lysozyme

Glycerol/water

5.87 M

CHARMM42

(c32b2)

20

[167]

α-Chymotrypsin

30 % TFE/water

GROMOS96

35

[168]

α-Chymotrypsin A

Different

polyarginine

(R,RR,RRR)

CHARMM27

100

[169]

©−Chymotrypsin

ACN

AMBER03

8

[69]

Candida Antartica

Lipase B

methanol

chloroform.

AMBER

2.5

[170]

Candida Antartica

Lipase B

Hexane, tert-butyl

ether, methanol,

tert- butyl alcohol

CHARMM27

20

[69]

Candida Antartica

Lipase B

Supercritical

CO2/water

mixtures

OPLS-AA

20

[114]

Candida Antartica

Lipase B

BMIM-PF6

BMIM-NO3

BMIM-BF4

MOEMIM-BF4

BAGUA-BF4

BCGUA-BF4

MCGUA-NO3

DCGUA-NO3

AMBER-03

5

[171]

Burkholderia cepacia

lipase

Toluene

Amber99

30

[172]

β-2 microglobulin

26% TFE/water

CHARMM

60

[173]

Lysozyme

Urea 8M

CHARMM

1000

[209]

Protein L

Urea 10 M

GROMOS96

(43a1)

30

[293]

Villin headpiece

protein HP-35 and

A doubly norleucine-

substituent mutant

(Lys24Nle/Lys29Nle)

Urea 5 M

AMBER99

200

[174]

Human zinger Finger

Protein

EMIMCF3SO3/wate

r mixtures

2.36-4.41 M

CHARMM

200

[175]

Triosephosphate

isomerase from

Trypanosoma cruzi

Decane

GROMOS96

(43a2)

40

[176]

Cardosin A

10, 90 % (v/v)

TFE/water

GROMOS 53a6

100

[177]

Cold Shock protein

Bc-CsP from Bacillus

caldolyticus

Urea 8M

OPLS-AA

453

[178]