The role of the protecting osmolyte Trimethyl N-oxide (TMAO) in counteracting the denaturing effect of urea on a protein is quite well established. However, the mechanistic role of osmolytes on the hydrophobic interaction underlying protein folding is a topic of contention and is emerging as a key area of biophysical interest. Although recent experiment and computer simulation have established that individual aqueous solution of TMAO and urea respectively stabilizes and destabilizes the collapsed conformation of a hydrophobic polymer, it remains to be explored how a mixed aqueous solution of protecting and denaturing osmolytes influences the conformations of the polymer. In order to bridge the gap, we have simulated the conformational behavior of both a model hydrophobic polymer and a synthetic polymer polystyrene in an aqueous mixture of TMAO and urea. Intriguingly, our free energy based simulations on both the systems show that even though a pure aqueous solution of TMAO stabilizes the collapsed or globular conformation of the hydrophobic polymer, addition of TMAO to an aqueous solution of urea further destabilizes the collapsed conformation of the hydrophobic polymer. We also observe that the extent of destabilization in a mixed osmolyte solution is relatively higher than that in pure aqueous urea solution. The reinforcement of the denaturation effect of the hydrophobic macromolecule in a mixed osmolyte solution is in stark contrast to the well-known counteracting role of TMAO in proteins under denaturing condition of urea. In both model and realistic systems, our results show that in a mixed aqueous solution, greater number of cosolutes preferentially bind to the extended conformation of the polymer relative to that in the collapsed conformation, thereby complying with Tanford-Wyman preferential solvation theory disfavoring the collapsed conformation. The results are robust across a range of osmolyte concentrations and multiple cosolute forcefields. Our findings unequivocally imply that the action of mixed osmolyte solution on hydrophobic polymer is significantly distinct from that of proteins.
The origin of the rapid dynamical slowdown in glass forming liquids in the growth of static length scales, possibly associated with identifiable structural ordering, is a much debated issue. Growth of medium range crystalline order (MRCO) has been observed in various model systems to be associated with glassy behavior. Such observations raise the question of whether molecular mechanisms for the glass transition in liquids with and without MRCO are the same. In this study we perform extensive molecular dynamics simulations of a number of glass forming liquids and show that the static and dynamic properties of glasses with MRCO are different from those of other glass forming liquids with no predominant local order. We also resolve an important issue regarding the so-called point-to-set method for determining static length scales, and demonstrate it to be a robust method for determining static correlation lengths in glass formers.
Non-Gaussian nature of the probability distribution of particles' displacements in the supercooled temperature regime in glass-forming liquids are believed to be one of the major hallmarks of glass transition. It has already been established that this probability distribution, which is also known as the van Hove function, shows universal exponential tail. The origin of such an exponential tail in the distribution function is attributed to the hopping motion of particles observed in the supercooled regime. The non-Gaussian nature can also be explained if one assumes that the system has heterogeneous dynamics in space and time. Thus exponential tail is the manifestation of dynamic heterogeneity. In this work we directly show that non-Gaussianity of the distribution of particles' displacements occur over the dynamic heterogeneity length scale and the dynamical behavior course grained over this length scale becomes homogeneous. We study the non-Gaussianity of the van Hove function by systematically coarse graining at different length scales and extract the length scale of dynamic heterogeneity at which the shape of the van Hove function crosses over from non-Gaussian to Gaussian. The obtained dynamic heterogeneity scale is found to be in very good agreement with the scale obtained from other conventional methods.
We present block analysis, an efficient method of performing finite-size scaling for obtaining the length scale of dynamic heterogeneity and the point-to-set length scale for generic glass-forming liquids. This method involves considering blocks of varying sizes embedded in a system of a fixed (large) size. The length scale associated with dynamic heterogeneity is obtained from a finite-size scaling analysis of the dependence of the four-point dynamic susceptibility on the block size. The block size dependence of the variance of the α relaxation time yields the static point-to-set length scale. The values of the obtained length scales agree quantitatively with those obtained from other conventional methods. This method provides an efficient experimental tool for studying the growth of length scales in systems such as colloidal glasses for which performing finite-size scaling by carrying out experiments for varying system sizes may not be feasible.
The rapid rise of viscosity or relaxation time upon supercooling is a universal hallmark of glassy liquids. The temperature dependence of viscosity, however, is quite nonuniversal for glassy liquids and is characterized by the system’s “fragility,” with liquids with nearly Arrhenius temperature-dependent relaxation times referred to as strong liquids and those with super-Arrhenius behavior referred to as fragile liquids. What makes some liquids strong and others fragile is still not well understood. Here, we explore this question in a family of harmonic spheres that range from extremely strong to extremely fragile, using “softness,” a structural order parameter identified by machine learning to be highly correlated with dynamical rearrangements. We use a support vector machine to identify softness as the same linear combination of structural quantities across the entire family of liquids studied. We then use softness to identify the factors controlling fragility.
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