Temperature control algorithms in molecular dynamics (MD) simulations are necessary to study isothermal systems. However, these thermostatting algorithms alter the velocities of the particles and thus modify the dynamics of the system with respect to the microcanonical ensemble, which could potentially lead to thermostat-dependent dynamical artifacts. In this study, we investigate how six well-established thermostat algorithms applied with different coupling strengths and to different degrees of freedom affect the dynamics of various molecular systems. We consider dynamic processes occurring on different times scales by measuring translational and rotational self-diffusion as well as the shear viscosity of water, diffusion of a small molecule solvated in water, and diffusion and the dynamic structure factor of a polymer chain in water. All of these properties are significantly dampened by thermostat algorithms which randomize particle velocities, such as the Andersen thermostat and Langevin dynamics, when strong coupling is used. For the solvated small molecule and polymer, these dampening effects are reduced somewhat if the thermostats are applied to the solvent alone, such that the solute's temperature is maintained only through thermal contact with solvent particles. Algorithms which operate by scaling the velocities, such as the Berendsen thermostat, the stochastic velocity rescaling approach of Bussi and co-workers, and the Nosé-Hoover thermostat, yield transport properties that are statistically indistinguishable from those of the microcanonical ensemble, provided they are applied globally, i.e. coupled to the system's kinetic energy. When coupled to local kinetic energies, a velocity scaling thermostat can have dampening effects comparable to a velocity randomizing method, as we observe when a massive Nose-Hoover coupling scheme is used to simulate water. Correct dynamical properties, at least those studied in this paper, are obtained with the Berendsen thermostat applied globally, despite the fact that it yields the wrong kinetic energy distribution.

A multiscale model is presented to elucidate protein adsorption and transport behaviors in ion‐exchange chromatography (IEC) adsorbent particles that have either an open pore structure or charged dextran polymers grafted into the pores. Molecular dynamics simulation is used to determine protein diffusion and partitioning in different regions of the adsorbent pore, and these outputs are used in numerical simulations of mass transfer to determine the intraparticle protein concentration profile and the mass‐transfer rate. Modeling results indicate that, consistent with experimental observations, protein transport can be faster in the polymer‐grafted material compared to the open pore case. This occurs when favorable partitioning of protein into the polymer‐filled pore space is combined with relatively high protein mobility within this region. The modeling approach presented here should be applicable to proteins and adsorbents with different properties, and could help elucidate the factors that control adsorption and transport in various IEC systems. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3888–3901, 2014

Multiscale simulation is used to study the adsorption of lysozyme onto ion exchangers obtained by grafting charged polymers into a porous matrix, in systems with various polymer properties and strengths of electrostatic interaction. Molecular dynamics simulations show that protein partitioning into the polymer-filled pore space increases with the overall charge content of the polymers, while the diffusivity in the pore space decreases. However, the combination of greatly increased partitioning and modestly decreased diffusion results in macroscopic transport rates that increase as a function of charge content, as the large concentration driving force due to enhanced pore space partitioning outweighs the reduction in the pore space diffusivity. Matrices having greater charge associated with the grafted polymers also exhibit more diffuse intraparticle concentration profiles during transient adsorption. In systems with a high charge content per polymer and a low protein loading, the polymers preferentially partition toward the surface due to favorable interactions with the surface-bound protein. These results demonstrate the potential of multiscale modeling to illuminate qualitative trends between molecular properties and the adsorption equilibria and kinetic properties observable on macroscopic scales.

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This work examines how molecular properties affect protein adsorption in polymergrafted ion exchange chromatography (IEC) resins, as predicted by multiscale computational modeling. Polymer-grafted IEC resins, which have charged polymers grafted into their pores, are widely used because they can enhance the protein binding capacity and adsorption kinetics relative to traditional macroporous resins with open pore structures. Multiscale modeling is used to elucidate I am grateful for the support of various mentors, fellow students, friends, and family in helping me complete this work. Thanks to my first research advisor, Professor Jack Hudson, whose wisdom and perspective will not be forgotten. Thanks as well to Prof. Giorgio Carta, for introducing me to the field of protein chromatography and providing me the opportunity to do both modeling and laboratory research. Thanks to Professor Michael Shirts, for guiding my modeling work and helping me improve my research, writing, and presentation skills. I'd also like to thank the members of my Ph.D. committee, Professors Ford, Cafiso, and Zhigeli, for their careful review of my work. Thanks as well to Vickie, Teresa, Jennifer, and Kim for their help over the years. I very much appreciate the financial support I have received to do this research. Thanks to the U.S. National Science Foundation for their grant, and the UVa Chemical Engineering Department for providing additional funding. Thanks as well to UVa Advanced Computing Services and Engagement and the National Institute for Computational Sciences for providing the computing resources for our simulations. The advice and friendship of my fellow students at UVa have been invaluable in completing this work. In particular, thanks to Jing Guo for helping me learn experimental techniques, and to Ed Wong for countless helpful discussions.