We
present a novel multi-conformation Monte Carlo simulation method
that enables the modeling of protein–protein interactions and
aggregation in crowded protein solutions. This approach is relevant
to a molecular-scale description of realistic biological environments,
including the cytoplasm and the extracellular matrix, which are characterized
by high concentrations of biomolecular solutes (e.g., 300–400
mg/mL for proteins and nucleic acids in the cytoplasm of Escherichia coli). Simulation of such environments
necessitates the inclusion of a large number of protein molecules.
Therefore, computationally inexpensive methods, such as rigid-body
Brownian dynamics (BD) or Monte Carlo simulations, can be particularly
useful. However, as we demonstrate herein, the rigid-body representation
typically employed in simulations of many-protein systems gives rise
to certain artifacts in protein–protein interactions. Our approach
allows us to incorporate molecular flexibility in Monte Carlo simulations
at low computational cost, thereby eliminating ambiguities arising
from structure selection in rigid-body simulations. We benchmark and
validate the methodology using simulations of hen egg white lysozyme
in solution, a well-studied system for which extensive experimental
data, including osmotic second virial coefficients, small-angle scattering
structure factors, and multiple structures determined by X-ray and
neutron crystallography and solution NMR, as well as rigid-body BD
simulation results, are available for comparison.
The mechanisms leading to aggregation of the crystallin proteins of the eye lens remain largely unknown. We use atomistic multiscale molecular simulations to model the solution-state conformational dynamics of γD-crystallin and its cataract-related W42R variant at both infinite dilution and physiologically relevant concentrations. We find that the W42R variant assumes a distinct conformation in solution that leaves the Greek key domains of the native fold largely unaltered but lacks the hydrophobic interdomain interface that is key to the stability of wild-type γD-crystallin. At physiologically relevant concentrations, exposed hydrophobic regions in this alternative conformation become primary sites for enhanced interprotein interactions leading to large-scale aggregation.
Efficient computational
modeling of biological systems characterized
by high concentrations of macromolecules often relies on rigid-body
Brownian Dynamics or Monte Carlo (MC) simulations. However, the accuracy
of rigid-body models is limited by the fixed conformation of the simulated
biomolecules. Multi-conformation Monte Carlo (mcMC) simulations of
protein solutions incorporate conformational flexibility via a conformational
swap trial move within a predetermined library of discrete protein
structures, thereby alleviating artifacts arising from the use of
a single protein conformation. Here, we investigate the impact of
the number of distinct protein structures in the conformational library
and the extent of conformational sampling used in its generation on
structural observables computed from simulations of hen egg white
lysozyme (HEWL), human γD-Crystallin, and bovine γB-Crystallin
solutions. We find that the importance of specific protocols for the
construction of the protein structure library is strongly dependent
on the nature of the simulated system.
charge of MTs decreases electrostatic repulsion, increasing the probability of end-to-end interactions and (2) shielding electrostatically attractive ends of the MTs does not inhibit end-to-end interactions, suggesting that selfassembly is driven predominately by hydrophobic interactions. Finally, data exploring the role of temperature suggests that increasing temperature causes MT filaments to undergo a phase transition resulting in a discontinuous Arrhenius behavior. Together, these results provide insights into tunable parameters driving self-assembly of MT nano-arrays. Adjusting these parameters to optimize assembly will greatly aid in the application of biopolymeric templates for use in nanomaterials applications. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation,
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