Because biological systems span a broad range of length and time scales, they cannot be studied effectively using conventional brute-force simulations. While hardware advances represent one route to improving the computational tractability of simulating biological systems, significant progress can be made via theory through the development of more efficient computational algorithms and simplified models describing protein solutions.
We have developed a general framework for modeling proteins in concentrated and crowded solutions. Our approach accounts for both the intrinsic thermodynamics of folding and the general physical characteristics of the native and denatured states. Protein–protein interactions are derived using the salient physical features of the native and denatured conformations predicted by heteropolymer collapse theory. Ultimately, we are able to study the effects of protein concentration and crowding on protein stability in a computationally efficient manner using transition-matrix Monte Carlo. This approach provides a general theoretical framework to study the generic effects of environmental factors (e.g., temperature, pressure, composition) on protein solution stability.
Using our coarse-grained model, we have studied the effect of temperature, concentration, macrolecular crowding, and protein sequence information on the stability of protein solutions, including aggregation behavior.
Our general approach builds upon the predictions of heteropolymer collapse theory to provide intrinsic protein folding thermodynamics and inter-protein interactions. The end result provides a reactive forcefield that can be used to simulate proteins in solution. In order to simulate these generic systems, we use highly efficient transition-matrix Monte Carlo to calculate thermodynamic properties over a large range of state points from a single simulation.