Computational techniques for efficient conformational sampling of proteins

Abstract

In this review, we summarize the computational methods for sampling the conformational space of biomacromolecules. We discuss the methods applicable to find only lowest energy conformations (global minimization of the potential-energy function) and to generate canonical ensembles (canonical Monte Carlo method and canonical molecular dynamics method and their extensions). Special attention is devoted to the use of coarse-grained models that enable simulations to be enhanced by several orders of magnitude.

Figure 1

Illustration of the importance of the entropic effect for the probability of occurrence of a particular family of structures. The energy surface shown in the picture contains a narrow minimum (left), which is the global minimum, and a broad basin of minima (right). The horizontal line is the approximate boundary of the accessibility of the regions of conformational space belonging to these two basins, DE being the thermal energy at absolute temperature T, g the number of degrees of freedom, and R the universal gas constant. The area closed by the energy curve and the corresponding horizontal line is related to the entropy of each basin. Consequently, at sufficiently high temperature, the free energy of conformations that belong to the right basin will be significantly lower than that of the global-minimum region and these conformations will, therefore, be more probable than those corresponding to the global minimum of the potential energy.