Advances in free-energy-based simulations of protein folding and ligand binding

Abstract

Free-energy-based simulations are increasingly providing the narratives about the structures, dynamics and biological mechanisms that constitute the fabric of protein science. Here, we review two recent successes. It is becoming practical: first, to fold small proteins with free-energy methods without knowing substructures and second, to compute ligand-protein binding affinities, not just their binding poses. Over the past 40 years, the timescales that can be simulated by atomistic MD are doubling every 1.3 years--which is faster than Moore's law. Thus, these advances are not simply due to the availability of faster computers. Force fields, solvation models and simulation methodology have kept pace with computing advancements, and are now quite good. At the tip of the spear recently are GPU-based computing, improved fast-solvation methods, continued advances in force fields, and conformational sampling methods that harness external information.

Figure 1

The accessible time scale for computational biology has grown faster than Moore's law of semiconductors and computing. [1] [2-6].

Figure 2

A. Simulation times for finding native states as a function of protein size, by unrestrained MD, and by MELD, which uses generic directives as restraints. Data from [10,15]. Figure 2B. When will MD find protein native structures of size N? Extrapolations by combining the data in Fig 1 and 2A. The slope for the two lines is calculated as the ratio of the slopes for the biological “law” and the slope for increase in simulation time with protein size. The ordinate in the origin is set to be what simulations can accomplish as of today starting from fully extended chains.

Figure 3. Binding free energy calculations correlate with experiments

Computed RBFEs [34] (A) and computed ABFEs [35] (B) compared to experimental values. While the RMS errors are still relatively large (around 2 kcal/mol), nevertheless, it shows that it is becoming possible to calculate approximate binding affinities. [39]