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: (i) to fold small proteins with free-energy methods without knowing substructures, and (ii) 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.

The case for defined protein folding pathways

“…One has long looked forward to the time when computer simulations would illuminate the intimate details of protein folding processes. The effort has been obstructed by the lack of formal equations that can capture the delicate balance of multiple interlocking structural interactions and by the immense computer power needed to simulate protein folding in atomistic detail (72). Much of the theoretical folding literature describes efforts to overcome these limitations.…”

Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration

“…1-3 A broad range of methods exists for the prediction of protein–ligand binding affinities (henceforth referred to simply as ”binding affinities”) from molecular dynamics (MD) simulations, including fast empirical or semiempirical methods such as the linear interaction energy (LIE) model 4 and its variants, 5 the molecular mechanics (MM) combined with Poisson–Boltzmann or generalized Born solvation plus surface area correction (MM/PBSA and MM/GBSA), 6 and more rigorous and time-consuming alchemical free energy methods. 7-10 Currently, a wide range of alchemical free energy methods provides the most robust and accurate estimates of binding affinities from molecular dynamics simulations. 3,7,11-15 These methods include Thermodynamic Integration (TI) 11,16-18 and Free Energy Perturbation (FEP), 11,19-22 as well as analysis through Bennett’s acceptance ratio and its variants (BAR/MBAR) 23-28 and are augmented with enhanced sampling such as replica exchange molecular dynamics, 29 metadynamics, 30 driven adiabatic free energy dynamics, 31 orthogonal space random walk, 32 adaptive integration, 33 and other methods.…”

“…3,7,11-15 These methods include Thermodynamic Integration (TI) 11,16-18 and Free Energy Perturbation (FEP), 11,19-22 as well as analysis through Bennett’s acceptance ratio and its variants (BAR/MBAR) 23-28 and are augmented with enhanced sampling such as replica exchange molecular dynamics, 29 metadynamics, 30 driven adiabatic free energy dynamics, 31 orthogonal space random walk, 32 adaptive integration, 33 and other methods. 34,35 Advanced alchemical free energy methods for binding affinity prediction 9,10,36 have evolved to the point that they approach quantitative predictive accuracy for ligand-binding affinities, 27,37,38 although much work remains, such as addressing sampling and convergence issues, 27,37 to improve precision so as to afford meaningful comparison with experimental uncertainties. 39 …”

Docking optimization, variance and promiscuity for large-scale drug-like chemical space using high performance computing architectures

“…Note that there are clear cases in docking where water molecules should be included explicitly while implicit solvation models continue to improve[59][60][61]. The choices of which pH and which salt concentration to choose should be matched to the local environment of the selected protein.…”