Variational Optimization of an All-Atom Implicit Solvent Force Field To Match Explicit Solvent Simulation Data

Abstract

The development of accurate implicit solvation models with low computational cost is essential for addressing many large-scale biophysical problems. Here, we present an efficient solvation term based on a Gaussian solvent-exclusion model (EEF1) for simulations of proteins in aqueous environment, with the primary aim of having a good overlap with explicit solvent simulations, particularly for unfolded and disordered states – as would be needed for multiscale applications. In order to achieve this, we have used a recently proposed coarse-graining procedure based on minimization of an entropy-related objective function to train the model to reproduce the equilibrium distribution obtained from explicit water simulations. Via this methodology, we have optimized both a charge screening parameter and a backbone torsion term against explicit solvent simulations of an α-helical and a β-stranded peptide. The performance of the resulting effective energy function, termed EEF1-SB, is tested with respect to the properties of folded proteins, the folding of small peptides or fast-folding proteins, and NMR data for intrinsically disordered proteins. The results show that EEF1-SB provides a reasonable description of a wide range of systems, but its key advantage over other methods tested is that it captures very well the structure and dimension of disordered or weakly structured peptides. EEF1-SB is thus a computationally inexpensive (~ 10 times faster than Generalized-Born methods) and transferable approximation for treating solvent effects.

Free energies of solvation in the context of protein folding: Implications for implicit and explicit solvent models

“…As can be seen in Supporting Information Figure S19, the changes were generally insignificant, except for several cases in which EEF1 does significantly worse. In the same figure, we included calculations for a reparameterization of EEF1 for CHARMM36 carried out by Bottaro et al Considering they used an approach that allows the parameters to be optimized by matching the entire ensembles of two small structured peptides, it is somewhat surprising that the new parameter set results in little change to the findings. Also related, the previously mentioned optimization of the radii with GBSW was carried out alongside optimization of the CMAP parameters, so we reran the calculations with the CHARMM22/CMAP forcefield and these updated parameters (Supporting Information Fig.…”

Intrinsically Disordered Proteins: Where Computation Meets Experiment

“…First, most parameterized force fields were developed for folded proteins, and it is an open question as to whether all of the available energy functions are generally applicable to IDPs. While some more specific force fields have been developed with IDPs in mind (and fruitfully applied), it is not clear how generally applicable these methods are [78][79][80][81][82]. More importantly, the conformational heterogeneity of IDPs calls for extensive simulations to ensure that the relevant regions of conformational space have been adequately sampled.…”

Protein structure refinement using a quantum mechanics-based chemical shielding predictor

“…The physical force field CHARMM36, with the EEF1-SB solvent model, were used in the simulations. 30 The simulated annealing protocol consisted of simply lowering the temperature from t start = 300 K to t end = 3 K over N steps = 10 M steps, with the temperature at step i being . In the constant temperature (300 K) MCMC simulations, the resulting set of structures do not represent a thermodynamic ensemble because a hybrid energy function is used.…”