Modeling aqueous solvation with semi-explicit assembly

Abstract

We describe a computational solvation model called semi-explicit assembly (SEA). SEA water captures much of the physics of explicit-solvent models but with computational speeds approaching those of implicit-solvent models. We use an explicit-water model to precompute properties of water solvation shells around simple spheres, then assemble a solute's solvation shell by combining the shells of these spheres. SEA improves upon implicit-solvent models of solvation free energies by accounting for local solute curvature, accounting for near-neighbor nonadditivities, and treating water's dipole as being asymmetrical with respect to positive or negative solute charges. SEA does not involve parameter fitting, because parameters come from the given underlying explicit-solvation model. SEA is about as accurate as explicit simulations as shown by comparisons against four different homologous alkyl series, a set of 504 varied solutes, solutes taken retrospectively from two solvation-prediction events, and a hypothetical polar-solute series, and SEA is about 100-fold faster than Poisson-Boltzmann calculations.

Fig. 1.

The precomputation step. (A) Simulate the waters around sphere. (B) Superimpose all the first-shell waters onto a common axis relative to the solute–sphere center. (C) Create solvent atomic density maps. (D) Convert the density maps into dipole representations.

Fig. 2.

An illustration of SEA sampling process around p-methoxyaniline. Semi-explicit dipoles are placed along the solvent-accessible dot surface according to the local electric field within a continuum dielectric cavity.

Fig. 3.

Functional group comparisons for the linear alkyl series of (A) acetates, (B) phenyls, (C) alcohols, and (D) amines. Experimental results come from ref.  and the TIP3P results from ref. . The GB results are from Amber 10 using iGB5 and γA with γ = 5 cal mol^-1 Å^-2 (13).

Fig. 4.

Scatter plots of (A) GB (13), (B) PB (53), and (C) SEA air–water ΔG transfer values against TIP3P calculations (10). If the single largest outlier (triacetylglycerol) in the GB and PB calculations is removed, the RMSEs decrease to 2.78 and 1.77 kcal/mol, respectively.

Fig. 5.

The average execution time per solute vs. the RMSE to experiment for calculations on the 504 small-molecule solute set. The vertical axis is a logarithmic scale. The light-gray region indicates the region of performance faster than GB and more accurate than TIP3P, a target realm for future solvent models.

Fig. 6.

The calculated ΔG from SEA vs. experimental values for the (A) 56 blind prediction molecules of the SAMPL1 event and the (B) 23 blind prediction molecules in the SAMPL2 event.

Fig. 7.

The solvation asymmetry (ΔΔG) upon partial charge inversion for model polygons studied in ref. . In this case, one atom of each polygon has a formal charge, whereas the neutralizing counter charge is distributed evenly among the remaining atoms, as shown in the six-sided bracelet illustration on the left.