Abstract:We describe here some tests we made in the SAMPL5 communal event of `Semi-Explicit Assembly' (SEA), a recent method for computing solvation free energies. We combined the prospective tests of SAMPL5 with followup retrospective calculations, to improve two technical aspects of the field variant of SEA. First, SEA uses an approximate analytical surface around the solute on which a water potential is computed. We have improved and simplified the mathematical model of that surface. Second, some of the solutes in S… Show more
“…Many of these predictions used alchemical molecular dynamics simulations to estimate the solvation free energy in explicit solvent using several classes of force fields, including fixed-charge all-atom force fields [49–53], all-atom/coarse-grained hybrid force fields [54], and polarizable force fields [55]. One participant used Semi-Explicit Assembly, a type of implicit solvent solvation free energy method applied to one or more chosen solute conformations [56]. A variety of quantum mechanics (QM) methods were also used including QM/molecular mechanics (QM/MM) with explicit solvent [52, 53], QM with non-Boltzmann Bennett free energy calculations [52, 53], and QM energy calculations with a single optimized molecular geometry [52, 57].…”
In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty – how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided.
“…Many of these predictions used alchemical molecular dynamics simulations to estimate the solvation free energy in explicit solvent using several classes of force fields, including fixed-charge all-atom force fields [49–53], all-atom/coarse-grained hybrid force fields [54], and polarizable force fields [55]. One participant used Semi-Explicit Assembly, a type of implicit solvent solvation free energy method applied to one or more chosen solute conformations [56]. A variety of quantum mechanics (QM) methods were also used including QM/molecular mechanics (QM/MM) with explicit solvent [52, 53], QM with non-Boltzmann Bennett free energy calculations [52, 53], and QM energy calculations with a single optimized molecular geometry [52, 57].…”
In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty – how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided.
“…With the development of the field version of the technique and its dynamic solvent accessible surface boundary ( Figure 28 ), SEA can also be applied to solvents beyond pure water by simply updating the surface accessibility and solvation coefficient tables for solvated LJ particles in the new environments. 412 , 415 , 416 Figure 28 The water-accessible surface depends on the charge on the solute (gray). (left) Water-accessible surface (green) around an uncharged solute.…”
Section: Modeling Large Complex
Solutes Requires Approximations
and Ementioning
confidence: 99%
“…SEA is as fast as implicit-solvent models, because of its free-energy additivities in the assembly step. SEA has been found to be as accurate as explicit-solvent models in blind tests, called SAMPL; − see sec . While SEA is only as accurate as its underlying explicit-solvent model, nevertheless its accuracy stems, in part, from its intrinsic capture of water–water multibody effects in the presimulations.…”
Section: Modeling Large Complex
Solutes Requires Approximations
and E...mentioning
confidence: 99%
“…While SEA is only as accurate as its underlying explicit-solvent model, nevertheless its accuracy stems, in part, from its intrinsic capture of water–water multibody effects in the presimulations. With the development of the field version of the technique and its dynamic solvent accessible surface boundary (Figure ), SEA can also be applied to solvents beyond pure water by simply updating the surface accessibility and solvation coefficient tables for solvated LJ particles in the new environments. ,, 28 The water-accessible surface depends on the charge on the solute (gray). (left) Water-accessible surface (green) around an uncharged solute.…”
Section: Modeling Large Complex
Solutes Requires Approximations
and E...mentioning
How are water’s
material properties encoded within the structure
of the water molecule? This is pertinent to understanding Earth’s
living systems, its materials, its geochemistry and geophysics, and
a broad spectrum of its industrial chemistry. Water has distinctive
liquid and solid properties: It is highly cohesive. It has volumetric
anomalies—water’s solid (ice) floats on its liquid;
pressure can melt the solid rather than freezing the liquid; heating
can shrink the liquid. It has more solid phases than other materials.
Its supercooled liquid has divergent thermodynamic response functions.
Its glassy state is neither fragile nor strong. Its component ions—hydroxide
and protons—diffuse much faster than other ions. Aqueous solvation
of ions or oils entails large entropies and heat capacities. We review
how these properties are encoded within water’s molecular structure
and energies, as understood from theories, simulations, and experiments.
Like simpler liquids, water molecules are nearly spherical and interact
with each other through van der Waals forces. Unlike simpler liquids,
water’s orientation-dependent hydrogen bonding leads to open
tetrahedral cage-like structuring that contributes to its remarkable
volumetric and thermal properties.
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