Abstract
Free energy calculation has long been an important goal for molecular dynamics simulation and force field development, but historically it has been challenged by limited performance, accuracy, and creation of topologies for arbitrary small molecules. This has made it difficult to systematically compare different sets of parameters to improve existing force fields, but in the past few years several authors have developed increasingly automated procedures to generate parameters for force fields such as Amber, CHARMM, and OPLS. Here, we present a new framework that enables fully automated generation of GROMACS topologies for any of these force fields and an automated setup for parallel adaptive optimization of high-throughput free energy calculation by adjusting lambda point placement on the fly. As a small example of this automated pipeline, we have calculated solvation free energies of 50 different small molecules using the GAFF, OPLS-AA, and CGenFF force fields and four different water models, and by including the often neglected polarization costs, we show that the common charge models are somewhat underpolarized.
Highlights
Free energy is of paramount importance in chemistry
The calculated results from CGenFF had a root-mean-square error (RMSE) of 7.94 kJ/mol compared to the experimental data, whereas GAFF had an RMSE of 5.95 kJ/mol and OPLS-AA had 8.92
STaGE can be used to generate GROMACS topologies for multiple force fields using common molecular file formats as input. It can generate partial charges using a number of different charge models and provides basic functionality for scaling or adjusting force field parameters, if required
Summary
Free energy is of paramount importance in chemistry. Almost all the experimental properties traditionally interpreted e.g. in terms of concentration, reaction rates, stability, folding, complex formation, binding catalysis, or solubility can well be described with free energy concepts, in particular on the molecular level. While the most complex systems are still limited by computational performance, the calculation of solvation free energies (i.e., the change in Gibbs free energy upon transfer from gas phase to solvent) has matured rapidly. It is already used in pharmaceutical applications since only a small fraction of commercially available compounds have had their solvation free energy determined experimentally.[1,2] This makes computational predictions tractable, if they are proven to be reliable, and likely to pave the way to more complex applications. There have been a number of blind challenges to predict hydration free energies of provided compounds, with experimental data that is difficult to find, in order to assess the state of the art and to improve current methodology.[1,2,4,5]
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