Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein–Ligand Docking

Accurate and rapid
calculation of protein-small molecule interaction
free energies is critical for computational drug discovery. Because
of the large chemical space spanned by drug-like molecules, classical
force fields contain thousands of parameters describing atom-pair
distance and torsional preferences; each parameter is typically optimized
independently on simple representative molecules. Here, we describe
a new approach in which small molecule force field parameters are
jointly optimized guided by the rich source of information contained
within thousands of available small molecule crystal structures. We
optimize parameters by requiring that the experimentally determined
molecular lattice arrangements have lower energy than all alternative
lattice arrangements. Thousands of independent crystal lattice-prediction
simulations were run on each of 1386 small molecule crystal structures,
and energy function parameters of an implicit solvent energy model
were optimized, so native crystal lattice arrangements had the lowest
energy. The resulting energy model was implemented in Rosetta, together
with a rapid genetic algorithm docking method employing grid-based
scoring and receptor flexibility. The success rate of bound structure
recapitulation in cross-docking on 1112 complexes was improved by
more than 10% over previously published methods, with solutions within
<1 Å in over half of the cases. Our results demonstrate that
small molecule crystal structures are a rich source of information
for guiding molecular force field development, and the improved Rosetta
energy function should increase accuracy in a wide range of small
molecule structure prediction and design studies.