PyAutoFEP: An Automated Free Energy Perturbation Workflow for GROMACS Integrating Enhanced Sampling Methods

Free
energy perturbation (FEP) calculations are now routinely used
in drug discovery to estimate the relative FEB (RFEB) of small molecules
to a biomolecular target of interest. Using enhanced sampling can
improve the correlation between predictions and experimental data,
especially in systems with conformational changes. Due to the large
number of perturbations required in drug discovery campaigns, the
manual setup of FEP calculations is no longer viable. Here, we introduce
PyAutoFEP, a flexible and open-source tool to aid the setup of RFEB
FEP. PyAutoFEP is written in Python3, and automates the generation
of perturbation maps, dual topologies, system building and molecular
dynamics (MD), and analysis. PyAutoFEP supports multiple force fields,
incorporates replica exchange with solute tempering (REST) and replica
exchange with solute scaling (REST2) enhanced sampling methods, and
allows flexible λ values along perturbation windows. To validate
PyAutoFEP, it was applied to a set of 14 Farnesoid X receptor ligands,
a system included in the drug design data resource grand challenge
2. An 88% mean correct sign prediction was achieved, and 75% of the
predictions had an error below 1.5 kcal/mol. Results using Amber03/GAFF,
CHARMM36m/CGenFF, and OPLS-AA/M/LigParGen had Pearson’s r values of 0.71 ± 0.13, 0.30 ± 0.27, and 0.66
± 0.20, respectively. The Amber03/GAFF and OPLS-AA/M/LigParGen
results were on par with the top grand challenge 2 submissions. Applying
REST2 improved the results using CHARMM36m/CGenFF (Pearson’s r = 0.43 ± 0.21) but had little impact on the other
force fields. CHARMM36-YF and CHARMM36-WYF modifications did not yield
improved predictions compared to CHARMM36m. Finally, we estimated
the probability of finding a molecule 1 pKi better than a lead when using PyAutoFEP to screen
10 or 100 analogues. The probabilities, when compared to random sampling,
increased up to sevenfold when 100 molecules were to be screened,
suggesting that PyAutoFEP would likely be useful for lead optimization.
PyAutoFEP is available on GitHub at https://github.com/lmmpf/PyAutoFEP.