Open Source de novo Design and Free Energy Calculation Workflows
University of Newcastle
Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses and selecting reliable force fields for the ligands is challenging.
Here, I introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations . For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. I will illustrate its use by building a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and using free energy calculations to retrospectively compute their relative binding free energies.
Furthermore, the predictive utility of the resulting free energy calculations depends critically on the accuracy of the employed force fields. Here, I will introduce the Open Force Field QCSubmit and BespokeFit software packages  that, when combined, facilitate the fitting of torsion parameters to quantum mechanical reference data at scale. We demonstrate the use of QCSubmit for simplifying the process of creating and archiving large numbers of quantum chemical calculations, by generating a dataset of 671 torsion scans for drug-like fragments. When employed to compute the relative binding free energies of a congeneric series of inhibitors of the TYK2 protein, the resulting bespoke force fields demonstrate significant improvements in accuracy compared to the base force field (correlation with experiment improved from 0.72 to 0.93).
 Horton JT, Boothroyd S, Wagner J, Mitchell JA, Gokey T, Dotson DL, Behara PK, Ramaswamy VK, Mackey M, Chodera JD, Anwar J, Mobley DL, Cole DJ, Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization At Scale. Journal of Chemical Information and Modeling, 2022, 62, 5622-5633.