An Open-Source Molecular Builder and Free Energy Preparation Workflow

23 May 2022, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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 for the ligands is challenging. Here, we 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. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein–ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein–ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole- group/FEgrow, along with a tutorial.

Keywords

De novo design
Binding free energy
Protein-ligand structure
R-group
Machine learning
Molecular modelling
Software

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Supporting Information for: An Open-Source Molecular Builder and Free Energy Preparation Workflow
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