Accurate Binding Free Energy Predictions in Fragment Optimization

• Published in: Journal of chemical information and modeling 2015-11, Vol.55 (11), p.2411-2420
• Main Authors: Steinbrecher, Thomas B, Dahlgren, Markus, Cappel, Daniel, Lin, Teng, Wang, Lingle, Krilov, Goran, Abel, Robert, Friesner, Richard, Sherman, Woody
• Format: Article
• Language: English
• Subjects: Animals; Bacterial Proteins - metabolism; Binding sites; Chemistry; Drug Design; Humans; Ligands; Mice; Molecular Dynamics Simulation; Protein Binding; Proteins; Proteins - metabolism; Staphylococcus aureus - metabolism; Thermodynamics
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.5b00538

Predicting protein–ligand binding free energies is a central aim of computational structure-based drug design (SBDD)  improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R 2-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.