Protein–Ligand Binding Free Energy Calculations with FEP+

Accurate and reliable calculation of protein–ligand binding free energy is of central importance in computational biophysics and structure-based drug design. Among the various methods to calculate protein–ligand binding affinities, alchemical free energy perturbation (FEP) calculations performed by way of explicitly solvated molecular dynamics simulations (FEP/MD) provide a thermodynamically rigorous and complete description of the binding event and should in turn yield highly accurate predictions. Although the original theory of FEP was proposed more than 60 years ago, subsequent applications of FEP to compute protein–ligand binding free energies in the context of drug discovery projects over much of that time period was sporadic and generally unsuccessful. This was mainly due to the limited accuracy of the available force fields, inadequate sampling of the protein–ligand conformational space, complexity of simulation set up and analysis, and the large computational resources required to pursue such calculations. Over the past few years, there have been advances in computing power, classical force field accuracy, enhanced sampling algorithms, and simulation setup. This has led to newer FEP implementations such as the FEP+ technology developed by Schrödinger Inc., which has enabled accurate and reliable calculations of protein–ligand binding free energies and positioned free energy calculations to play a guiding role in small-molecule drug discovery. In this chapter, we outline the methodological advances in FEP+, including the OPLS3 force fields, the REST2 (Replica Exchange with Solute Tempering) enhanced sampling, the incorporation of REST2 sampling with conventional FEP (Free Energy Perturbation) through FEP/REST, and the advanced simulation setup and data analysis. The validation of FEP+ method in retrospective studies and the prospective applications in drug discovery projects are also discussed. We then present the recent extension of FEP+ method to handle challenging perturbations, including core-hopping transformations, macrocycle modifications, and reversible covalent inhibitor optimization. The limitations and pitfalls of the current FEP+ methodology and the best practices in real applications are also examined.