Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation

Abstract

The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to soluble globular proteins. Despite breakthroughs, experimental X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand–protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calculations are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water molecules, amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calculations following typical FEP setup and execution protocols delivered no correlation with experiment, but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coefficient of determination (R 2) compared with experiment in the range of 0.6–0.9 and mean unsigned errors compared to experiment of 0.6–0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.

Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease

“…For example, G protein coupled receptors (GPCRs) are a frequent target comprising a large market share 71 . A recent computational study of relative binding free energies for two GPCRs, adensosine 2A and orexin-2, has illustrated that alternative histidine tautomeric/protonation states impacts the overall stability of the bound ligand and thus contributes to the overall performance of relative free energy predictions 72 . Here we have enumerated these states and determined the effects of protonating various residues for M pro in both the apo form as well as ligand-bound complexes using MD simulations.…”

Best Practices for Alchemical Free Energy Calculations [Article v1.0]

“…An interesting cost benefit analysis has shown the value of activity prediction, see discussion above and articles such as [85]. From a drug discovery point of view, alchemical calculations are expanding their domain of applicability, and there are reports of success using homology models [99] and GPCRs [100,101] for instance, as well as enabling charge change and scaffold hopping [102,103], but these systems are undoubtedly more difficult. In the meantime, the use cases are expanding to resistance prediction, selectivity prediction , solubility prediction -an exciting future for alchemical calculations [6,104,105].…”

“…Other tools such as WaterMap or open source equivalents (SSTMap, GIST, and others) can be used to define water structure for systems with no experimental evidence of water sites [112]. Well known protein systems with water mediated ligand interactions are for example: HSP90 which formed part of the D3R grand challenge 2015 [16], A2A [113], MUP [114], [100], and others [115].…”

A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Prediction: New Opportunities and Challenges for Drug Discovery

“…With this available information, FEP can be used to optimize and validate binding pose, predict binding affinity of different ligands, interpret binding affinity cliff, and construct virtual SAR, etc. In recent years, many studies have been published from both academic and industrial communities to further optimize the FEP protocol and methodology for more efficient and accurate predictions 20-27, 32, 35-40, 42 , identify and extend the domain of applicability to more challenging target-ligand systems and scenarios 35,[43][44][45] , and build open toolkits to reduce the access barrier for FEP applications 26,[37][38][39]46 . Most of these studies focus on the use of RBFE for R-group substitution and core hopping in corresponding drug discovery scenarios of lead optimization and hit-to-lead stages.…”