Advancing Free-Energy Calculations of Metalloenzymes in Drug Discovery via Implementation of LFMM Potentials

Abstract

To address some of the inherent challenges in modeling metalloenzymes, we here report an extension to the functional form of the OPLS3e force field to include terms adopted from the ligand field molecular mechanics (LFMM) model, including the angular overlap and Morse potential terms. The integration of these terms with OPLS3e, herein referred to as OPLS3e+M, improves the description of metal–ligand interactions and provides accurate relative binding energies and geometric preferences of transition-metal complexes by training to gas-phase density functional theory (DFT) energies. For [Cu­(H2O)4]2+, OPLS3e+M significantly improves H2O binding energies and the geometric preference of the tetra-aqua Cu2+ complex. In addition, we conduct free-energy perturbation calculations on two pharmaceutically relevant metalloenzyme targets, which include chemical modifications at varying proximity to the binding-site metals, including changes to the metal-binding moiety of the ligand itself. The extensions made to OPLS3e lead to accurate predicted relative binding free energies for these series (mean unsigned error of 1.29 kcal mol–1). Our results provide evidence that integration of the LFMM model with OPLS3e can be utilized to predict thermodynamic quantities for such systems near chemical accuracy. With these improvements, we anticipate that robust free-energy perturbation calculations can be employed to accelerate the drug development efforts for metalloenzyme targets.

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

“…However, the number of possible RNA targets far exceeds that of druggable protein targets. Multiple studies have shown that RNA is a suitable small-molecule drug target. − However, FEP prediction targeting RNA faces several major challenges including the limited accuracy of RNA and related metal-ion FFs, − the issue of proper sampling of flexible RNA structures, and the complications arising in a highly charged simulation environment as that of RNA and counterions. Addressing these issues would open up an entirely new avenue of FEP applications for drug discovery and is expected to be a subject of interest for the whole FEP research community in the near future. How to interpret FEP prediction results: From retrospective validation of FEP predictions, we know that it is unavoidable to have false negative and false positive results in most cases.…”

OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space

“…For example, the application of FEP to predict changes in binding affinity proximate to bound transition metals remains one of the more challenging problems in force field modeling. Improvements to the force field targeting this context are under active development in this group, but significant development remains before a streamlined and robust solution is available. The present ion vdW work also makes clear the limitations of pairwise additive potentials.…”

“…This success has motivated new developments aimed at extending the reach of these free energy methods to cover more ambitious problems in structure-based molecular design, including proper handling of buried water molecules, variable tautomer and ionization states, covalently bound ligands, , and transition metal coordination . These advances can introduce greater sensitivity to chemical space regimes that present outstanding challenges to the fidelity of the model.…”

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

“…Using LFMM, Paesani and co-workers performed molecular dynamics and Monte Carlo simulations to observe changes in the spin-crossover transition of a Fe­(II) SCO MOF in the presence of additional water molecules in the unit cell that alter the ligand field environment around the Fe­(II) metal (Figure ). Other recent extensions of LFMM have been developed for metalloenzyme modeling …”