A Method for Treating Significant Conformational Changes in Alchemical Free Energy Simulations of Protein-Ligand Binding.

Relative binding free energy (RBFE) simulation is a rigorous approach to the calculation of quantitatively accurate binding free energy values for protein-ligand binding in which a reference binder is gradually converted to a target binder through alchemical transformation during the simulation. The success of such simulations relies on being able to accurately sample the correct conformational phase space for each alchemical state; however, this becomes a challenge when a significant conformation change occurs between the reference and target binder-receptor complexes. Increasing the simulation time and using enhanced sampling methods can be helpful, but effects can be limited, especially when the free energy barrier between conformations is high or when the correct target complex conformation is difficult to find and maintain.

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.

Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published.

The maximal and current accuracy of rigorous protein-ligand binding free energy calculations.

Computational techniques can speed up the identification of hits and accelerate the development of candidate molecules for drug discovery. Among techniques for predicting relative binding affinities, the most consistently accurate is free energy perturbation (FEP), a class of rigorous physics-based methods. However, uncertainty remains about how accurate FEP is and can ever be.

Reliable and Accurate Prediction of Single-Residue p Values through Free Energy Perturbation Calculations.

Accurate prediction of the p's of protein residues is crucial to many applications in biological simulation and drug discovery. Here, we present the use of free energy perturbation (FEP) calculations for the prediction of single-protein residue p values. We begin with an initial set of 191 residues with experimentally determined p values.

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions.

Transferable high dimensional neural network potentials (HDNNPs) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previously reported such a potential (Schrödinger-ANI) that has broad coverage of druglike molecules. We extend that work here to cover ionic and zwitterionic druglike molecules expected to be relevant to drug discovery research activities.

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

We report on the development and validation of the OPLS4 force field. OPLS4 builds upon our previous work with OPLS3e to improve model accuracy on challenging regimes of drug-like chemical space that includes molecular ions and sulfur-containing moieties. A novel parametrization strategy for charged species, which can be extended to other systems, is introduced.

Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo.

The prediction of protein-ligand binding affinities using free energy perturbation (FEP) is becoming increasingly routine in structure-based drug discovery. Most FEP packages use molecular dynamics (MD) to sample the configurations of proteins and ligands, as MD is well-suited to capturing coupled motion. However, MD can be prohibitively inefficient at sampling water molecules that are buried within binding sites, which has severely limited the domain of applicability of FEP and its prospective usage in drug discovery.

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

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(HO)], OPLS3e+M significantly improves HO binding energies and the geometric preference of the tetra-aqua Cu complex.

OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules.

Building upon the OPLS3 force field we report on an enhanced model, OPLS3e, that further extends its coverage of medicinally relevant chemical space by addressing limitations in chemotype transferability. OPLS3e accomplishes this by incorporating new parameter types that recognize moieties with greater chemical specificity and integrating an on-the-fly parametrization approach to the assignment of partial charges. As a consequence, OPLS3e leads to greater accuracy against performance benchmarks that assess small molecule conformational propensities, solvation, and protein-ligand binding.

Advancing Drug Discovery through Enhanced Free Energy Calculations.

A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery.