The accurate calculation of the binding free energy for arbitrary ligand–protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space. In the current work we demonstrate that accurate absolute binding free energies (ABFE) can also be obtained via theoretically rigorous non-equilibrium approaches. Our investigation of ligands binding to bromodomains and T4 lysozyme reveals that both equilibrium and non-equilibrium approaches converge to the same results. The non-equilibrium approach achieves the same level of accuracy and convergence as an equilibrium free energy perturbation (FEP) method enhanced by Hamiltonian replica exchange. We also compare uni- and bi-directional non-equilibrium approaches and demonstrate that considering the work distributions from both forward and reverse directions provides substantial accuracy gains. In summary, non-equilibrium ABFE calculations are shown to yield reliable and well-converged estimates of protein–ligand binding affinity.

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains...

Many-body Green’s functions theory within the GW approximation and the Bethe-Salpeter Equation (BSE) is implemented in the open-source VOTCA-XTP software, aiming at the calculation of electronically excited states in complex molecular environments. Based on Gaussian-type atomic orbitals and making use of resolution of identity techniques, the code is designed specifically for nonperiodic systems. Application to a small molecule reference set successfully validates the methodology and its implementation for a variety of excitation types covering an energy range from 2 to 8 eV in single molecules. Further, embedding each GW-BSE calculation into an atomistically resolved surrounding, typically obtained from Molecular Dynamics, accounts for effects originating from local fields and polarization. Using aqueous DNA as a prototypical system, different levels of electrostatic coupling between the regions in this GW-BSE/MM setup are demonstrated. Particular attention is paid to charge-transfer (CT) excitations in adenine base pairs. It is found that their energy is extremely sensitive to the specific environment and to polarization effects. The calculated redshift of the CT excitation energy compared to a nucelobase dimer treated in vacuum is of the order of 1 eV, which matches expectations from experimental data. Predicted lowest CT energies are below that of a single nucleobase excitation, indicating the possibility of an initial (fast) decay of such an UV excited state into a binucleobase CT exciton. The results show that VOTCA-XTP’s GW-BSE/MM is a powerful tool to study a wide range of types of electronic excitations in complex molecular environments.

Drug discovery can be thought of as a search for a needle in a haystack: searching through a large chemical space for the most active compounds. Computational techniques can narrow the search space for experimental follow up, but even they become unaffordable when evaluating large numbers of molecules. Therefore, machine learning (ML) strategies are being developed as computationally cheaper complementary techniques for navigating and triaging large chemical libraries. Here, we explore how an active learning protocol can be combined with first-principles based alchemical free energy calculations to identify high affinity phosphodiesterase 2 (PDE2) inhibitors. We first calibrate the procedure using a set of experimentally characterized PDE2 binders. The optimized protocol is then used prospectively on a large chemical library to navigate toward potent inhibitors. In the active learning cycle, at every iteration a small fraction of compounds is probed by alchemical calculations and the obtained affinities are used to train ML models. With successive rounds, high affinity binders are identified by explicitly evaluating only a small subset of compounds in a large chemical library, thus providing an efficient protocol that robustly identifies a large fraction of true positives.

Mandelate racemase (EC 5.1.2.2) from Pseudomonas putida catalyzes the interconversion of the enantiomers of mandelic acid and a variety of aryl- and heteroaryl-substituted mandelate derivatives, suggesting that β,γ-unsaturation is a requisite feature of substrates for the enzyme. We show that β,γ-unsaturation is not an absolute requirement for catalysis and that mandelate racemase can bind and catalyze the racemization of (S)-trifluorolactate (k(cat) = 2.5 ± 0.3 s(-1), K(m) = 1.74 ± 0.08 mM) and (R)-trifluorolactate (k(cat) = 2.0 ± 0.2 s(-1), K(m) = 1.2 ± 0.2 mM). The enzyme was shown to catalyze hydrogen-deuterium exchange at the α-postion of trifluorolactate using (1)H NMR spectrocsopy. β-Elimination of fluoride was not detected using (19)F NMR spectroscopy. Although mandelate racemase bound trifluorolactate with an affinity similar to that exhibited for mandelate, the turnover numbers (k(cat)) were markedly reduced by ∼318-fold, resulting in catalytic efficiencies (k(cat)/K(m)) that were ~400-fold lower than those observed for mandelate. These observations suggested that chemical steps on the enzyme were likely rate-determining, which was confirmed by demonstrating that the rates of mandelate racemase-catalyzed racemization of (S)-trifluorolactate were not dependent upon the solvent microviscosity. Circular dichroism spectroscopy was used to measure the rates of nonenzymatic racemization of (S)-trifluorolactate at elevated temperatures. The values of ΔH(‡) and ΔS(‡) for the nonenzymatic racemization reaction were determined to be 28.0 (±0.7) kcal/mol and -15.7 (±1.7) cal K(-1) mol(-1), respectively, corresponding to a free energy of activation equal to 33 (±4) kcal/mol at 25 °C. Hence, mandelate racemase stabilizes the altered trifluorolactate in the transition state (ΔG(tx)) by at least 20 kcal/mol.