Accurate prediction of binding affinities has been a central goal of computational chemistry for decades, yet remains elusive. Despite good progress, the required accuracy for use in a drug-discovery context has not been consistently achieved for drug-like molecules. Here, we perform absolute free energy calculations based on a thermodynamic cycle for a set of diverse inhibitors binding to bromodomain-containing protein 4 (BRD4) and demonstrate that a mean absolute error of 0.6 kcal mol⁻¹ can be achieved. We also show a similar level of accuracy (1.0 kcal mol⁻¹) can be achieved in pseudo prospective approach. Bromodomains are epigenetic mark readers that recognize acetylation motifs and regulate gene transcription, and are currently being investigated as therapeutic targets for cancer and inflammation. The unprecedented accuracy offers the exciting prospect that the binding free energy of drug-like compounds can be predicted for pharmacologically relevant targets.

Cysteine plays an essential role in cellular redox homoeostasis as a key constituent of the tripeptide glutathione (GSH). A rate limiting step in cellular GSH synthesis is the availability of cysteine. However, circulating cysteine exists in the blood as the oxidised di-peptide cystine, requiring specialised transport systems for its import into the cell. System xc⁻ is a dedicated cystine transporter, importing cystine in exchange for intracellular glutamate. To counteract elevated levels of reactive oxygen species in cancerous cells system xc⁻ is frequently upregulated, making it an attractive target for anticancer therapies. However, the molecular basis for ligand recognition remains elusive, hampering efforts to specifically target this transport system. Here we present the cryo-EM structure of system xc⁻ in both the apo and glutamate bound states. Structural comparisons reveal an allosteric mechanism for ligand discrimination, supported by molecular dynamics and cell-based assays, establishing a mechanism for cystine transport in human cells.

Ionotropic GABA receptors are abundant in both vertebrate and invertebrate nervous systems, where they mediate rapid, mostly inhibitory synaptic transmission. A GABA-gated chloride channel subunit from Drosophila melanogaster [Resistant to Dieldrin (RDL)] has been cloned, functionally expressed, and found to exhibit many aspects of the pharmacology of native, bicuculline-insensitive insect GABA receptors. RDL is the target of the commercially important insecticide fipronil. A point mutation in the channel-lining region of the RDL molecule is known to underlie most cases of resistance to insecticides acting on GABA receptors. RDL is widely distributed throughout the insect nervous system, but the subunit composition of RDL-containing in native receptors is unknown. It is possible that in some instances, RDL coexpresses with glutamate-gated chloride channel subunits. Other ionotropic receptor subunits (LCCH3 and GRD) form GABA-gated cation channels when heterologously expressed. Interest in RDL as a model ligandgated anion channel has been enhanced by the recent discovery of pre-mRNA A-to-I editing, which, together with alternative splicing, adds to the functional diversity of this GABA receptor subunit.

Binding selectivity is a requirement for the development of a safe drug, and it is a critical property for chemical probes used in preclinical target validation. Engineering selectivity adds considerable complexity to the rational design of new drugs, as it involves the optimization of multiple binding affinities. Computationally, the prediction of binding selectivity is a challenge, and generally applicable methodologies are still not available to the computational and medicinal chemistry communities. Absolute binding free energy calculations based on alchemical pathways provide a rigorous framework for affinity predictions and could thus offer a general approach to the problem. We evaluated the performance of free energy calculations based on molecular dynamics for the prediction of selectivity by estimating the affinity profile of three bromodomain inhibitors across multiple bromodomain families, and by comparing the results to isothermal titration calorimetry data. Two case studies were considered. In the first one, the affinities of two similar ligands for seven bromodomains were calculated and returned excellent agreement with experiment (mean unsigned error of 0.81 kcal/mol and Pearson correlation of 0.75). In this test case, we also show how the preferred binding orientation of a ligand for different proteins can be estimated via free energy calculations. In the second case, the affinities of a broad-spectrum inhibitor for 22 bromodomains were calculated and returned a more modest accuracy (mean unsigned error of 1.76 kcal/mol and Pearson correlation of 0.48); however, the reparametrization of a sulfonamide moiety improved the agreement with experiment.