Cytochrome P450 3A4 (CYP3A4) is the most abundant membrane-associated isoform of the P450 family in humans and is responsible for biotransformation of more than 50% of drugs metabolized in the body. Despite the large number of crystallographic structures available for CYP3A4, no structural information for its membrane-bound state at an atomic level is available. In order to characterize binding, depth of insertion, membrane orientation, and lipid interactions of CYP3A4, we have employed a combined experimental and simulation approach in this study. Taking advantage of a novel membrane representation, highly mobile membrane mimetic (HMMM), with enhanced lipid mobility and dynamics, we have been able to capture spontaneous binding and insertion of the globular domain of the enzyme into the membrane in multiple independent, unbiased simulations. Despite different initial orientations and positions of the protein in solution, all the simulations converged into the same membrane-bound configuration with regard to both the depth of membrane insertion and the orientation of the enzyme on the surface of the membrane. In tandem, linear dichroism measurements performed on CYP3A4 bound to Nanodisc membranes were used to characterize the orientation of the enzyme in its membrane-bound form experimentally. The heme tilt angles measured experimentally are in close agreement with those calculated for the membrane-bound structures resulted from the simulations, thereby verifying the validity of the developed model. Membrane binding of the globular domain in CYP3A4, which appears to be independent of the presence of the transmembrane helix of the full-length enzyme, significantly reshapes the protein at the membrane interface, causing conformational changes relevant to access tunnels leading to the active site of the enzyme.

The Escherichia coli uracil:proton symporter UraA is a prototypical member of the nucleobase/ascorbate transporter (NAT) or nucleobase/cation symporter 2 (NCS2) family, which corresponds to the human solute carrier family SLC23. UraA consists of 14 transmembrane segments (TMs) that are organized into two distinct domains, the core domain and the gate domain, a structural fold that is also shared by the SLC4 and SLC26 transporters. Here we present the crystal structure of UraA bound to uracil in an occluded state at 2.5 Å resolution. Structural comparison with the previously reported inward-open UraA reveals pronounced relative motions between the core domain and the gate domain as well as intra-domain rearrangement of the gate domain. The occluded UraA forms a dimer in the structure wherein the gate domains are sandwiched by two core domains. In vitro and in vivo biochemical characterizations show that UraA is at equilibrium between dimer and monomer in all tested detergent micelles, while dimer formation is necessary for the transport activity. Structural comparison between the dimeric UraA and the recently reported inward-facing dimeric UapA provides important insight into the transport mechanism of SLC23 transporters.

Using Nanodiscs, we quantitate the heterotropic interaction between two different drugs mediated by monomeric CYP3A4 incorporated into a native-like membrane environment. The mechanism of this interaction is deciphered by global analysis of multiple turnover experiments performed under identical conditions using the pure substrates progesterone (PGS) and carbamazepine (CBZ) and their mixtures. Activation of CBZ epoxidation and simultaneous inhibition of PGS hydroxylation are measured and quantitated through differences in their respective affinities towards both a remote allosteric site and the productive catalytic site near the heme iron. Preferred binding of PGS at the allosteric site and higher preference of CBZ binding at the productive site give rise to a non-trivial drug-drug interaction. Molecular dynamics simulations indicate functionally important conformational changes caused by PGS binding at the allosteric site and by two CBZ molecules positioned inside the substrate binding pocket. Structural changes involving Phe-213, Phe-219, and Phe-241 are suggested to be responsible for the observed synergetic effects and positive allosteric interactions between these two substrates. Such a mechanism is likely of general relevance to the mutual heterotropic effects caused by biologically active compounds which exhibit different patterns of interaction with the distinct allosteric and productive sites of CYP3A4, as well as other xenobiotic metabolizing cytochromes P450 that are also involved in drug-drug interactions. Importantly, this work demonstrates that a monomeric CYP3A4 can display the full spectrum of activation and cooperative effects that are observed in hepatic membranes.

Chemical synthesis planning is a key aspect in many fields of chemistry, especially drug discovery. Recent implementations of machine learning and artificial intelligence techniques for retrosynthetic analysis have shown great potential to improve computational methods for synthesis planning. Herein, we present a multiscale, data-driven approach for retrosynthetic analysis with deep highway networks (DHN). We automatically extracted reaction rules (i.e., ways in which a molecule is produced) from a data set consisting of chemical reactions derived from U.S. patents. We performed the retrosynthetic reaction prediction task in two steps: first, we built a DHN model to predict which group of reactions (consisting of chemically similar reaction rules) was employed to produce a molecule. Once a reaction group was identified, a DHN trained on the subset of reactions within the identified reaction group, was employed to predict the transformation rule used to produce a molecule. To validate our approach, we predicted the first retrosynthetic reaction step for 40 approved drugs using our multiscale model and compared its predictive performance with a conventional model trained on all machine-extracted reaction rules employed as a control. Our multiscale approach showed a success rate of 82.9% at generating valid reactants from retrosynthetic reaction predictions. Comparatively, the control model trained on all machine-extracted reaction rules yielded a success rate of 58.5% on the validation set of 40 pharmaceutical molecules, indicating a significant statistical improvement with our approach to match known first synthetic reaction of the tested drugs in this study. While our multiscale approach was unable to outperform state-of-the-art rule-based systems curated by expert chemists, multiscale classification represents a marked enhancement in retrosynthetic analysis and can be easily adapted for use in a range of artificial intelligence strategies.

Cytochrome P450 (CYP) 2J2 is the primary epoxygenase in the heart and is responsible for the epoxidation of arachidonic acid (AA), an ω-6 polyunsaturated fatty acid (PUFA), into anti-inflammatory epoxide metabolites. It also epoxidizes other PUFAs such as docosahexaenoic acid (DHA), linoleic acid (LA), and eicosapentaenoic acid (EPA). Herein, we have performed detailed thermodynamic and kinetic analyses to determine how DHA, LA and EPA modulate AA metabolism by CYP2J2. We use the Nanodisc (ND) system to stabilize CYP2J2 and its redox partner CYP reductase (CPR). We observe that DHA strongly inhibits CYP2J2-mediated AA metabolism, while LA only moderately inhibits and EPA exhibits insignificant inhibition. We also characterized the binding of these molecules using ebastine competitive binding assays and show that DHA binds significantly tighter to CYP2J2 as compared to AA, EPA, or LA. Furthermore, we utilize a combined approach of molecular dynamics (MD) simulations and docking to predict key residues mediating the tight binding of DHA. We show that although all the tested fatty acids form similar contacts to the active site residues, the affinity of DHA binding to CYP2J2 is tighter due to the interaction of DHA with residues Arg-321, Thr-318 and Ser-493. To demonstrate the importance of these residues in binding, we mutated these residues to make two mutant variants—CYP2J2-T318A and CYP2J2-T318V/S493A. Both of these variants showed weaker binding affinity to DHA and AA compared to the WT and the stronger inhibition of AA by DHA in the WT is mitigated in these mutants. Therefore, using a combined experimental and MD simulations approach, we establish that CYP2J2 inhibition of AA metabolism by DHA, EPA and LA is asymmetric due to tighter binding of DHA to select residues in the active site.