Concentration dependence of in vitro biotransformation rates of hydrophobic organic sunscreen agents in rainbow trout S9 fractions: Implications for bioaccumulation assessment.

In vitro biotransformation studies were performed to support the bioaccumulation assessment of 3 hydrophobic organic ultraviolet filters (UVFs), 4-methylbenzylidene camphor (4-MBC), 2-ethylhexyl-4-methoxycinnamate (EHMC), and octocrylene. In vitro depletion rate constants (kdep ) were determined for each UVF using rainbow trout liver S9 fractions. Incubations performed with and without added cofactors showed complete (4-MBC) or partial (EHMC and octocrylene) dependence of kdep on addition of the reduced form of nicotinamide adenine dinucleotide phosphate (NADPH), suggesting that hydrolysis of EHMC and octocrylene by NADPH-independent enzymes (e.g., carboxylesterases) is an important metabolic route. The concentration dependence of kdep was then evaluated to estimate Michaelis-Menten parameters (KM and Vmax ) for each UVF. Measured kdep values were then extrapolated to apparent whole-body biotransformation rate constants using an in vitro-in vivo extrapolation (IVIVE) model. Bioconcentration factors (BCFs) calculated from kdep values measured at concentrations well below KM were closer to empirical BCFs than those calculated from kdep measured at higher test concentrations. Modeled BCFs were sensitive to in vitro binding assumptions employed in the IVIVE model, highlighting the need for further characterization of chemical binding effects on hepatic clearance. The results suggest that the tested UVFs are unlikely to accumulate to levels exceeding the European Union Registration, Evaluation, Authorisation, and Restriction regulation criterion for bioaccumulative substances (BCF > 2000 L kg-1 ). However, consideration of appropriate in vitro test concentrations and binding correction factors are important when IVIVE methods are used to refine modeled BCFs. Environ Toxicol Chem 2019;38:548-560. © 2018 SETAC.

Mapping the Substrate Recognition Pathway in Cytochrome P450.

Cytochrome P450s are ubiquitous metalloenzymes involved in the metabolism and detoxification of foreign components via catalysis of the hydroxylation reactions of a vast array of organic substrates. However, the mechanism underlying the pharmaceutically critical process of substrate access to the catalytic center of cytochrome P450 is a long-standing puzzle, further complicated by the crystallographic evidence of a closed catalytic center in both substrate-free and substrate-bound cytochrome P450. Here, we address a crucial question whether the conformational heterogeneity prevalent in cytochrome P450 translates to heterogeneous pathways for substrate access to the catalytic center of these metalloenzymes. By atomistically capturing the full process of spontaneous substrate association from bulk solvent to the occluded catalytic center of an archetypal system P450cam in multi-microsecond-long continuous unbiased molecular dynamics simulations, we here demonstrate that the substrate recognition in P450cam always occurs through a single well-defined dominant pathway. The simulated final bound pose resulting from these unguided simulations is in striking resemblance with the crystallographic bound pose. Each individual binding trajectory reveals that the substrate, initially placed at random locations in bulk solvent, spontaneously lands on a single key channel on the protein-surface of P450cam and resides there for an uncharacteristically long period, before correctly identifying the occluded target-binding cavity. Surprisingly, the passage of substrate to the closed catalytic center is not accompanied by any large-scale opening in protein. Rather, the unbiased simulated trajectories (∼57 µs) and underlying Markov state model, in combination with free-energy analysis, unequivocally show that the substrate recognition process in P450cam needs a substrate-induced side-chain displacement coupled with a complex array of dynamical interconversions of multiple metastable substrate conformations. Further, the work reconciles multiple precedent experimental and theoretical observations on P450cam and establishes a comprehensive view of substrate-recognition in cytochrome P450 that only occurs via substrate-induced structural rearrangements.

Machine Learning Based Dimensionality Reduction Facilitates Ligand Diffusion Paths Assessment: A Case of Cytochrome P450cam.

In this work we propose an application of a nonlinear dimensionality reduction method to represent the high-dimensional configuration space of the ligand-protein dissociation process in a manner facilitating interpretation. Rugged ligand expulsion paths are mapped into 2-dimensional space. The mapping retains the main structural changes occurring during the dissociation. The topological similarity of the reduced paths may be easily studied using the Fréchet distances, and we show that this measure facilitates machine learning classification of the diffusion pathways. Further, low-dimensional configuration space allows for identification of residues active in transport during the ligand diffusion from a protein. The utility of this approach is illustrated by examination of the configuration space of cytochrome P450cam involved in expulsing camphor by means of enhanced all-atom molecular dynamics simulations. The expulsion trajectories are sampled and constructed on-the-fly during molecular dynamics simulations using the recently developed memetic algorithms [ Rydzewski, J.; Nowak, W. J. Chem. Phys. 2015 , 143 ( 12 ), 124101 ]. We show that the memetic algorithms are effective for enforcing the ligand diffusion and cavity exploration in the P450cam-camphor complex. Furthermore, we demonstrate that machine learning techniques are helpful in inspecting ligand diffusion landscapes and provide useful tools to examine structural changes accompanying rare events.

Computation of binding free energy with molecular dynamics and grand canonical Monte Carlo simulations.

The binding of a ligand to a receptor is often associated with the displacement of a number of bound water molecules. When the binding site is exposed to the bulk region, this process may be sampled adequately by standard unbiased molecular dynamics trajectories. However, when the binding site is deeply buried and the exchange of water molecules with the bulk region may be difficult to sample, the convergence and accuracy in free energy perturbation (FEP) calculations can be severely compromised. These problems are further compounded when a reduced system including only the region surrounding the binding site is simulated. To address these issues, we couple molecular dynamics (MD) with grand canonical Monte Carlo (GCMC) simulations to allow the number of water to fluctuate during an alchemical FEP calculation. The atoms in a spherical inner region around the binding pocket are treated explicitly while the influence of the outer region is approximated using the generalized solvent boundary potential (GSBP). At each step during thermodynamic integration, the number of water in the inner region is equilibrated with GCMC and energy data generated with MD is collected. Free energy calculations on camphor binding to a deeply buried pocket in cytochrome P450cam, which causes about seven water molecules to be expelled, are used to test the method. It concluded that solvation free energy calculations with the GCMC/MD method can greatly improve the accuracy of the computed binding free energy compared to simulations with fixed number of water.