Structural determinants of steroids for cytochrome P450 3A4-mediated metabolism

Rational design of thermophilic CYP119 for progesterone hydroxylation by in silico mutagenesis and docking screening

Steroid-based chemicals can affect the metabolism, immune functions, and development of sexual characteristics. Because of these effects, steroid derivatives are widely used in the pharmaceutical industry. Progesterone is a steroid-based hormone that mainly controls the ovulation period of women but is also a precursor molecule for the synthesis of important hormones like testosterone and cortisone. Cytochrome P450 (CYP) enzymes are important for the production of hydroxyprogesterones in the industry since they can catalyze regio- and enantioselective hydroxylation reactions. Although human CYP enzymes can catalyze hydroxyprogesterone synthesis with high selectivity, these enzymes are membrane bound, which limits their application for industrial production. CYP119 is a soluble and thermophilic enzyme from the archaea Sulfolobus acidocaldarius. Even though the native substrate of the enzyme is not known, CYP119 can catalyze styrene epoxidation, lauric acid hydroxylation, and Amplex®Red peroxidation. In this work, an in silico mutagenesis approach was used to design CYP119 mutants with high progesterone affinity. Energy scores of progesterone docking simulations were used for the design and elimination of single, double, and triple mutants of CYP119. Among designed 674 mutants, five of them match the criteria for progesterone hydroxylation. The most common mutation of these five mutants, L69G mutant was analyzed using independent molecular dynamics (MD) simulations in comparison with the wild-type (WT) enzyme. L69G CYP119, was expressed and isolated from Escherichia coli; it showed 800-fold higher affinity for progesterone compared to WT CYP119. L69G CYP119 also catalyzed progesterone hydroxylation. The novel designed enzyme L69G CYP119 is a potential versatile biocatalyst for progesterone hydroxylation that is expected to be stable under industrial production conditions.

Role of Water Hydrogen Bonding on Transport of Small Molecules inside Hydrophobic Channels

We conducted a systematic analysis of water networking inside smooth hyperboloid hydrophobic structures (cylindrical, barrel, and hourglass shapes) to elucidate the role of water hydrogen bonding on the transport of small hydrophobic molecules (ligands). Through a series of molecular dynamics simulations, we established that a hydrogen-bonded network forming along the centerline resulted in a water exclusion zone adjacent to the walls. The size of the exclusion zone is a function of the geometry and the nonbonded interaction strength, defining the effective hydrophobicity of the structure. Exclusion of water molecules from this zone results in lower apparent viscosity, leading to acceleration of ligand transport up to 7 times faster than that measured in the bulk. Transport of ligands into and out of the hydrophobic structures was shown to be controlled by a single water molecule that capped the narrow regions in the structure. This mechanism provides physical insights into the behavior and role of water in the bottleneck regions of hydrophobic enzyme channels. These findings were then used in a sister publication [ Escalante , D. E. , Comput. Struct. Biotechnol. J. 2019 17 757 760 ] to develop a model that can accurately predict the transport of ligands along nanochannels of broad-substrate specificity enzymes.

A Mechanism-Based Model for the Prediction of the Metabolic Sites of Steroids Mediated by Cytochrome P450 3A4

Early prediction of xenobiotic metabolism is essential for drug discovery and development. As the most important human drug-metabolizing enzyme, cytochrome P450 3A4 has a large active cavity and metabolizes a broad spectrum of substrates. The poor substrate specificity of CYP3A4 makes it a huge challenge to predict the metabolic site(s) on its substrates. This study aimed to develop a mechanism-based prediction model based on two key parameters, including the binding conformation and the reaction activity of ligands, which could reveal the process of real metabolic reaction(s) and the site(s) of modification. The newly established model was applied to predict the metabolic site(s) of steroids; a class of CYP3A4-preferred substrates. 38 steroids and 12 non-steroids were randomly divided into training and test sets. Two major metabolic reactions, including aliphatic hydroxylation and N-dealkylation, were involved in this study. At least one of the top three predicted metabolic sites was validated by the experimental data. The overall accuracy for the training and test were 82.14% and 86.36%, respectively. In summary, a mechanism-based prediction model was established for the first time, which could be used to predict the metabolic site(s) of CYP3A4 on steroids with high predictive accuracy.

Investigation of antigen-antibody interactions of sulfonamides with a monoclonal antibody in a fluorescence polarization immunoassay using 3D-QSAR models

A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs binding a monoclonal antibody (MAb_SMR) produced against sulfamerazine was carried out by Distance Comparison (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA). The affinities of the MAb_SMR, expressed as Log₁₀IC₅₀, for 17 sulfonamide analogs were determined by competitive fluorescence polarization immunoassay (FPIA). The results demonstrated that the proposed pharmacophore model containing two hydrogen-bond acceptors, two hydrogen-bond donors and two hydrophobic centers characterized the structural features of the sulfonamides necessary for MAb_SMR binding. Removal of two outliers from the initial set of 17 sulfonamide analogs improved the predictability of the models. The 3D-QSAR models of 15 sulfonamides based on CoMFA and CoMSIA resulted in q²_cv values of 0.600 and 0.523, and r² values of 0.995 and 0.994, respectively, which indicates that both methods have significant predictive capability. Connolly surface analysis, which mainly focused on steric force fields, was performed to complement the results from CoMFA and CoMSIA. This novel study combining FPIA with pharmacophore modeling demonstrates that multidisciplinary research is useful for investigating antigen-antibody interactions and also may provide information required for the design of new haptens.

In silico prediction of estrogen receptor subtype binding affinity and selectivity using statistical methods and molecular docking with 2-arylnaphthalenes and 2-arylquinolines

Over the years development of selective estrogen receptor (ER) ligands has been of great concern to researchers involved in the chemistry and pharmacology of anticancer drugs, resulting in numerous synthesized selective ER subtype inhibitors. In this work, a data set of 82 ER ligands with ERα and ERβ inhibitory activities was built, and quantitative structure-activity relationship (QSAR) methods based on the two linear (multiple linear regression, MLR, partial least squares regression, PLSR) and a nonlinear statistical method (Bayesian regularized neural network, BRNN) were applied to investigate the potential relationship of molecular structural features related to the activity and selectivity of these ligands. For ERα and ERβ, the performances of the MLR and PLSR models are superior to the BRNN model, giving more reasonable statistical properties (ERα: for MLR, R_tr² = 0.72, Q_te² = 0.63; for PLSR, R_tr² = 0.92, Q_te² = 0.84. ERβ: for MLR, R_tr² = 0.75, Q_te² = 0.75; for PLSR, R_tr² = 0.98, Q_te² = 0.80). The MLR method is also more powerful than other two methods for generating the subtype selectivity models, resulting in R_tr² = 0.74 and Q_te² = 0.80. In addition, the molecular docking method was also used to explore the possible binding modes of the ligands and a relationship between the 3D-binding modes and the 2D-molecular structural features of ligands was further explored. The results show that the binding affinity strength for both ERα and ERβ is more correlated with the atom fragment type, polarity, electronegativites and hydrophobicity. The substitutent in position 8 of the naphthalene or the quinoline plane and the space orientation of these two planes contribute the most to the subtype selectivity on the basis of similar hydrogen bond interactions between binding ligands and both ER subtypes. The QSAR models built together with the docking procedure should be of great advantage for screening and designing ER ligands with improved affinity and subtype selectivity property.

Modeling Km values using electrotopological state: Substrates for cytochrome P450 3A4-mediated metabolism

In order to determine K(m) values of substrates for CYP3A4-mediated metabolism, an in silico model has been developed in the present work. Using electrotopological state (E-state) indices, together with Bayesian-regularized neural network (BRNN), we have described an in silico method to model log(1/K(m)) values of various substrates. The relative importance of the E-state indices is analyzed by principal component analysis. By using an additional external test set, which is independent of the training set, the robustness and predictivity of the model are also validated.