Unraveling the Ligand-Binding Sites of CYP3A4 by Molecular Dynamics Simulations with Solvent Probes.

Cytochrome P450 3A4 (CYP3A4) is one of the most important drug-metabolizing enzymes in the human body and is well known for its complicated, atypical kinetic characteristics. The existence of multiple ligand-binding sites in CYP3A4 has been widely recognized as being capable of interfering with the active pocket through allosteric effects. The identification of ligand-binding sites other than the canonical active site above the heme is especially important for understanding the atypical kinetic characteristics of CYP3A4 and the intriguing association between the ligand and the receptor. In this study, we first employed mixed-solvent molecular dynamics (MixMD) simulations coupled with the online computational predictive tools to explore potential ligand-binding sites in CYP3A4. The MixMD approach demonstrates better performance in dealing with the receptor flexibility compared with other computational tools. From the sites identified by MixMD, we then picked out multiple sites for further exploration using ensemble docking and conventional molecular dynamics (cMD) simulations. Our results indicate that three extra sites are suitable for ligand binding in CYP3A4, including one experimentally confirmed site and two novel sites.

Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition.

Cytochrome P450 (CYP) enzymes are involved in the metabolism of approximately 75% of marketed drugs. Inhibition of the major drug-metabolizing P450s could alter drug metabolism and lead to undesirable drug-drug interactions. Therefore, it is of great significance to explore the inhibition of P450s in drug discovery. Currently, machine learning including deep learning algorithms has been widely used for constructing in silico models for the prediction of P450 inhibition. These models exhibited varying predictive performance depending on the use of machine learning algorithms and molecular representations. This leads to the difficulty in the selection of appropriate models for practical use. In this study, we systematically evaluated the conventional machine learning and deep learning models for three major P450 enzymes, CYP3A4, CYP2D6, and CYP2C9 from several perspectives, such as algorithms, molecular representation, and data partitioning strategies. Our results showed that the XGBoost and CatBoost algorithms coupled with the combined fingerprint/physicochemical descriptor features exhibited the best performance with Area Under Curve (AUC)  of 0.92, while the deep learning models were generally inferior to the conventional machine learning models (average AUC reached 0.89) on the same test sets. We also found that data volume and sampling strategy had a minor effect on model performance. We anticipate that these results are helpful for the selection of molecular representations and machine learning/deep learning algorithms in the P450 model construction and the future model development of P450 inhibition.

A macrophage cell membrane-coated cascade-targeting photothermal nanosystem for combating intracellular bacterial infections.

Current antibacterial interventions encounter formidable challenges when confronting intracellular bacteria, attributable to their clustering within phagocytes, particularly macrophages, evading host immunity and resisting antibiotics. Herein, we have developed an intelligent cell membrane-based nanosystem, denoted as MM@DAu NPs, which seamlessly integrates cascade-targeting capabilities with controllable antibacterial functions for the precise elimination of intracellular bacteria. MM@DAu NPs feature a core comprising D-alanine-functionalized gold nanoparticles (DAu NPs) enveloped by a macrophage cell membrane (MM) coating. Upon administration, MM@DAu NPs harness the intrinsic homologous targeting ability of their macrophage membrane to infiltrate bacteria-infected macrophages. Upon internalization within these host cells, exposed DAu NPs from MM@DAu NPs selectively bind to intracellular bacteria through the bacteria-targeting agent, D-alanine present on DAu NPs. This intricate process establishes a cascade mechanism that efficiently targets intracellular bacteria. Upon exposure to near-infrared irradiation, the accumulated DAu NPs surrounding intracellular bacteria induce local hyperthermia, enabling precise clearance of intracellular bacteria. Further validation in animal models infected with the typical intracellular bacteria, Staphylococcus aureus, substantiates the exceptional cascade-targeting efficacy and photothermal antibacterial potential of MM@DAu NPs in vivo. Therefore, this integrated cell membrane-based cascade-targeting photothermal nanosystem offers a promising approach for conquering persistent intracellular infections without drug resistance risks. STATEMENT OF SIGNIFICANCE: Intracellular bacterial infections lead to treatment failures and relapses because intracellular bacteria could cluster within phagocytes, especially macrophages, evading the host immune system and resisting antibiotics. Herein, we have developed an intelligent cell membrane-based nanosystem MM@DAu NPs, which is designed to precisely eliminate intracellular bacteria through a controllable cascade-targeting photothermal antibacterial approach. MM@DAu NPs combine D-alanine-functionalized gold nanoparticles with a macrophage cell membrane coating. Upon administration, MM@DAu NPs harness the homologous targeting ability of macrophage membrane to infiltrate bacteria-infected macrophages. Upon internalization, exposed DAu NPs from MM@DAu NPs selectively bind to intracellular bacteria through the bacteria-targeting agent, enabling precise clearance of intracellular bacteria through local hyperthermia. This integrated cell membrane-based cascade-targeting photothermal nanosystem offers a promising avenue for conquering persistent intracellular infections without drug resistance risks.