Revisiting Aldehyde Oxidase Mediated Metabolism in Drug-like Molecules: An Improved Computational Model

Zhao, Jihui; Cui, Rongrong; Wang, Lihao; Chen, Yingjia; Fu, Zunyun; Ding, Xiaoyu; Yang, Tianjun; Li, Xutong; Yue, Xu; Chen, Kaixian; Luo, Xiaomin; Jiang, Hualiang; Zheng, Mingyue

doi:10.1021/acs.jmedchem.9b01895

Cited by 12 publications

(22 citation statements)

References 39 publications

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“…Understanding and predicting hAOX1 bioactive conformations is of enormous relevance due to problems posed by the lack of reliable animal models and to intraspecies variations. Although computational models have been reported to help evaluate the lability of molecules toward hAOX1, there is a lack of an efficient experimental approach. , The work here reported constitutes an alternative strategy for predicting putative substrates and inhibitors of hAOX1.…”

Section: Introductionmentioning

confidence: 99%

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Interrogating the Inhibition Mechanisms of Human Aldehyde Oxidase by X-ray Crystallography and NMR Spectroscopy: The Raloxifene Case

et al. 2021

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Human aldehyde oxidase (hAOX1) is mainly present in the liver and has an emerging role in drug metabolism, since it accepts a wide range of molecules as substrates and inhibitors. Herein, we employed an integrative approach by combining NMR, X-ray crystallography, and enzyme inhibition kinetics to understand the inhibition modes of three hAOX1 inhibitorsthioridazine, benzamidine, and raloxifene. These integrative data indicate that thioridazine is a noncompetitive inhibitor, while benzamidine presents a mixed type of inhibition. Additionally, we describe the first crystal structure of hAOX1 in complex with raloxifene. Raloxifene binds tightly at the entrance of the substrate tunnel, stabilizing the flexible entrance gates and elucidating an unusual substrate-dependent mechanism of inhibition with potential impact on drug−drug interactions. This study can be considered as a proof-of-concept for an efficient experimental screening of prospective substrates and inhibitors of hAOX1 relevant in drug discovery.

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Section: Introductionmentioning

confidence: 99%

“…Although computational models have been reported to help evaluate the lability of molecules toward hAOX1, there is a lack of an efficient experimental approach. 24,25 The work here reported constitutes an alternative strategy for predicting putative substrates and inhibitors of hAOX1.…”

Section: ■ Introductionmentioning

confidence: 99%

Interrogating the Inhibition Mechanisms of Human Aldehyde Oxidase by X-ray Crystallography and NMR Spectroscopy: The Raloxifene Case

et al. 2021

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“…As shown in Table 1 , experts summarized two types of potential SOMs metabolized by hAOX and optimized them as the SMILES arbitrary target specification (SMARTS) strings according to reaction mechanism and hAOX metabolites reported in the literature [ 12 ]. We employed atom environment fingerprint to describe specific atoms in a molecule [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

“…Montefiori et al developed fast methods for the prediction of SOMs mediated by aldehyde oxidase: (1) using ChemBioDraw to calculate the chemical shift or (2) calculating ESP charges or chemical shifts with density functional theory [ 11 ]. Zhao et al combined traditional quantum chemical calculations and decision tree models to predict the SOMs mediated by hAOX [ 12 , 13 ]. It is difficult for these above-mentioned methods to automate and it is time-consuming.…”

Section: Introductionmentioning

confidence: 99%

In Silico Prediction of Metabolic Reaction Catalyzed by Human Aldehyde Oxidase

et al. 2023

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Aldehyde oxidase (AOX) plays an important role in drug metabolism. Human AOX (hAOX) is widely distributed in the body, and there are some differences between species. Currently, animal models cannot accurately predict the metabolism of hAOX. Therefore, more and more in silico models have been constructed for the prediction of the hAOX metabolism. These models are based on molecular docking and quantum chemistry theory, which are time-consuming and difficult to automate. Therefore, in this study, we compared traditional machine learning methods, graph convolutional neural network methods, and sequence-based methods with limited data, and proposed a ligand-based model for the metabolism prediction catalyzed by hAOX. Compared with the published models, our model achieved better performance (ACC = 0.91, F1 = 0.77). What’s more, we built a web server to predict the sites of metabolism (SOMs) for hAOX. In summary, this study provides a convenient and automatable model and builds a web server named Meta-hAOX for accelerating the drug design and optimization stage.

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“…Consequently, HGMDM is advocated to be systematically and experimentally tested in the early stage of drug discovery, and its computational prediction becomes increasingly useful for the rapid assessment before laborious experiments. So far, only four computational works on the HGM-mediated metabolism susceptibility of drug molecules were reported, − albeit there is a large volume of works on the prediction of the host-mediated drug metabolism (e.g., Phase I and II metabolism). − …”

Section: Introductionmentioning

confidence: 99%

Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods

Zheng

Wang

Xiong

et al. 2022

J. Chem. Inf. Model.

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The human gut microbiota (HGM) colonizing human gastrointestinal tract (HGT) confers a repertoire of dynamic and unique metabolic capacities that are not possessed by the host and therefore is tentatively perceived as an alternative metabolic ″organ″ besides the liver in the host. Nevertheless, the significant contribution of HGM to the overall human metabolism is often overlooked in the modern drug discovery pipeline. Hence, a systematic evaluation of HGM-mediated drug metabolism is gradually important, and its computational prediction becomes increasingly necessary. In this work, a new data set containing both the HGM-mediated metabolism susceptible (HGMMS) and insusceptible (HGMMI) compounds (329 vs 320) was manually curated. Based on this data set, the first machine learning (ML) model, a new structural alerts (SA) model, and the K-nearest neighboring dietary compounds-based average similarity (AS) model were proposed to directly predict the HGM-mediated metabolism susceptibility for small molecules, and exhibit promising performance on three independent test sets. Finally, consensus prediction (ML/SA/AS) for DrugBank molecules revealed an intriguing phenomenon that a typical Michael acceptor ″α,β-unsaturated carbonyl group″ is a very common warhead for the design of covalent inhibitors and inclined to be metabolized by HGM in anaerobic HGT to generate the reduced metabolite without the reactive warhead, which could be a new concern to medicinal chemists. To the best of our knowledge, we gleaned the first HGMMS/HGMMI data set, developed the first HGMMS/HGMMI classification model, implemented a relatively comprehensive program based on ML/SA/AS approaches, and found a new phenomenon on the HGM-mediated deactivation of an extensively used warhead for covalent inhibitors.

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Revisiting Aldehyde Oxidase Mediated Metabolism in Drug-like Molecules: An Improved Computational Model

Cited by 12 publications

References 39 publications

Interrogating the Inhibition Mechanisms of Human Aldehyde Oxidase by X-ray Crystallography and NMR Spectroscopy: The Raloxifene Case

Interrogating the Inhibition Mechanisms of Human Aldehyde Oxidase by X-ray Crystallography and NMR Spectroscopy: The Raloxifene Case

In Silico Prediction of Metabolic Reaction Catalyzed by Human Aldehyde Oxidase

Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods

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