ABSTRACT
Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.
ABSTRACT
Objective To understand the relationship between AOX1,IRF4 gene methylation status in peripheral blood leukocyte DNA,as well as its interaction with environmental factors,and the risk of breast cancer.Methods A case-control study was conducted among 401 breast cancer patients and 555 cancer-free controls selected from 2010 to 2014.Methylation sensitive-high resolution melting curve analysis was used to detect the methylation status of AOX1 and IRF4.The multiplication interaction effect between genes' methylation and environmental factors on the risk of breast cancer was analyzed by using unconditional logistic regression,and Excel software was used to analyze the additive interaction effect.Results Individuals without AOX1 methylation had a 1.37-fold (95％ CI:1.02-1.84) higher breast cancer risk compared to individuals with AOX1 methylation.AOX1 methylation interacted with fungi intake (OR=2.06,95％ CI:1.12-3.79) and physical activity (OR=2.18,95％CI:1.16-4.09) synergistically,on the risk for breast cancer,but no additive interaction effects were observed.Non-methylation of IRF4 could increase the risk for breast cancer,with statistical significance (OR=1.71,95％CI:0.99-7.43).Neither multiplication nor additive interactions were observed between IRF4 methylation and environmental factors.Conclusion Non-methylation of AOX1 and IRF4 were a risk factors for breast cancer.
ABSTRACT
Objective To understand the relationship between AOX1,IRF4 gene methylation status in peripheral blood leukocyte DNA,as well as its interaction with environmental factors,and the risk of breast cancer.Methods A case-control study was conducted among 401 breast cancer patients and 555 cancer-free controls selected from 2010 to 2014.Methylation sensitive-high resolution melting curve analysis was used to detect the methylation status of AOX1 and IRF4.The multiplication interaction effect between genes' methylation and environmental factors on the risk of breast cancer was analyzed by using unconditional logistic regression,and Excel software was used to analyze the additive interaction effect.Results Individuals without AOX1 methylation had a 1.37-fold (95％ CI:1.02-1.84) higher breast cancer risk compared to individuals with AOX1 methylation.AOX1 methylation interacted with fungi intake (OR=2.06,95％ CI:1.12-3.79) and physical activity (OR=2.18,95％CI:1.16-4.09) synergistically,on the risk for breast cancer,but no additive interaction effects were observed.Non-methylation of IRF4 could increase the risk for breast cancer,with statistical significance (OR=1.71,95％CI:0.99-7.43).Neither multiplication nor additive interactions were observed between IRF4 methylation and environmental factors.Conclusion Non-methylation of AOX1 and IRF4 were a risk factors for breast cancer.
ABSTRACT
The major non-P450 enzymes involved in the oxidative metabolism of drugs are:the flavincontaining monooxygenase (FMO), the monoamine oxidase (MAO), the aldehyde oxidase (AO), the xanthine oxidase (XO), the alcohol dehydrogenase (ADH) and the aldehyde dehydrogenase (ALDH). In recent years, the role of non-P450 enzymes in drug oxidative metabolism has garnered increasing attention. However, the contribution of non-P450 enzymes to the drug oxidative metabolism is possibly underestimated in many cases, as most metabolism studies in drug discovery and lead optimization are conducted using in vitro test systems related to P450 enzymes. In this article, these non-P450 enzymes in terms of catalyzed reaction types, common substrates, gene polymorphism and drug interaction are reviewed, and the in vitro models and factors for non-P450-mediated oxidative metabolism are summarized. Similar to P450 enzymes, non-P450 enzymes can directly catalyze the oxidation of drugs, yielding therapeutically active metabolites or toxic metabolites. These enzymes can also oxidize the toxic metabolites, generated from P450-catalyzed reaction, to nontoxic metabolites. In general, most non-P450 enzymes (such as FMO and MAO) appear to be much less inducible than P450 enzymes.
ABSTRACT
Aldehyde oxidase (AO) plays important role in plant hormone biosynthetic pathways, such as abscisic acid (ABA) and indole- 3-acetic acid (IAA). The enzyme catalyzes the last step of the pathways. In this study, a full-length cDNA encoding an aldyhyde oxidase was cloned and sequenced from leaves of peanut by RT-PCR, RACE-PCR and genomic DNA walking methods. The full-length cDNA, designated as Arachis hygogaea L. aldehyde oxidase1 (AhAO1), consists of an open reading frame of 4131bp, a 326 bp 5¢ untranslated region and a 128 bp 3¢ untranslated region including a poly (A) tail of 21 nucleotides. The gene encodes a polypeptide of 1377 amino acids with a calculated molecular weight of 150 kDa and an isoelectric point (pI) of 6.99. Analysis of amino acid sequence of AhAO1 shows that it had 61%, 59% and 55% identity with the AOs from tomato, Arabidopsis and maize, respectively. The peanut AO polypeptide contains consensus sequences for iron-sulfur centers and a molybdenum cofactor (MoCo)-binding domain. Semi-quantitative RT-PCR analysis showed that AhAO1 expression was higher in leaves than in roots of peanut.