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Furan-containing compounds distribute widely in food, herbal medicines, industrial synthetic products, and environmental media. These compounds can undergo oxidative metabolism catalyzed by cytochrome P450 enzymes (CYP450) within organisms, which may produce reactive products, possibly reacting with biomolecules to induce toxic effects. In this work, we performed DFT calculations to investigate the CYP450-mediated metabolic mechanism of furan-ring oxidation using 2-methylfuran as a model substrate, meanwhile, we studied the regioselective competition of another hydroxylation reaction involving methyl group of 2-methylfuran. As a result, we found the toxicological-relevant cis-enedione product can be produced from O-addition directly via a concerted manner without formation of an epoxide intermediate as traditionally believed. Moreover, our calculations demonstrate the kinetic and thermodynamic feasibility of both furan-ring oxidation and methyl hydroxylation pathways, although the former pathway is a bit more favorable. We then constructed a linear model to predict the rate-limiting activation energies (ΔE*) of O-addition with 11 diverse furan substates based on their adiabatic ionization potentials (AIPs) and condensation Fukui functions (CFFs). The results show a good predictive ability (R2=0.94, Q2CV=0.87). Therefore, AIP and CFF with clear physichem meanings relevant to the mechanism, emerge as pivotal molecular descriptors to enable the fast prediction of furan-ring oxidation reactivities for quick insight into the toxicological risk of furans, using just ground-state calculations.

The uncommon metabolic pathways of organic pollutants are easily overlooked, potentially leading to idiosyncratic toxicity. Prediction of their biotransformation associated with the toxic effects is the very purpose that this work focuses, to develop a de novo method to mechanistically predict the reactive toxicity pathways of uncommon metabolites from start aliphatic amine molecules, which employed sertraline triggered by CYP450 enzymes as a model system, as there are growing concerns about the effects on human health posed by antidepressants in the aquatic environment. This de novo prediction strategy combines computational and experimental methods, involving DFT calculations upon sequential growth, in vitro and in vivo assays, dissecting chemically reactive mechanism relevant to toxicity, and rationalizing the fundamental factors. Significantly, desaturation and debenzylation-aromatization as the emerging metabolic pathways of sertraline have been elucidated, with the detection of DNA adducts of oxaziridine metabolite in mice, highlighting the potential reactive toxicity. Molecular orbital analysis supports the reactivity preference for toxicological-relevant C-N desaturation over N-hydroxylation of sertraline, possibly extended to several other aliphatic amines based on the Bell-Evans-Polanyi principle. It was further validated toward some other wide-concerned aliphatic amine pollutants involving atrazine, ε-caprolactam, 6PPD via in silico and in vitro assays, thereby constituting a complete path for de novo prediction from case study to general applications.

Summary: Functional explication of genes is of great scientific value. However, conventional methods have challenges for those genes that may affect biological processes but are not annotated in public databases. Here, we developed a novel explainable gene ontology fingerprint (XGOF) method to automatically produce knowledge networks on biomedical literature in a given field which quantitatively characterizes the association between genes and ontologies. XGOF provides systematic knowledge for the potential function of genes and ontologically compares similarities and discrepancies in different disease-XGOFs integrating omics data. More importantly, XGOF can not only help to infer major cellular components in a disease microenvironment but also reveal novel gene panels or functions for in-depth experimental research where few explicit connections to diseases have previously been described in the literature. The reliability of XGOF is validated in four application scenarios, indicating a unique perspective of integrating text and data mining, with the potential to accelerate scientific discovery.