Dataset Information

CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates.

ABSTRACT: The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacological efficacy and toxicity. Therefore, efficient means of predicting the interactions of small organic molecules with CYPs are of high importance to a host of different industries. In this work, we present a new set of machine learning models for the classification of xenobiotics into substrates and non-substrates of nine human CYP isozymes: CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4. The models are trained on an extended, high-quality collection of known substrates and non-substrates and have been subjected to thorough validation. Our results show that the models yield competitive performance and are favorable for the detection of CYP substrates. In particular, a new consensus model reached high performance, with Matthews correlation coefficients (MCCs) between 0.45 (CYP2C8) and 0.85 (CYP3A4), although at the cost of coverage. The best models presented in this work are accessible free of charge via the "CYPstrate" module of the New E-Resource for Drug Discovery (NERDD).

SUBMITTER: Holmer M

PROVIDER: S-EPMC8347321 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates.

Holmer Malte M de Bruyn Kops Christina C Stork Conrad C Kirchmair Johannes J

Molecules (Basel, Switzerland) 20210802 15

The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacological efficacy and toxicity. Therefore, efficient means of predicting the interactions of small organic molecules with CYPs are of high importance to a host of different industries. In this work, we present a new set of machine learning models for the ...[more]

PMID: 34361831

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Project description:BACKGROUND AND PURPOSE:MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. MATERIALS AND METHODS:We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS:Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). CONCLUSIONS:Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

Project description:BackgroundCytochrome P450s (CYPs) are important heme-containing proteins, well known for their monooxygenase reaction. The human cytochrome P450 4X1 (CYP4X1) is categorized as "orphan" CYP because of its unknown function. In recent studies it is found that this enzyme is expressed in neurovascular functions of the brain. Also, various studies have found the expression and activity of orphan human cytochrome P450 4X1 in cancer. It is found to be a potential drug target for cancer therapy. However, three-dimensional structure, the active site topology and substrate specificity of CYP4X1 remain unclear.MethodsIn the present study, the three-dimensional structure of orphan human cytochrome P450 4X1 was generated by homology modeling using Modeller 9v8. The generated structure was accessed for geometrical errors and energy stability using PROCHECK, VERFIY 3D and PROSA. A molecular docking analysis was carried out against substrates arachidonic acid and anandamide and the docked substrates were predicted for drug-likeness, ADME-Tox parameters and biological spectrum activity.ResultsThe three-dimensional model of orphan human cytochrome P450 4X1 was generated and assessed with various structural validation programmes. Docking of orphan human cytochrome P450 4X1 with arachidonic acid revealed that TYR 112, ALA 126, ILE 222, ILE 223, THR 312, LEU 315, ALA 316, ASP 319, THR 320, PHE 491 and ILE 492 residues were actively participating in the interaction, while docking of CYP4X1 with anandamide showed that TYR 112, GLN 114, PRO 118, ALA 126, ILE 222, ILE 223, SER 251, LEU 315, ALA 316 and PHE 491 key residues were involved in strong interaction.ConclusionFrom this study, several key residues were identified to be responsible for the binding of arachidonic acid and anandamide with orphan human cytochrome P450 4X1. Both substrates obeyed Lipinski rule of five in drug-likeness test and biological spectrum prediction showed anticarcinogenic activity. Compared to anandamide, arachidonic acid showed strong interaction with cytochrome P450 4X1 and also less health effect in certain human system in ADME-Tox prediction. These findings provide useful information on the biological role and structure-based drug design of orphan human cytochrome P450 4X1.

Dataset Information

CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates.

Publications

CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates.

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