Site Of Metabolism Prediction Research Articles

Prediction of Sites of Metabolism of CYP3A4 Substrates Utilizing Docking-Derived Geometric Features.

Cytochrome P450 3A4 (CYP3A4) is one of the major drug-metabolizing enzymes in the human body and is responsible for the metabolism of ∼50% of clinically used drugs. Therefore, the identification of the compound's sites of metabolism (SOMs) mediated by CYP3A4 is of utmost importance in the early stage of drug discovery and development. Herein, docking-based approaches incorporating geometric features were used for SOMs prediction of CYP3A4 substrates. The cross-docking poses of a relatively large data set containing 474 substrates were analyzed in depth, and a widely observed geometric pattern called the close proximity of SOMs was derived from the poses. On the basis of the close proximity, several structure-based models have been constructed, which demonstrated better performance than those structure-based models using the criterion of Fe-SOM distance. For further improving the prediction performance, the structure-based models were also combined with the well-known ligand-based model SMARTCyp. One combined model exhibited good performance on the SOMs prediction of an external substrate set containing kinase inhibitors, PROTACs, approved drugs, and some lead compounds.

Site of Metabolism (SOM) Predictions for Centella asiatica and Orthosiphon stamineus Marker Compounds with CYP3A4 Using a Molecular Docking Approach

The human liver enzyme CYP3A4 plays an important role in the biotransformation of xenobiotics from herbs to harmless and excretable metabolites. However, CYP3A4 catalytic activity may also produce more toxic metabolites. Predicting sites of metabolism (SOMs) in the xenobiotics may prevent unwanted CYP3A4 products. The evidence to explain the comprehensive biotransformation of major compounds from Centella asiatica and Orthosiphon stamineus is inadequate. The aim of the present study was to determine the specific interaction between CYP3A4 ferric–oxidative active sites and potential SOMs for the major chemical compounds of C. asiatica and O. stamineus. Molecular docking on CYP3A4 ferric–oxo (bound to the protoporphirin group, HEME–Fe3+–O-) was carried out using the CDOCKER simulation program to predict the SOMs for compounds from C. asiatica (asiaticoside, madecassoside, asiatic acid and madecassic acid) and O. stamineus (sinensetin, eupatorin and 5-hydroxy-6,7,3',4'-tetramethoxyflavone (5TMF)). Binding energies, residue–ligand interactions, and SOMs were obtained from the analysis. The molecular docking results also revealed that sinensetin, eupatorin, and 5TMF bound strongly to the CYP3A4 active site with binding energies of -22.44, -34.29, and -25.84 kcal/mol, respectively. Generally, the residues Arg106, Ile301, Ther309, Glu374 and Ala307 were identified to have the highest number of interactions with eupatorine, 5TMF and sinensetin. The SOM prediction for eupatorine showed interactions between the oxygen of the HEME iron (ferric oxo) and the C-11 and O-4 atoms. The possible metabolism reactions were C-11-hydroxylation and O-4-demethylation. For 5TMF, the SOM prediction for interactions were at the C-13 and O-7 positions, which could undergo hydroxylation and O-demethylation reactions, respectively. The SOM of sinensetin was predicted at C-3’ and the possible metabolic reaction taking place would be hydroxylation. The present study concluded that the major compounds of O. stamineus have the potential to be metabolized by CYP3A4 through hydroxylation and O-demethylation reactions. These data may be useful for phytomedicine product development related to CYP3A4 biotransformation mechanisms.

Assessment of CYP2C9 Structural Models for Site of Metabolism Prediction.

Structure-based prediction of a compound's potential sites of metabolism (SOMs) mediated by cytochromes P450 (CYPs) is highly advantageous in the early stage of drug discovery. However, the accuracy of the SOMs prediction can be influenced by several factors. CYP2C9 is one of the major drug-metabolizing enzymes in humans and is responsible for the metabolism of ∼13 % of clinically used drugs. In this study, we systematically evaluated the effects of protein crystal structure models, scoring functions, heme forms, conserved active-site water molecules, and protein flexibility on SOMs prediction of CYP2C9 substrates. Our results demonstrated that, on average, ChemScore and GlideScore outperformed four other scoring functions: Vina, GoldScore, ChemPLP, and ASP. The performance of the crystal structure models with pentacoordinated heme was generally superior to that of the hexacoordinated iron-oxo heme (referred to as Compound I) models. Inclusion of the conserved active-site water molecule improved the prediction accuracy of GlideScore, but reduced the accuracy of ChemScore. In addition, the effect of the conserved water on SOMs prediction was found to be dependent on the receptor model and the substrate. We further found that one of snapshots from molecular dynamics simulations on the apo form can improve the prediction accuracy when compared to the crystal structural model.

Identification of the\xa0most potent acetylcholinesterase inhibitors from plants for possible treatment of Alzheimer\u2019s disease: a computational approach

BackgroundBeing one of the rapidly growing dementia type diseases in the world, Alzheimer’s disease (AD) has gained much attention from researchers in the recent decades. Many hypotheses have been developed that describe different reasons for the development of AD. Among them, the cholinergic hypothesis depicts that the degradation of an important neurotransmitter, acetylcholine by the enzyme acetylcholinesterase (AChE), is responsible for the development of AD. Although, many anti-AChE drugs are already available in the market, their performance sometimes yields unexpected results. For this reason, research works are going on to find out potential anti-AChE agents both from natural and synthetic sources. In this study, 50 potential anti-AChE phytochemicals were analyzed using numerous tools of bioinformatics and in silico biology to find out the best possible anti-AChE agents among the selected 50 ligands through molecular docking, determination of the druglikeness properties, conducting the ADMET test, PASS and P450 site of metabolism prediction, and DFT calculations.ResultThe predictions of this study suggested that among the selected 50 ligands, bellidifolin, naringenin, apigenin, and coptisine were the 4 best compounds with quite similar and sound performance in most of the experiments.ConclusionIn this study, bellidifolin, naringenin, apigenin, and coptisine were found to be the most effective agents for treating the AD targeting AChE. However, more in vivo and in vitro analyses are required to finally confirm the outcomes of this research.

PreMetabo: An in silico phase I and II drug metabolism prediction platform

This study aimed to develop a drug metabolism prediction platform using knowledge-based prediction models. Site of Metabolism (SOM) prediction models for four cytochrome P450 (CYP) subtypes were developed along with uridine 5′-diphosphoglucuronosyltransferase (UGT) and sulfotransferase (SULT) substrate classification models. The SOM substrate for a certain CYP was determined using the sum of the activation energy required for the reaction at the reaction site of the substrate and the binding energy of the substrate to the CYP enzyme. Activation energy was calculated using the EaMEAD model and binding energy was calculated by docking simulation. Phase II prediction models were developed to predict whether a molecule is the substrate of a certain phase II conjugate protein, i.e., UGT or SULT. Using SOM prediction models, the predictability of the major metabolite in the top-3 was obtained as 72.5–84.5% for four CYPs, respectively. For internal validation, the accuracy of the UGT and SULT substrate classification model was obtained as 93.94% and 80.68%, respectively. Additionally, for external validation, the accuracy of the UGT substrate classification model was obtained as 81% in the case of 11 FDA-approved drugs. PreMetabo is implemented in a web environment and is available at https://premetabo.bmdrc.kr/.

Using chemical bond-based method to predict site of metabolism for five biotransformations mediated by CYP 3A4, 2D6, and 2C9

Although it has been proposed for decades to predict site of metabolism (SOM) by in silico methods, identifying SOM correctly remains an unsolved fundamental problem and is an active area of research. In our prior works, we proposed a chemical bond-based approach to construction of SOM prediction models by integrating chemical bond descriptors and drug-metabolizing enzymes data. Although it has been evaluated with both 10-fold cross-validation and independent validation, we believe comparisons between this method and prior methods using publicly accessible external datasets are indispensable and more desirable. In the current study, based on chemical bond-based method, metabolism data released by Sheridan et al. and Zaretzki et al. was utilized to establish metabolite prediction models for CYP450 3A4, 2D6, and 2C9. Five major reaction types were involved, including Aliphatic C-hydroxylation, Aromatic C-hydroxylation, N-dealkylation, O-dealkylation, and S-Oxidation. Consequently, all our five models showed impressive performance on predicting SOMs, with accuracy and area under curve exceeded 0.940 and 0.953, respectively. Compared to prior works, our models were better than SOMP both in “SOM-scale” and “molecule-scale”. In conclusion, comparisons between chemical-bond based method and prior works were conducted for the first time, which demonstrated that chemical-bond based method is better than or at least comparable to prior works.

Alignment-Based Prediction of Sites of Metabolism.

Prediction of metabolically labile atom positions in a molecule (sites of metabolism) is a key component of the simulation of xenobiotic metabolism as a whole, providing crucial information for the development of safe and effective drugs. In 2008, an exploratory study was published in which sites of metabolism were derived based on molecular shape- and chemical feature-based alignment to a molecule whose site of metabolism (SoM) had been determined by experiments. We present a detailed analysis of the breadth of applicability of alignment-based SoM prediction, including transfer of the approach from a structure- to ligand-based method and extension of the applicability of the models from cytochrome P450 2C9 to all cytochrome P450 isozymes involved in drug metabolism. We evaluate the effect of molecular similarity of the query and reference molecules on the ability of this approach to accurately predict SoMs. In addition, we combine the alignment-based method with a leading chemical reactivity model to take reactivity into account. The combined model yielded superior performance in comparison to the alignment-based approach and the reactivity models with an average area under the receiver operating characteristic curve of 0.85 in cross-validation experiments. In particular, early enrichment was improved, as evidenced by higher BEDROC scores (mean BEDROC = 0.59 for α = 20.0, mean BEDROC = 0.73 for α = 80.5).

Site of Metabolism Prediction Based on ab initio Derived Atom Representations.

Machine learning models for site of metabolism (SoM) prediction offer the ability to identify metabolic soft spots in low-molecular-weight drug molecules at low computational cost and enable data-based reactivity prediction. SoM prediction is an atom classification problem. Successful construction of machine learning models requires atom representations that capture the reactivity-determining features of a potential reaction site. We have developed a descriptor scheme that characterizes an atom's steric and electronic environment and its relative location in the molecular structure. The partial charge distributions were obtained from fast quantum mechanical calculations. We successfully trained machine learning classifiers on curated cytochrome P450 metabolism data. The models based on the new atom descriptors showed sustained accuracy for retrospective analyses of metabolism optimization campaigns and lead optimization projects from Bayer Pharmaceuticals. The results obtained demonstrate the practicality of quantum-chemistry-supported machine learning models for hit-to-lead optimization.

Dissecting the Cytochrome P450 1A2- and 3A4-Mediated Metabolism of Aflatoxin B1 in Ligand and Protein Contributions.

Aflatoxin B1 (AFB1) is a chemically intriguing compound because it has several potential sites of metabolism (SOMs), although only some of them are observed experimentally. Cytochrome P450 (CYP) 3A4 and 1A2 are the major isoforms involved in its metabolism. Here, we systematically investigate reactivity and accessibility of all possible SOMs in these two CYPs to elucidate AFB1 metabolism. DFT calculations were used to determine activation energies for each possible reaction. Aliphatic hydroxylation on position 9A and 3α are energetically favored, whereas position 9 is the preferred site for epoxidation. Docking studies, molecular dynamics (MD) simulations, and free energy (MM/GBSA) calculations were applied to elucidate the accessibility of each SOM. The most stable binding modes in CYP3A4 favor the formation of the 3α-hydroxylated and 8,9-exo-epoxide metabolites. Conversion of the methoxy group is also sterically possible, but not observed experimentally due to its low reactivity. In the CYP1A2 active site, AFB1 cannot orient position 3 towards the catalytic center, whereas the 8,9-exo-epoxide and 9A-hydroxylated metabolites are formed from the most stable and the 8,9-endo-epoxide from a less stable binding mode, respectively. The results agree with experimental data and suggest that both reactivity and the shape of the enzyme active site need to be considered to understand the distribution of SOMs and to improve current SOM prediction methods.

Site of metabolism prediction for oxidation reactions mediated by oxidoreductases based on chemical bond.

The metabolites of exogenous and endogenous compounds play a pivotal role in the domain of metabolism research. However, they are still unclear for most chemicals in our environment. The in silico methods for predicting the site of metabolism (SOM) are considered to be efficient and low-cost in SOM discovery. However, many in silico methods are focused on metabolism processes catalyzed by several specified Cytochromes P450s, and only apply to substrates with special skeleton. A SOM prediction model always deserves more attention, which demands no special requirements to structures of substrates and applies to more metabolic enzymes. By incorporating the use of hybrid feature selection techniques (CHI, IG, GR, Relief) and multiple classification procedures (KStar, BN, IBK, J48, RF, SVM, AdaBoostM1, Bagging), SOM prediction models for six oxidation reactions mediated by oxidoreductases were established by the integration of enzyme data and chemical bond information. The advantage of the method is the introduction of unlabeled SOM. We defined the SOM which not reported in the literature as unlabeled SOM, where negative SOM was filtered. Consequently, for each type of reaction, a series of SOM prediction models were built based on information about metabolism of 1237 heterogeneous chemicals. Then optimal models were attained through comparisons among these models. Finally, independent test set was used to validate optimal models. It demonstrated that all models gave accuracies above 0.90. For receiver operating characteristic analysis, the area under curve values of all these models over 0.906. The results suggested that these models showed good predicting power. All the models will be available when contact with wangyun@bucm.edu.cn. wangyun@bucm.edu.cn or yjqiao@yahoo.com. Supplementary data are available at Bioinformatics online.

Improved Prediction of CYP-Mediated Metabolism with Chemical Fingerprints.

Molecule and atom fingerprints, similar to path-based Daylight fingerprints, can substantially improve the accuracy of P450 site-of-metabolism prediction models. Only two chemical fingerprints have been used in metabolism prediction, so little is known about the importance of fingerprint parameters on site of metabolism predictions. It is possible that different fingerprints might yield more accurate models. Here, we study if tuning fingerprints to specific site of metabolism data sets can lead to improved models. We measure the impact of 484 specific chemical fingerprints on the accuracy of P450 site-of-metabolism prediction models on nine P450 isoform site of metabolism data sets. Using a range of search depths, we study path, circular, and subgraph fingerprints. Two different labelings, also, are considered, both standard SMILES labels and also a labeling that marks ring bonds differently than nonring bonds, enabling ortho, para, and meta positioning of substituents to be more clearly encoded. Optimal fingerprint models chosen by cross-validation performance on the full training data are, on average, 3.8% (Top-2; percent of molecules with a site of metabolism in the top two predictions) and 1.4% (AUC; area under the ROC curve) more accurate than base fingerprint models. These gains represent, respectively, a 25.6% and 16.7% reduction in error. A more rigorous assessment selects fingerprints within each cross-validation fold, sometimes selecting different fingerprints for different folds, but yielding a more reliable estimate of generalization error. In this assessment, averaging the scores from the top few fingerprints yields performances improvements of, on average, 3.0% (Top-2) and 0.7% (AUC). These gains are statistically significant and represent, respectively, a 20.1% and 8.8% reduction in error. Between different isoforms, not many consistencies were observed among the top performing fingerprints, with different fingerprints working best for different isoforms. These results suggest that there are important gains achievable in site of metabolism modeling by including and optimizing atom and molecule fingerprints. The optimal site of metabolism models determined by this approach are available for use at http://swami.wustl.edu/.

Effects of protein flexibility on the site of metabolism prediction for CYP2A6 substrates

Structure-based prediction for the site of metabolism (SOM) of a compound metabolized by human cytochrome P450s (CYPs) is highly beneficial in drug discovery and development. However, the flexibility of the CYPs’ active site remains a huge challenge for accurate SOM prediction. Compared with other CYPs, the active site of CYP2A6 is relatively small and rigid. To address the impact of the flexibility of CYP2A6 active site residues on the SOM prediction for substrates, in this work, molecular dynamics (MD) simulations and molecular docking were used to predict the SOM of 96 CYP2A6 substrates. Substrates with known SOM were docked into the snapshot structures from MD simulations and the crystal structures of CYP2A6. Compared to the crystal structures, the protein structures obtained from MD simulations showed more accurate prediction for SOM. Our results indicated that the flexibility of the active site of CYP2A6 significantly affects the SOM prediction results. Further analysis for the 40 substrates with definite Km values showed that the prediction accuracy for the low Km substrates is comparable to that by ligand-based methods.

Metabolism Site Prediction Based on Xenobiotic Structural Formulas and PASS Prediction Algorithm

A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms of cytochrome P450. An average IAP (invariant accuracy of prediction) of SOMs calculated by the leave-one-out cross-validation procedure was 0.89 for the developed method. The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. An average IAP of regioselectivity for evaluation sets was 0.83. It was shown that the proposed method exceeds accuracy of SOM prediction by RS-Predictor for CYP 1A2, 2D6, 2C9, 2C19, and 3A4 and is comparable to or better than SMARTCyp for CYP 2C9 and 2D6.

ChemInform Abstract: Open Source Drug Discovery with Bioclipse

AbstractReview: 67 refs.

Open Source Drug Discovery with Bioclipse

We present the open source components for drug discovery that has been developed and integrated into the graphical workbench Bioclipse. Building on a solid open source cheminformatics core, Bioclipse has advanced functionality for managing and visualizing chemical structures and related information. The features presented here include QSAR/QSPR modeling, various predictive solutions such as decision support for chemical liability assessment, site-ofmetabolism prediction, virtual screening, and knowledge discovery and integration. We demonstrate the utility of the described tools with examples from computational pharmacology, toxicology, and ADME. Bioclipse is used in both academia and industry, and is a good example of open source leading to new solutions for drug discovery.

Malleability and Versatility of Cytochrome P450 Active Sites Studied by Molecular Simulations

As the most important phase I drug metabolizing enzymes, the human Cytochromes P450 display an enormous versatility in the molecular structures of possible substrates. Individual isoforms may preferentially bind specific classes of molecules, but also within these classes, some isoforms show remarkable levels of promiscuity. In this work, we try to link this promiscuity to the versatility and malleability of the active site at the hand of examples from our own work. Mainly focusing on the flexibility of protein structures and the presence or absence of water molecules, we establish molecular reasons for observed promiscuity, determine the relevant factors to take into account when predicting binding poses and rationalize the role of individual interactions in the process of ligand binding. A high level of active site flexibility does not only allow for the binding of a large variety of substrates and inhibitors, but also appears to be important to facilitate ligand binding and unbinding.

Open Source Drug Discovery with Bioclipse

We present the open source components for drug discovery that has been developed and integrated into the graphical workbench Bioclipse. Building on a solid open source cheminformatics core, Bioclipse has advanced functionality for managing and visualizing chemical structures and related information. The features presented here include QSAR/QSPR modeling, various predictive solutions such as decision support for chemical liability assessment, site-ofmetabolism prediction, virtual screening, and knowledge discovery and integration. We demonstrate the utility of the described tools with examples from computational pharmacology, toxicology, and ADME. Bioclipse is used in both academia and industry, and is a good example of open source leading to new solutions for drug discovery.

Prediction of sites of metabolism in a substrate molecule, instanced by carbamazepine oxidation by CYP3A4

In drug discovery process, improvement of ADME/Tox properties of lead compounds including metabolic stability is critically important. Cytochrome P450 (CYP) is one of the major metabolizing enzymes and the prediction of sites of metabolism (SOM) on the given lead compounds is key information to modify the compounds to be more stable against metabolism. There are two factors essentially important in SOM prediction. First is accessibility of each substrate atom to the oxygenated Fe atom of heme in a CYP protein, and the other is the oxidative reactivity of each substrate atom. To predict accessibility of substrate atoms to the heme iron, conventional protein-rigid docking simulations have been applied. However, the docking simulations without consideration of protein flexibility often lead to incorrect answers in the case of very flexible proteins such as CYP3A4. In this study, we demonstrated an approach utilizing molecular dynamics (MD) simulation for SOM prediction in which multiple MD runs were executed using different initial structures. We applied this strategy to CYP3A4 and carbamazepine (CBZ) complex. Through 10ns MD simulations started from five different CYP3A4-CBZ complex models, our approach correctly predicted SOM observed in experiments. The experimentally known epoxidized sites of CBZ by CYP3A4 were successfully predicted as the most accessible sites to the heme iron that was judged from a numerical analysis of calculated ΔGbinding and the frequency of appearance. In contrast, the predictions using protein-rigid docking methods hardly provided the correct SOM due to protein flexibility or inaccuracy of the scoring functions. Our strategy using MD simulation with multiple initial structures will be one of the reliable methods for SOM prediction.

Structure-Based Site of Metabolism Prediction for Cytochrome P450 2D6

Realistic representation of protein flexibility in biomolecular simulations remains an unsolved fundamental problem and is an active area of research. The high flexibility of the cytochrome P450 2D6 (CYP2D6) active site represents a challenge for accurate prediction of the preferred binding mode and site of metabolism (SOM) for compounds metabolized by this important enzyme. To account for this flexibility, we generated a large ensemble of unbiased CYP2D6 conformations, to which small molecule substrates were docked to predict their experimentally observed SOM. SOM predictivity was investigated as a function of the number of protein structures, the scoring function, the SOM-heme cutoff distance used to distinguish metabolic sites, and intrinsic reactivity. Good SOM predictions for CYP2D6 require information from the protein. A critical parameter is the distance between the heme iron and the candidate site of metabolism. The best predictions were achieved with cutoff distances consistent with the chemistry relevant to CYP2D6 metabolism. Combination of the new ensemble-based docking method with estimated intrinsic reactivities of substrate sites considerably improved the predictivity of the model. Testing on an independent set of substrates yielded area under curve values as high as 0.93, validating our new approach.

Potentially increasing the metabolic stability of drug candidates via computational site of metabolism prediction by CYP2C9: The utility of incorporating protein flexibility via an ensemble of structures

Cytochrome P450 enzymes are responsible for metabolizing many endogenous and xenobiotic molecules encountered by the human body. It has been estimated that 75% of all drugs are metabolized by cytochrome P450 enzymes. Thus, predicting a compound’s potential sites of metabolism (SOM) is highly advantageous early in the drug development process. We have combined molecular dynamics, AutoDock Vina docking, the neighboring atom type (NAT) reactivity model, and a solvent-accessible surface-area term to form a reactivity–accessibility model capable of predicting SOM for cytochrome P450 2C9 substrates. To investigate the importance of protein flexibility during the ligand-binding process, the results of SOM prediction using a static protein structure for docking were compared to SOM prediction using multiple protein structures in ensemble docking. The results reported here indicate that ensemble docking increases the number of ligands that can be docked in a bioactive conformation (ensemble: 96%, static: 85%) but only leads to a slight improvement (49% vs. 44%) in predicting an experimentally known SOM in the top-1 position for a ligand library of 75 CYP2C9 substrates. Using ensemble docking, the reactivity–accessibility model accurately predicts SOM in the top-1 ranked position for 49% of the ligand library and considering the top-3 predicted sites increases the prediction success rate to approximately 70% of the ligand library. Further classifying the substrate library according to K m values leads to an improvement in SOM prediction for substrates with low K m values (57% at top-1). While the current predictive power of the reactivity–accessibility model still leaves significant room for improvement, the results illustrate the usefulness of this method to identify key protein–ligand interactions and guide structural modifications of the ligand to increase its metabolic stability.