Qiancheng Shen

Site of metabolism prediction for six biotransformations mediated by cytochromes P450

MOTIVATION One goal of metabolomics is to define and monitor the entire metabolite complement of a cell, while it is still far from reach since systematic and rapid approaches for determining the biotransformations of newly discovered metabolites are lacking. For drug development, such metabolic biotransformation of a new chemical entity (NCE) is of more interest because it may profoundly affect its bioavailability, activity and toxicity profile. The use of in silico methods to predict the site of metabolism (SOM) in phase I cytochromes P450-mediated reactions is usually a starting point of metabolic pathway studies, which may also assist in the process of drug/lead optimization. RESULTS This article reports the Cytochromes P450 (CYP450)-mediated SOM prediction for the six most important metabolic reactions by incorporating the use of machine learning and semi-empirical quantum chemical calculations. Non-local models were developed on the basis of a large dataset comprising 1858 metabolic reactions extracted from 1034 heterogeneous chemicals. For validation, the overall accuracies of all six reaction types are higher than 0.81, four of which exceed 0.90. In further receiver operating characteristic (ROC) analyses, each of the SOM model gave a significant area under curve (AUC) value over 0.86, indicating a good predicting power. An external test was made on a previously published dataset, of which 80% of the experimentally observed SOMs can be correctly identified by applying the full set of our SOM models. AVAILABILITY The program package SOME_v1.0 (Site Of Metabolism Estimator) developed based on our models is available at http://www.dddc.ac.cn/adme/myzheng/SOME_1_0.tar.gz.

Estimation of acute oral toxicity in rat using local lazy learning.

BackgroundAcute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD50, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of LD50. Unfortunately, it is difficult to accurately predict compound LD50 using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes.ResultsIn this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop LD50 prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients R2 of 0.712 on a test set containing 2,896 compounds.ConclusionEncouraged by the promising results, we expect that our consensus LLL model of LD50 would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus.

Non-Covalent Interactions with Aromatic Rings: Current Understanding and Implications for Rational Drug Design

Non-covalent interactions like hydrogen bonding, hydrophobic interactions and salt bridges, have been our primary focus in designing and optimizing drugs. Recently, there is mounting evidence that non-covalent interactions involving aromatic rings are also potent forces for the recognition between small drug-like compounds and their targets. Understanding of these interactions and their physical origin is of significant interest for improving the current drug design strategy. Hence, numerous efforts have been devoted to elucidating the structural, geometrical, energetic, and thermodynamic properties of these interactions, which include π-π, cation-π and anion-πinteractions. In this review, we established a framework to systematically understand the structural basis and physicochemical properties of the aromatic interactions at the binding interface of protein-ligand complexes. Firstly, we presented an introduction including the definition, universality, energy components, geometry conformations and substituent effects of these interactions. Secondly, we retrospected the widely employed computational approaches for studying these interactions, including quantum mechanical calculations and crystallographic data mining. Finally, we illustrated with several representative protein-ligand systems to show how the aromatic interactions contribute to the design and optimization of ligand in both affinity and specificity.

Knowledge-Based Scoring Functions in Drug Design: 3. A Two-Dimensional Knowledge-Based Hydrogen-Bonding Potential for the Prediction of Protein–Ligand Interactions

Hydrogen bonding is a key contributor to the molecular recognition between ligands and their host molecules in biological systems. Here we develop a novel orientation-dependent hydrogen bonding potential based on the geometric characteristics of hydrogen bonds observed in 44,585 protein-ligand complexes. We find a close correspondence between the empirical knowledge and the energy landscape inferred from the distribution of HBs. A scoring function based on the resultant hydrogen-bonding potentials discriminates native protein-ligand structures from incorrectly docked decoys with remarkable predictive power.

Binding sensitivity of adefovir to the polymerase from different genotypes of HBV: molecular modeling, docking and dynamics simulation studies

Aim:To investigate the molecular mechanisms underlying the influence of DNA polymerase from different genotypes of hepatitis B virus (HBV) on the binding affinity of adefovir (ADV).Methods:Computational approaches, including homology modeling, docking, MD simulation and MM/PBSA free energy analyses were used.Results:Sequence analyses revealed that residue 238 near the binding pocket was not only a polymorphic site but also a genotype-specific site (His238 in genotype B; Asn238 in genotype C). The calculated binding free-energy supported the hypothesis that the polymerase from HBV genotype C was more sensitive to ADV than that from genotype B. By using MD simulation trajectory analysis, binding free energy decomposition and alanine scanning, some energy variation in the residues around the binding pocket was observed. Both the alanine mutations at residues 236 and 238 led to an increase of the energy difference between genotypes C and B (ΔΔGC–B), suggesting that these residues contributed to the genotype-associated antiviral variability with regard to the interaction with ADV.Conclusion:The results support the hypothesis that the HBV genotype C polymerase is more sensitive to ADV than that from genotype B. Moreover, residue N236 and the polymorphic site 238 play important roles in contributing to the higher sensitivity of genotype C over B in the interaction with ADV.

Estimation of Carcinogenicity Using Molecular Fragments Tree

Carcinogenicity is an important toxicological endpoint that poses high concern to drug discovery. In this study, we developed a method to extract structural alerts (SAs) and modulating factors of carcinogens on the basis of statistical analyses. First, the Gaston algorithm, a frequent subgraph mining method, was used to detect substructures that occurred at least six times. Then, a molecular fragments tree was built and pruned to select high-quality SAs. The p-value of the parent node in the tree and that of its children nodes were compared, and the nodes that had a higher statistical significance in binomial tests were retained. Finally, modulating factors that suppressed the toxic effects of SAs were extracted by three self-defining rules. The accuracy of the 77 SAs plus four SA/modulating factor pairs model for the training set, and the test set was 0.70 and 0.65, respectively. Our model has higher predictive ability than Benignis model, especially in the test set. The results highlight that this method is preferable in terms of prediction accuracy, and the selected SAs are useful for prediction as well as interpretation. Moreover, our method is convenient to users in that it can extract SAs from a database using an automated and unbiased manner that does not rely on a priori knowledge of mechanism of action.

Computational Models for Predicting Interactions with Membrane Transporters

Membrane transporters, including two members: ATP-binding cassette (ABC) transporters and solute carrier (SLC) transporters are proteins that play important roles to facilitate molecules into and out of cells. Consequently, these transporters can be major determinants of the therapeutic efficacy, toxicity and pharmacokinetics of a variety of drugs. Considering the time and expense of bio-experiments taking, research should be driven by evaluation of efficacy and safety. Computational methods arise to be a complementary choice. In this article, we provide an overview of the contribution that computational methods made in transporters field in the past decades. At the beginning, we present a brief introduction about the structure and function of major members of two families in transporters. In the second part, we focus on widely used computational methods in different aspects of transporters research. In the absence of a high-resolution structure of most of transporters, homology modeling is a useful tool to interpret experimental data and potentially guide experimental studies. We summarize reported homology modeling in this review. Researches in computational methods cover major members of transporters and a variety of topics including the classification of substrates and/or inhibitors, prediction of protein-ligand interactions, constitution of binding pocket, phenotype of non-synonymous single-nucleotide polymorphisms, and the conformation analysis that try to explain the mechanism of action. As an example, one of the most important transporters P-gp is elaborated to explain the differences and advantages of various computational models. In the third part, the challenges of developing computational methods to get reliable prediction, as well as the potential future directions in transporter related modeling are discussed.

In Silico Prediction of Cytochrome P450-Mediated Site of Metabolism (SOM)

Drug metabolism is a major consideration for modifying drug clearance and also a primary source for drug metabolite- induced toxicity. Cytochromes P450 (CYPs) are the major enzymes involved in drug metabolism and bioactivation, accounting for almost 75% of the total drug metabolism. Predicting the sites of cytochrome P450-mediated metabolism of drug-like molecules using in silico methods would be highly beneficial and time efficient. An ideal system would enable researchers to make a confident elimination decision based purely on the structure of a new compound. In this review, several tools and models for predicting probable site of metabolism (SOM) have been compared and discussed. The methods are generally based on enzyme structure, ligand structure, and combined methods. Although all the methods have certain accuracy and considerable progress has been made, the results of the calculations still need careful inspection.

SOMEViz: A Web Service for Site of Metabolism Estimating and Visualizing

Phase I metabolism is an important consideration in drug discovery because it profoundly affects the toxicity and activity profile of a drug candidate. In these metabolic processes, CYP450 family is responsible for the majority of biotransformation events. However, it is still an important challenge to predict sites of metabolism (SOM) of a new chemical entity due to the complex reaction mechanism and variety in CYP450 enzymes. SOMEViz is an online service designed for predicting and visualizing human cytochromes P450 (CYP450)-mediated sites of metabolism (SOM) of a molecule, on the basis of a previously reported model. The service provides an access for predicting sites of metabolism of molecules with reasonable accuracy, and predicted results are shown in a user-friendly as well as interactive way, which may help chemists explore metabolism properties of chemicals in the early stage of drug discovery. The web-based GUI of SOMEViz offers user a straightforward way to manage and visualize the sites of metabolism (SOM) prediction results. The service and examples are available free of charge at http://www.dddc.ac.cn/some.