Deorphanizing Human Cytochrome P450 Enzymes CYP4A22 and CYP4Z1 through Mechanistic in silico Modeling

Cytochrome P450 (CYP) enzymes are monooxygenases that catalyze the oxidation of structurally diverse substrates and are present in various lifeforms, including humans. Human CYPs catalyze the metabolism of xenobiotics including drugs and are involved in the essential biosynthesis of steroids, vitamins, and lipids. CYP-catalyzed metabolism and biosynthesis has been extensively studied recently, but several CYPs remain understudied despite their potential role in key biotransformation pathways. For these so-called ‘orphaned CYPs’, physiological function and structure are yet unknown, such as for CYP4A22 and 4Z1. CYP4A22 catalyzes the ω-hydroxylation of arachidonic acid to the angiogenic 20-hydroxyeicosatetraenoic acid. CYP4Z1 is overexpressed in breast cancer and other malignancies, which is correlated with tumor progression. Hence, CYP4Z1 is considered a promising breast cancer target that was not previously addressed by small molecule inhibitors. Here, we report our efforts to deorphanize CYP4A22 and 4Z1 together with our experimental partner Prof. Bureik. We were the first to predict the structure of CYP4A22 and 4Z1 by homology modeling and overcame the challenge of low-sequence similarity templates by incorporating substrate activities. We applied substrate docking and 3D pharmacophore modeling to rationalize how the binding site structure determines structure-activity relationships (SAR) trends. The well-known structural flexibility of CYPs was partially accounted for by molecular dynamics simulations. For the first time, enzyme-substrate interactions dynamics were analyzed with our novel dynamic pharmacophore approach, which led to the prediction of key residues. For CYP4A22, a residue influencing ω-hydroxylation (Phe320) and two binding residues (Arg96 and Arg233) were predicted. For CYP4Z1, the key role of Arg487 and assisting role of Asn381 for substrate binding were predicted, which was validated by in vitro mutational studies. The thereby validated CYP4Z1 model and substrate SAR were used in a virtual screening campaign resulting in a new potent and selective CYP4Z1 inhibitor (IC50: 63 ± 19 nM). Taken together, we established an in vitro/in silico deorphanization protocol that shed light on the structure-function relationships of CYP4A22 and 4Z1. This enabled us to discover a potent inhibitor of CYP4Z1 that will allow further studies on the physiological and pathophysiological role of the enzyme and might be further improved to target CYP4Z1 in a new therapeutical approach. Similar workflows could easily be applied to study other neglected enzymes in metabolism and other biotransformation pathways.Cytochrom P450 (CYP)-Enzyme sind Monooxygenasen, die die Oxidation strukturell diverser Substrate katalysieren und in verschiedenen Lebensformen, einschließlich des Menschen, vorkommen. Menschliche CYPs katalysieren den Metabolismus von Xenobiotika einschließlich Arzneistoffen und sind an der essenziellen Biosynthese von Steroiden, Vitaminen und Lipiden beteiligt. CYP-katalysierter Metabolismus und Biosynthese wurden in der Vergangenheit intensiv untersucht, aber einige CYPs sind trotz ihrer potenziellen Rolle in wichtigen Biotransformationswegen noch wenig erforscht. Für diese so genannten „orphaned“ oder „verwaisten“ CYPs, sind physiologische Funktion und Struktur noch unbekannt, wie z.B. CYP4A22 und 4Z1. CYP4A22 katalysiert die ω-Hydroxylierung von Arachidonsäure zu der angiogenen 20-Hydroxyeicosatetraensäure. CYP4Z1 wird bei Brustkrebs und anderen malignen Erkrankungen überexprimiert, was mit der Tumorprogression korreliert ist. Daher wird CYP4Z1 als ein vielversprechendes Brustkrebs-Target angesehen, das bisher nicht durch niedermolekulare Inhibitoren adressiert wurde. Hier berichten wir über unsere Bemühungen, CYP4A22 und 4Z1 zusammen mit unserem experimentellen Partner Prof. Bureik zu deorphanisieren. Wir waren die Ersten, die die Struktur von CYP4A22 und 4Z1 durch Homologiemodellierung vorhersagten und überwanden die Herausforderung der Templates mit geringer Sequenzähnlichkeit, indem wir Substrataktivitäten mit einbezogen. Wir wendeten Substrat-Docking und 3D-Pharmakophor-Modellierung an, um zu rationalisieren, wie die Struktur der Bindungstasche die Trends der Struktur-Aktivitäts-Beziehungen (SAR) bestimmt. Die bekannte strukturelle Flexibilität von CYPs wurde partiell durch Molekulardynamik-Simulationen berücksichtigt. Zum ersten Mal wurde die Dynamik der Enzym-Substrat-Interaktionen mit unserem neuartigen dynamischen Pharmakophor-Ansatz analysiert, was zur Vorhersage von wichtigen Aminosäuren führte. Für CYP4A22 wurde eine Aminosäure, die die ω-Hydroxylierung beeinflusst (Phe320) und zwei Bindungsaminosäuren (Arg96 und Arg233) vorhergesagt. Für CYP4Z1 wurde die Schlüsselrolle von Arg487 und die unterstützende Rolle von Asn381 für die Substratbindung vorhergesagt, welche durch in vitro Mutationsstudien validiert wurde. Das dadurch validierte CYP4Z1-Modell und die Substrat-SAR wurden in einer virtuellen Screening-Kampagne verwendet, die zu einen neuen potenten und selektiven CYP4Z1-Inhibitor führte (IC50: 63 ± 19 nM). Zusammengenommen haben wir ein in vitro/in silico Deorphanisierungsprotokoll etabliert, welches die Struktur-Funktionsbeziehungen von CYP4A22 und 4Z1 beleuchtet. Dies versetzte uns in die Lage einen potenten Inhibitor von CYP4Z1 zu entdecken, der weitere Studien über die physiologische und pathophysiologische Rolle des Enzyms ermöglichen wird und möglicherweise weiter verbessert werden kann, um CYP4Z1 in einem neuen therapeutischen Ansatz zu adressieren. Ähnliche Arbeitsabläufe könnte leicht angewendet werden, um andere vernachlässigte Enzyme im Metabolismus und anderen Biotransformationswegen zu untersuchen

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Aromatase inhibitors and antiepileptic drugs: a computational systems biology analysis

<p>Abstract</p> <p>Background</p> <p>The present study compares antiepileptic drugs and aromatase (CYP19) inhibitors for chemical and structural similarity. Human aromatase is well known as an important pharmacological target in anti-breast cancer therapy, but recent research demonstrates its role in epileptic seizures, as well. The current antiepileptic treatment methods cause severe side effects that endanger patient health and often preclude continued use. As a result, less toxic and more tolerable antiepileptic drugs (AEDs) are needed, especially since every individual responds differently to given treatment options.</p> <p>Methods</p> <p>Through a pharmacophore search, this study shows that a model previously designed to search for new classes of aromatase inhibitors is able to identify antiepileptic drugs from the set of drugs approved by the Food and Drug Administration. Chemical and structural similarity analyses were performed using five potent AIs, and these studies returned a set of AEDs that the model identifies as hits.</p> <p>Results</p> <p>The pharmacophore model returned 73% (19 out of 26) of the drugs used specifically to treat epilepsy and approximately 82% (51 out of 62) of the compounds with anticonvulsant properties. Therefore, this study supports the possibility of identifying AEDs with a pharmacophore model that had originally been designed to identify new classes of aromatase inhibitors. Potential candidates for anticonvulsant therapy identified in this manner are also reported. Additionally, the chemical and structural similarity between antiepileptic compounds and aromatase inhibitors is proved using similarity analyses.</p> <p>Conclusions</p> <p>This study demonstrates that a pharmacophore search using a model based on aromatase inhibition and the enzyme's structural features can be used to screen for new candidates for antiepileptic therapy. In fact, potent aromatase inhibitors and current antiepileptic compounds display significant - over 70% - chemical and structural similarity, and the similarity analyses performed propose a number of antiepileptic compounds with high potential for aromatase inhibition.</p

КОМПЬЮТЕРНЫЙ ДИЗАЙН ПОТЕНЦИАЛЬНЫХ ИНГИБИТОРОВ АРОМАТАЗЫ НА ОСНОВЕ ПРОИЗВОДНЫХ 1,2,4-ТРИАЗОЛА

Computer-aided design of the high-affinity inhibitors of aromatase based on 1,2,4-triazole derivatives was performed by molecular modeling tools. The potential biological activity of the designed compounds was evaluated by molecular docking and quantum chemistry calculations. As a result, six hits that form a coordinate bond with an iron atom of an enzyme hem and effectively interact with its substrate-binding site were identified. The intermolecular interactions appearing in the structural complexes of these ligands with aromatase were analyzed and the enthalpies of their formation were calculated. Based on the data obtained, the identified compounds were suggested to present good scaffolds for the development of novel effective drugs against breast cancer.Методами молекулярного моделирования осуществлен компьютерный дизайн высокоаффинных ингибиторов ароматазы на основе производных 1,2,4-триазола. С помощью молекулярного докинга и квантовой химии проведена оценка потенциальной биологической активности сконструированных соединений. В результате идентифицированы шесть соединений-лидеров, которые образуют координационную связь с атомом железа гема фермента и эффективно взаимодействуют c его субстрат-связывающим сайтом. Выполнен анализ межмолекулярc его субстрат-связывающим сайтом. Выполнен анализ межмолекуляр его субстрат-связывающим сайтом. Выполнен анализ межмолекулярных взаимодействий, реализующихся в структурных комплексах этих лигандов с ароматазой, и рассчитаны энтальпии их образования. На основе полученных данных предсказано, что идентифицированные соединения формируют перспективные базовые структуры для разработки новых эффективных лекарственных препаратов для терапии рака молочной железы

Minireview PHARMACOPHORE AND THREE-DIMENSIONAL QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP METHODS FOR MODELING CYTOCHROME P450 ACTIVE SITES

This paper is available online at http://dmd.aspetjournals.org ABSTRACT: Structure activity relationships (SAR), three-dimensional structure activity relationships (3D-QSAR), and pharmacophores represent useful tools in understanding cytochrome P450 (CYP) active sites in the absence of crystal structures for these human enzymes. These approaches have developed over the last 30 years such that they are now being applied in numerous industrial and academic laboratories solely for this purpose. Such computational approaches have helped in understanding substrate and inhibitor binding to the major human CYPs 1A2, 2B6, 2C9, 2D6, 3A4 as well as other CYPs and additionally complement homology models for these enzymes. Similarly, these approaches may assist in our understanding of CYP induction. This review describes in detail the development of pharmacophores and 3D-QSAR techniques, which are now being more widely used for modeling CYPs; the review will also describe how such approaches are likely to further impact our active site knowledge of these omnipresent and important enzymes. By the end of the 1990s, several reviews had characterized the active site details and physicochemical properties of substrates for the major cytochrome P450 (CYP 1 ) enzymes. These reviews had been gathered from analysis of physicochemical data 1 Abbreviations used are: CYP or P450, cytochrome P450; CoMFA, comparative molecular field analysis; GOLPE, generating optimal linear PLS estimations; PLS, partial least squares; 3D-QSAR, three-dimensional quantitative structure-activity relationship; MS-WHIM, molecular surface weighted holistic invariant molecular. DMD 29:936-944, 2001 Printed in U.S.A. 936 intends to give an overview of the pharmacophore and 3D-QSAR models that have been used to describe P450s and indicate their varying degrees of success. 3D-QSAR and Pharmacophores The development of computational tools has paralleled that of in vitro approaches to understanding and characterizing CYPs. One of the first visual 3D-QSAR computational approaches was comparative molecular field analysis (CoMFA) Until recently, few CYP binding or active site models had been generated using enzyme kinetic data, and these focused primarily on inhibition. Now, however, a considerable number of CYP pharmacophores have appeared in the literature, which presents us with the opportunity to review what is known about several CYPs based on such computational analyses. CYP Models CYP1A2. CYP1A2 is an inducible member of the CYP superfamily, which can be inhibited by some selective serotonin reuptake inhibitors With regard to predicting substrates for CYP1A2, one study has suggested that they are generally neutral or protonated and that they possess a total interaction energy greater than Ϫ40 kcal/mol and a molecular volume lower than 200 Å 3 CYP2A6. To date there has been no published CYP2A6 QSAR; however, the related mouse form, CYP2A5, has been studied. One group analyzed substrate requirements using a graphical method and concluded that bicyclic ring systems with an electron-rich moiety were essential for the 11 molecules analyzed CYP2B6. Many examples of xenobiotics metabolized in part by CYP2B6 have been identified and described in more detail This challenge was answered by The first quantitative QSAR for CYP2C9 was published in 1996 This model was validated by testing 14 new compounds that had K i values ranging from 0.1 to 48 M (Rao et al., 2000). While the initial training set contained mostly coumarin-containing compounds, this validation set contained mostly sulfonamides. Interestingly, the initial model predicted the affinity of the validation set reasonably well, predicting 13 of the 14 compounds within 1 log residual. Finally, when these compounds where included in the training set, the pharmacophore remained essentially the same. In separate experiments, conducted at the same time as the validation study described above, pharmacophore and PLS predictive models where constructed using Catalyst and PLS MS-WHIM, respectively To gain confidence in the pharmacophores generated for CYP2C9, an attempt was made to determine the specific amino acid residues that might be involved in establishing the pharmacophore. Initial docking of the 9(R)-11(S)-cyclocoumarol and visualizing the CoMFA field in a CYP2C9 homology model indicated that two phenylalanine residues, Phe 110 EKINS ET AL CYP2C19. One group has focused on obtaining substrate structure activity relationships for the polymorphic CYP2C19 using inhibitors of omeprazole 5-hydroxylation (Lock et al., 1998a,b). Using mainly benzodiazepines which are N-dealkylated and 3-hydroxylated, it was suggested that these sites and the carbonyl group were important for inhibition. Electron-withdrawing groups were found to further decrease inhibition. As yet, the data for the 14 compounds used in these two studies have not been used to produce a published 3D-QSAR. CYP2D6. Human CYP2D6 is a polymorphic member of the CYP superfamily and is absent in 5 to 9% of the Caucasian population as a result of a recessive inheritance of gene mutations The first substrate models were manual alignments based on substrates containing a basic nitrogen atom at either 5 Å Another small-molecule model for CYP2D6 was derived by The actual positions of the heme moiety and the I-helix containing Asp 301 [derived from a protein homology model of CYP2D6 Recently, a combined pharmacophore and homology model for CYP2D6 has been derived (de Groot et al., 1999a,b). This model consists of a set of two pharmacophores (one for O-dealkylation and oxidation reactions and a second one for N-dealkylation reactions catalyzed by CYP2D6) embedded in a protein homology model based on bacterial CYP crystal structures (de Groot et al., 1999a,b). This model for the first time combines the strengths of pharmacophore models (atom-atom overlap and reproducible starting points) and homology models (steric interactions and the possibility to identify amino acids involved in binding). This model correctly predicted the metabolism of a wide variety of compounds (de Groot et al., 1999a,b). An inhibitor model for CYP2D6 has also been derived. The template of this model was derived by fitting six strong reversible inhibitors of CYP2D6 onto each other . The basic nitrogen atoms were superimposed and the aromatic planes of these inhibitors were fitted coplanar. Consecutively, other inhibitors, such as derivatives of ajmalicine and quinidine, were fitted onto the derived template. The derived preliminary pharmacophore model consisted of a tertiary nitrogen atom (protonated at physiological pH) and a flat hydrophobic region. There also appeared to be two regions in which functional groups with lone pairs were allowed. In one of these regions, these groups caused enhanced inhibitory potency, which was not the case in yet another region . The overall criteria derived for this inhibitor-based small-molecule model were very similar to the criteria for the proposed substrate models of CYP2D6 P450 PHARMACOPHORE AND QSAR MODELS A set of 3D/4D-QSAR pharmacophore models has also been created for competitive inhibitors of CYP2D6 in a manner similar to that described for CYP2B6 and CYP2C9 using Catalyst CYP2E1. CYP2E1 is involved in the metabolism of many toxic and carcinogenic molecules such as low molecular weight solvents and anesthetics. Early on, it was suggested that the active site was restricted due to the limited size of known substrates. A graphical model of the active site topology was derived from reactions of human CYP2E1 with phenyldiazene, 2-naphthyl, and p-biphenylhydrazine. This work indicated that the active site was open above the pyrrole rings A and D of the heme for a height of 10 Å CYP3A4. Smith et al. have described in detail the CYP3A4 active site characteristics (as well as those of the other major mammalian CYPs) based on homology models built using soluble bacterial CYP structures as a template More recently, a pharmacophore for inhibitors of CYP3A4-mediated midazolam 1Ј-hydroxylase was developed that consisted of four features necessary for the inhibition of CYP3A4 To evaluate these 3D-QSAR models, the activity of molecules excluded from the training set was predicted and then compared with those observed by means of a 1 log residual. Eight molecules were selected from the literature with K i (apparent) values. Both of the CYP3A4 K i (apparent) Catalyst models predicted the K i values similarly. Seven of eight best fit predictions were within 1 log unit residual, for both models, and the correct rank ordering of three protease inhibitors was observed Using the same commercially available software, a Catalyst hypothesis for 38 CYP3A4 substrates was generated using literature K m data Analyses of the likely features of activators of CYP3A4 have also been undertaken, as three substrates (carbamazepine, nifedipine, and testosterone) within the 38-molecule training set used in the CYP3A4 pharmacophore were known CYP3A4 autoactivators. A common features analysis of these molecules using the HipHop function within Catalyst generated a pharmacophore illustrating three hydrophobic areas and one hydrogen bond acceptor. The hydrophobic areas were located 4.4 to 7.6 Å from the hydrogen bond acceptor feature, and the sites of metabolism were colocated. Therefore, hydrophobic interactions with the CYP3A4 active site may be more important than hydrogen bonding for these same CYP3A4 substrates CYP19 (Aromatase). The importance of CYPs that metabolize endogenous substrates can be demonstrated by aromatase, which catalyzes the metabolism of androstenedione to estrone, 16␣-hy- EKINS ET AL droxyandrostenedione to estriol, and testosterone to estradiol via the aromatization of the A ring and the removal of the C19 methyl group CYP51 (14␣-Demethylase)

Development, evaluation and application of 3D QSAR Pharmacophore model in the discovery of potential human renin inhibitors

<p>Abstract</p> <p>Background</p> <p>Renin has become an attractive target in controlling hypertension because of the high specificity towards its only substrate, angiotensinogen. The conversion of angiotensinogen to angiotensin I is the first and rate-limiting step of renin-angiotensin system and thus designing inhibitors to block this step is focused in this study.</p> <p>Methods</p> <p>Ligand-based quantitative pharmacophore modeling methodology was used in identifying the important molecular chemical features present in the set of already known active compounds and the missing features from the set of inactive compounds. A training set containing 18 compounds including active and inactive compounds with a substantial degree of diversity was used in developing the pharmacophore models. A test set containing 93 compounds, Fischer randomization, and leave-one-out methods were used in the validation of the pharmacophore model. Database screening was performed using the best pharmacophore model as a 3D structural query. Molecular docking and density functional theory calculations were used to select the hit compounds with strong molecular interactions and favorable electronic features.</p> <p>Results</p> <p>The best quantitative pharmacophore model selected was made of one hydrophobic, one hydrogen bond donor, and two hydrogen bond acceptor features with high a correlation value of 0.944. Upon validation using an external test set of 93 compounds, Fischer randomization, and leave-one-out methods, this model was used in database screening to identify chemical compounds containing the identified pharmacophoric features. Molecular docking and density functional theory studies have confirmed that the identified hits possess the essential binding characteristics and electronic properties of potent inhibitors.</p> <p>Conclusion</p> <p>A quantitative pharmacophore model of predictive ability was developed with essential molecular features of a potent renin inhibitor. Using this pharmacophore model, two potential inhibitory leads were identified to be used in designing novel and future renin inhibitors as antihypertensive drugs.</p

Ligand based pharmacophore modelling of anticancer histone deacetylase inhibitors

Histone deacetylases have emerged as an important therapeutic target for the treatment of cancer. Genome-wide histone hypoacetylation causes many cancers. Recently, inhibitors of histone deacetylases (HDAC) have emerged as an important class of anticancer agents. Various side effectslike myocardium damage and bone marrow depression even leading to cell death have been observed in the treatment of caner cells using HDAC inhibitors. The discovery and development of type-specific HDAC inhibitors is of both research and clinical interests. Ligand based pharmacophore modelling is playing a key role for the identification of ligand features for the particular targets. We present a model for designing the pharmacophore onto the set of 70 compounds of three different classes and two subclasses. The ligand based pharmacophore model has been identified in order to facilitate the discovery of type specific anticancer HDAC inhibitors. The result indicates that the in silico methodsare useful in predicting the biological activity of the compound or compound library by screening it against a predicted pharmacophore. Ligand Scout 2.02 has been used to predict the pharmacophorefeatures for anticancer HDAC inhibitors and the distances between pharmacophore features have been calculated through the software Jmol. The proposed model has been validated by docking the MS275compound into the binding pocket of Human HDAC8. Our discovery will help in the identification of more specific anticancer human HDAC inhibitors