A TALE OF TWO ENZYMES: IDENTIFICATION OF AN UNKNOWN LIGAND BOUND TO CYTOCHROME P450 2A13 AND UNDERSTANDING SUBSTRATE SELECTIVITY OF CYTOCHROME P450 2E1

Cytochrome P450 (CYP) is the predominate superfamily of enzymes responsible for Phase I metabolism of drugs and other xenobiotics. Understanding the structural reasons for the substrate selectivity of these enzymes is important for both pharmacological and toxicological reasons. Two isoforms of interest from this enzyme superfamily that are CYP2A13 and CYP2E1. Cytochrome P450 2A13 (CYP2A13) is a lung specific enzyme known to activate the potent tobacco procarcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) into two carcinogenic metabolites. CYP2A13 has been crystallized and X-ray diffraction experiments illuminated the structure of this enzyme, but with an unknown ligand present in the enzyme active site. This unknown ligand was suspected to be indole but a selective method had to be developed to differentiate among indole and its metabolites in the protein sample. We successfully modified a microbiological colorimetric assay to spectrophotometrically differentiate between indole and a number of possible indole metabolites in nanomolar concentrations by derivatization with p-dimethylaminocinnamaldehyde (DMACA). Further differentiation of indoles was made by mass spectrometry (HPLC-UV/vis-MS/MS) utilizing the chromophore generated in the DMACA conjugation as a UV signature for HPLC detection. The ligand in the crystallized protein was unambiguously identified as unsubstituted indole, which facilitated refinement of two alternate conformations of indole in the CYP2A13 crystal structure active site. Human cytochrome P450 2E1 (CYP2E1) is a xenobiotic metabolizing enzyme that is highly conserved among mammals. In addition to small molecular weight exogenous drugs like the analgesic acetaminophen and the volatile anesthetic halothane, CYP2E1 is also involved in endogenous fatty acid metabolism. To more fully understand the structural factors that contribute to the substrate selectivity of CYP2E1, it has been cocrystallized with two structurally different heme-binding compounds: indazole, a small molecular weight inhibitor and ω-imidazolyl-decanoic acid, a fatty acid analog. Comparison of the CYP2E1 structures shows that only small side chain movements are required for the accommodation of the much larger fatty acid analog. Rotation of the side chain of F298 causes a change in the active site volume from 190 Å3 in the indazole-bound structure to 440 Å3 in the ω-imidazolyl-decanoic acid-bound structure. Future work will be focused on cocrystal structures of CYP2E1 with both longer and shorter chain analogs to better understand the ability of the enzyme to metabolize a variety of fatty acids substrates

Exploring the effects of polymorphic variation on the stability and function of human cytochrome P450 enzymes in silico and in vitro

Includes bibliographical references.Cytochrome P450s are highly polymorphic enzymes responsible for the Phase I metabolism of over 80% of pharmaceutical drugs. Polymorphic variation can result in altered drug efficacy as well as adverse drug reactions so the lack of understanding of the effects of single amino acid substitutions on cytochrome P450 drug metabolism is a major problem for drug development. In order to begin to address this problem, this thesis describes an in silico analysis of over 300 nonsynonymous single nucleotide polymorphisms found across nine of the major human drug metabolising cytochrome P450 isoforms. Information from functional studies - in which regions of the cytochrome P450 structure important for substrate recognition, substrate and product access and egress and interaction with the cytochrome P450 reductase were delineated - was combined with in silico calculations on the effect of mutations on protein stability in order to establish the likely causes of altered drug metabolism observed for cytochrome P450 variants in functional assays carried out to date. This study revealed that 75% of all cytochrome P450 mutations showing altered activity in vitro are either predicted to be damaging to protein structure or are found within regions predicted to be important for catalytic activity. Furthermore, this study showed that 70% of the mutations that showed similar activity to the wild-type enzyme in in vitro studies lie outside of functional regions important for catalytic activity and are predicted to have no effect on protein stability. Based on these results, a cytochrome P450 polymorphic variant map was created that should find utility in predicting the functional effect of uncharacterised variants on drug metabolism. To further test the accuracy of the in silico predictions, in vitro assays were performed on a panel of CYP3A4 and CYP2C9 variants heterogeneously expressed in E.coli. All mutations predicted to alter protein function by stabilising or destabilising the apo-protein structure in silico were found to significantly alter the thermostability of the holo-protein in solution. Thermostability assays also suggest that other mutations may affect stability by disrupting haem binding, changing protein conformation or altering oligomer formation. The utility of a fluorescence-based functional P450 protein microarray platform, previously developed in our laboratory, for generating kinetic data for multiple CYP450 variants in parallel was also examined. Since the microarray platform in its current stage of development was found to be unsuitable for this purpose, kinetic data for the full panel of CYP3A4 and CYP2C9 variants was generated using solution phase assays, revealing several variants with altered catalytic turnover and/or binding affinity for fluorescent substrates

Improvements in Molecular Mechanics Sampling and Energy Models

The process of bringing drugs to market continues to be a slow and expensive affair. And despite recent advances in technology, the cost both in monetary terms and in terms of time between target identification and arrival of a new drug on the market continues to increase. High throughput screening is a first step towards testing a large number of possible bioactive compounds very quickly. However, the space of possible small molecules is limitless, and high throughput screening is limited both by the size of available libraries and the cost of running such a large number of experiments. Therefore, advancements in computational drug screening are necessary in order to maintain the current rate of progress in modern medicine. Computational drug design, or computer assisted drug design, offers a possible way of addressing some of the shortfalls of conventional high throughput screening. Using computational methods, it is possible to estimate parameters such as binding affinity of any small molecule, even those not currently present in any small molecule library, without having to first invest in the often slow and expensive process of finding a synthetic pathway. Computational methods can be used to screen similar molecules, or mutations in small molecule space, seeking to increase binding affinity to the protein target, and thereby efficacy, while simultaneously minimizing binding affinity to other proteins, decreasing cross reactivity, and reducing toxicity and harmful side effects.Computational biology methods of drug research can be broadly classified in a number of different ways. However, one of the most common classifications is according to the methods used to identify possible drug compounds and later optimize those leads. The first broad category is informatics or artificial intelligence based approaches. In these approaches, artificial intelligence methods such as neural networks, support vector machines, and qualitative structure-activity relationships (QSAR) are used to identify chemical or structural properties that contribute heavily to binding affinity. The next category, ligand based approaches, is very useful when there are a large number of known binders for a specific family of proteins. In this approach, the ligands are clustered using a metric of chemical similarity and new compounds which occupy a similar chemical space are likely to also bind strongly with the protein of interest. The final class of methods of computational drug design, and the method explored in this thesis, is the diverse class known as structural methods. These approaches in the most general sense make use of a sampling method to sample a number of protein, or protein-small-molecule interaction conformations and an energy model or scoring function to measure dimensions which would be very difficult and or expensive to measure experimentally. In this thesis, a number of different sampling methods that are applicable to different questions in computational biology are presented. Additionally, an improved algorithm for evaluating implicit solvent effects is presented, and a number of improvements in performance, reliability and utility of the molecular mechanics program used are discussed

Molecular dynamics and virtual screening approaches in drug discovery

Computer-aided drug discovery (CADD) methods are now routinely used in the preclinical phase of drug development. Powerful high-performance computing facilities and the extremely fast CADD methods constantly scale up the coverage of drug-like chemical space achievable in rational drug development. In this thesis, CADD approaches were applied to address several early-phase drug discovery problems. Namely, small molecule binding site detection on a novel target protein, virtual screening (VS) of molecular databases, and characterization of small molecule interactions with metabolic enzymes were studied. Various CADD methods, including molecular dynamics (MD) simulations in mixed solvents, molecular docking, and binding free energy calculations, were employed. Co-solvent MD simulations detected biologically relevant binding sites and provided guidance for screening potential protein-protein interaction inhibitors for a crucial protein of the SARS-CoV-2. VS with fragment- and negative image-based (F-NIB) models identified three active and structurally novel inhibitors of the putative drug target phosphodiesterase 10A. MD simulations and docking provided detailed insights on the effects of active site structural flexibility and variation on the binding and resultant metabolism of small molecules with the cytochrome P450 enzymes. The results presented in this thesis contribute to the increasing evidence that supports employment and further development of CADD approaches in drug discovery. Ultimately, rational drug development coupled with CADD may enable higher quality drug candidates to the human studies in the future, reducing the risk of financially and temporally costly clinical failure. KEYWORDS: Structure-based drug development, Computer-aided drug discovery (CADD), Molecular dynamics (MD) simulation, Virtual screening (VS), Fragmentand negative image-based (F-NIB) model, Structure-activity relationship (QSAR), Cytochrome P450 ligand binding predictionMolekyylidynamiikka- ja virtuaaliseulontamenetelmät lääkeaine-etsinnässä Tietokoneavusteista lääkeaine-etsintää käytetään nykyisin yleisesti prekliinisessä lääketutkimuksessa. Suurteholaskenta ja äärimmäisen nopeat tietokoneavusteiset lääkeaine-etsintämenetelmät mahdollistavat jatkuvasti kattavamman lääkkeenkaltaisten molekyylien kemiallisen avaruuden seulonnan. Tässä väitöskirjassa tietokonepohjaisia menetelmiä hyödynnettiin lääketutkimuksen prekliiniseen vaiheeseen liittyvissä tyypillisissä tutkimusongelmissa. Työhön kuului pienmolekyylien sitoutumisalueiden tunnistus uuden kohdeproteiinin rakenteesta, molekyylitietokantojen virtuaaliseulonta sekä pienmolekyylien ja metabolian entsyymien välisten vuorovaikutusten tietokonemallinnus. Työssä käytettiin useita tietokoneavusteisen lääkeaine-etsinnän menetelmiä, sisältäen molekyylidynamiikkasimulaatiot (MD-simulaatiot) vaihtuvissa liuottimissa, molekulaarisen telakoinnin ja sitoutumisenergian laskennan. Orgaanisen liuottimen ja veden sekoituksessa tehdyt MD-simulaatiot tunnistivat biologisesti merkittäviä sitoutumisalueita SARS-CoV-2:n tärkeästä proteiinista ja ohjasivat infektioon liittyvän proteiini-proteiinivuorovaikutuksen potentiaalisten estäjien etsintää. Virtuaaliseulonnalla tunnistettiin kolme rakenteellisesti uudenlaista tunnetun lääkekehityskohteen, fosfodiesteraasi 10A:n, estäjää hyödyntäen fragmentti- ja negatiivikuvamalleja. MD-simulaatiot ja telakointi tuottivat yksityiskohtaista tietoa sytokromi P450 entsyymien aktiivisen kohdan rakenteen jouston ja muutosten vaikutuksesta pienmolekyylien sitoutumiseen ja metaboliaan. Tämän väitöskirjan tulokset tukevat kasvavaa todistusaineistoa tietokoneavusteisen lääkeaine-etsinnän käytön ja kehityksen hyödyllisyydestä prekliinisessä lääketutkimuksessa. Tietokoneavusteinen lääkeaine-etsintä voi lopulta mahdollistaa korkeampilaatuisten lääkekandidaattien päätymisen ihmiskokeisiin, pienentäen taloudellisesti ja ajallisesti kalliin kliinisen tutkimuksen epäonnistumisen riskiä. AVAINSANAT: Rakennepohjainen lääkeainekehitys, Tietokoneavusteinen lääkeaine-etsintä, Molekyylidynamiikkasimulaatio (MD-simulaatio), Virtuaaliseulonta, Fragmentti- ja negatiivikuvamalli, Rakenne-aktiivisuussuhde, Sytokromi P450 ligandien sitoutumisen ennustu

CREATION OF A BACTERIAL MUTAGENICITY ASSAY HIGHLY SENSITIVE TO DIALKYLNITROSAMINES

Although dialkylnitrosamines are environmentally significant carcinogens, the use of short-term bioassays to assess the mutagenic potential of these compounds remains problematic. The Ames test, a mutagenicity assay based on the reversion of Salmonella typhimurium histidine auxotrophs, is the most widely used bioassay in genetic toxicology, but the traditional Ames tester strains are largely insensitive to dialkylnitrosamine mutagenicity. I have constructed several mutagenicity tester strains that co-express combinations of full-length human cytochrome P450 2E1, rat cytochrome P450 reductase, and human cytochrome b5 in S. typhimurium lacking ogt and ada methyltransferases (YG7104ER, ogt-; and YG7108ER, ogt-, ada-). These new strains are susceptible to dialkylnitrosamine mutagenicity in the absence of an exogenous metabolic activating system (S9 fraction). Mutagenicity is dependent upon the coexpression of P450 2E1 with P450 reductase and is similar or greater than that obtained with the parental strains in the presence of S9 fraction from ethanol-induced rat liver. Coexpressing human cytochrome b5 with cytochrome P450 2E1 and cytochrome P450 reductase potentiates the mutagenicity observed with dialkylnitrosamines. These strains were sensitive to nitrosamines with varying alkyl side chains, including dimethylnitrosamine, diethylnitrosamine, dipropylnitrosamine, and dibutylnitrosamine. Mutagenicity decreased with alkyl chain length, consistent with the stringency of the ada-encoded enzyme for methyl and ethyl DNA adducts. These new strains may prove useful in the evaluation of nitrosamine contamination of food and environmental samples, and may serve as useful tools in investigating the molecular properties of proteins in the cytochrome P450 monooxygenase system

Molecular Similarity and Xenobiotic Metabolism

MetaPrint2D, a new software tool implementing a data-mining approach for predicting sites of xenobiotic metabolism has been developed. The algorithm is based on a statistical analysis of the occurrences of atom centred circular fingerprints in both substrates and metabolites. This approach has undergone extensive evaluation and been shown to be of comparable accuracy to current best-in-class tools, but is able to make much faster predictions, for the first time enabling chemists to explore the effects of structural modifications on a compound’s metabolism in a highly responsive and interactive manner.MetaPrint2D is able to assign a confidence score to the predictions it generates, based on the availability of relevant data and the degree to which a compound is modelled by the algorithm.In the course of the evaluation of MetaPrint2D a novel metric for assessing the performance of site of metabolism predictions has been introduced. This overcomes the bias introduced by molecule size and the number of sites of metabolism inherent to the most commonly reported metrics used to evaluate site of metabolism predictions.This data mining approach to site of metabolism prediction has been augmented by a set of reaction type definitions to produce MetaPrint2D-React, enabling prediction of the types of transformations a compound is likely to undergo and the metabolites that are formed. This approach has been evaluated against both historical data and metabolic schemes reported in a number of recently published studies. Results suggest that the ability of this method to predict metabolic transformations is highly dependent on the relevance of the training set data to the query compounds.MetaPrint2D has been released as an open source software library, and both MetaPrint2D and MetaPrint2D-React are available for chemists to use through the Unilever Centre for Molecular Science Informatics website.----Boehringer-Ingelhie

Doctor of Philosophy

dissertationThe characterization of novel and reactive Phase I metabolites of xenobiotics, such as those frequently produced by P450 enzymes, is an area of interest that has led to increased research efforts during preclinical drug-testing and development. A key interest is improving our understanding of factors that contribute to competing Phase I reaction mechanisms, some of which produce stable products that can be further metabolized and excreted, and others that produce reactive metabolites capable of causing toxicities. Due to the highenergy nature of the P450 catalytic oxyferryl heme species, Compound I, P450 enzymes can also catalyze different oxidation reaction mechanisms, including dehydrogenation reactions. Dehydrogenation reactions are more difficult to predict than the more common P450 oxygenation and dealkylation reactions. Moreover, dehydrogenation mechanisms can compete with hydroxylation mechanisms to produce unstable desaturated electrophilic metabolites capable of forming potentially toxic biomolecular adducts. The work presented here focuses on improving existing computational tools for the prediction of P450 metabolism of two model substrates, raloxifene and 4-hydroxy-tamoxifen. These two compounds are FDA-approved selective estrogen receptor modulators currently used in the treatment of breast cancer. In Chapter 2 the development, iv testing and refinement of molecular mechanics parameters for key species of the heme prosthetic group during the P450 catalytic cycle is presented. It is shown that the assignment of atomic partial charges for key heme species improves the identification of the sites of metabolism of raloxifene by CYP3A4. Building on this work, in Chapter 3 it is shown that despite using these new heme parameters, extensive quantum mechanics calculations to probe substrate reactivity, molecular dynamics of the enzyme structure to find representative active site conformations makes the greatest improvement in the identification of the sites of metabolism for 4-hydroxy-tamoxifen. In summary, this work identifies that heme electrostatics and enzyme conformational dynamics play important roles in enzyme function and that the ability to predict sites of metabolism for P450- substrates requires the integration of both for the improvement of future in silico tools