Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work

Study of ligand-based virtual screening tools in computer-aided drug design

Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested in silico with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates. One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes. In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.Siirretty Doriast

Scaffold searching: automated identification of similar ring systems for the design of combinatorial libraries

Rigid ring systems can be used to position receptor-binding functional groups in 3D space and they thus play an increasingly important role in the design of combinatorial libraries. This paper discusses the use of shape-similarity methods to identify ring systems that are structurally similar to, and aligned with, a user-defined target ring system. These systems can be used as alternative scaffolds for the construction of a combinatorial library

Molecular Modeling and Experimental Studies on Ligand Recognition in the LPA5 G Protein-Coupled Receptor

Lysophosphatidic acid (LPA) is a phospholipid growth factor mediating numerous biological effects such as platelet aggregation, mast cell activation, cell differentiation, cell migration, and cell survival by acting on specific LPA G protein-coupled receptors. Currently there are nine LPA receptors identified in the literature, LPA1-9. LPA1-3 are members of the endothelial differentiation gene (EDG) family and share approximately 50% sequence identity at the primary sequence level. LPA4-9 are structurally distinct from the EDG receptors with LPA5 sharing approximately 30% sequence identity with LPA4 at the primary sequence level. Due to the emerging role of LPA5 in human platelet activation, cancer, and neuropathic pain, a thorough characterization of LPA5is needed for the development of compounds to serve as starting points for anti-thrombotic and anti-cancer therapies as well as to inhibit neuropathic pain. In this dissertation we describe LPA5 pharmacophore model development and performance, LPA5 homology model evaluation and optimization through docking and site-directed mutagenesis studies, and structure-activity relationships (SAR) analysis at LPA5. Docking simulations were performed with the LPA5 homology model to computationally identify residues involved in ligand recognition. Pharmacophore modeling was performed to identify compounds with functional groups necessary for receptor inhibition to serve as starting points for therapeutic lead discovery. Our pharmacophore models identified weak partial antagonists and we validated headgroup recognition in alkyl-LPA (AGP 18:1), octadecenylthiophosphate (OTP 18:1), and oleyl-LPA (LPA 18:1). Specifically we proved three cationic residues to be involved in headgroup recongition: R78 (R2.60), R261 (R6.62), and R276 (R7.32). Furthermore we confirmed F71 (F2.53), F101 (F3.32), and M105 (M3.36) as three important residues involved in hydrophobic interactions with AGP, OTP, and LPA ligands. Also, we confirmed an alkyl-LPA preference in LPA5 relative to acyl-LPA. The SAR results suggests that the LPA5 binding pocket exhibits a bend that better accomadates cis relative to tran aslkenes located nine carbons from the headgroup, and that surrounding regions of the binding pocket are less bent, disfavoring recognition of ligands with cis double bonds located closer to or farther from the headgroup

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

Sobiva omaduste profiiliga ühendite tuvastamine keemiliste struktuuride andmekogudest

Keemiliste ühendite digitaalsete andmebaaside kasutuselevõtuga kaasneb vajadus leida neist arvutuslikke vahendeid kasutades sobivate omadustega molekule. Probleem on eriti huvipakkuv ravimitööstuses, kus aja- ja ressursimahukate katsete asendamine arvutustega, võimaldab märkimisväärset säästu. Kuigi tänapäevaste arvutusmeetodite piiratud võimsuse tõttu ei ole lähemas tulevikus võimalik kogu ravimidisaini protsessi algusest lõpuni arvutitesse ümber kolida, on lugu teine, kui vaadelda suuri andmekogusid. Arvutusmeetod, mis töötab teadaoleva statistilise vea piires, visates välja mõne sobiva ühendi ja lugedes mõni ekslikult aktiivseks, tihendab lõppkokkuvõttes andmekomplekti tuntaval määral huvitavate ühendite suhtes. Seetõttu on ravimiarenduse lihtsamate ja vähenõudlikkumade etappide puhul, nagu juhtühendite või ravimikandidaatide leidmine, edukalt võimalik rakendada arvutuslikke vahendeid. Selline tegevus on tuntud virtuaalsõelumisena ning käesolevasse töösse on sellest avarast ja kiiresti arenevast valdkonnast valitud mõningad suunad, ning uuritud nende võimekust ja tulemuslikkust erinevate projektide raames. Töö tulemusena on valminud arvutusmudelid teatud tüüpi ühendite HIV proteaasi vastase aktiivsuse ja tsütotoksilisuse hindamiseks; koostatud uus sõelumismeetod; leitud potentsiaalsed ligandid HIV proteaasile ja pöördtranskriptaasile; ning kokku pandud farmakokineetiliste filtritega eeltöödeldud andmekomplekt – mugav lähtepositsioon edasisteks töödeks.With the implementation of digital chemical compound libraries, creates the need for finding compounds from them that fit the desired profile. The problem is of particular interest in drug design, where replacing the resource-intensive experiments with computational methods, would result in significant savings in time and cost. Although due to the limitations of current computational methods, it is not possible in foreseeable future to transfer all of the drug development process into computers, it is a different story with large molecular databases. An in silico method, working within a known error margin, is still capable of significantly concentrating the data set in terms of attractive compounds. That allows the use of computational methods in less stringent steps of drug development, such as finding lead compounds or drug candidates. This approach is known as virtual screening, and today it is a vast and prospective research area comprising of several paradigms and numerous individual methods. The present thesis takes a closer look on some of them, and evaluates their performance in the course of several projects. The results of the thesis include computational models to estimate the HIV protease inhibition activity and cytotoxicity of certain type of compounds; a few prospective ligands for HIV protease and reverse transcriptase; pre-filtered dataset of compounds – convenient starting point for subsequent projects; and finally a new virtual screening method was developed