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

Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies

Computational Ligand-Based CNS Therapeutic Design: The Search for Novel-Scaffold Norepinephrine Transporter Inhibitors

Monoamine transporter (MAT) proteins are responsible for regulating cellular signal transduction through control of neurotransmitter reuptake in the synapse, and are therefore relevant to diseases including addiction, psychosis, anxiety and depression. MATs, specifically the serotonin transporter (SERT or 5-HTT), norepinephrine transporter (NET), and dopamine transporter (DAT), serve as the principal targets for antidepressant drugs, such as SSRIs (selective serotonin reuptake inhibitors), NRIs (norepinephrine reuptake inhibitors) and TCAs (tricyclic antidepressants), as well as psychostimulant drugs of abuse such as cocaine and the amphetamines. Due to a lack of crystallographic MAT data, it is unclear as to which of two MAT protein ligand binding sites these drugs bind, hindering knowledge of the specific binding modes of MAT ligands. In this study an in silico pharmacophore model was created using a ligand-based method aimed at drug screening for the ability to specifically inhibit NET, using Molecular Operating Environment software. A group of four structurally-diverse compounds with high NET binding affinities comprised the training set used to generate the model. A test set, which included ten compounds with a range of known NET affinities, served in the validation of the model. The constructed pharmacophore model selected all high affinity NET inhibitors and one relatively inactive compound from the test set. Following model validation, the ZINC small molecule structural database was virtually screened to identify novel MAT inhibitor candidates. Hit compounds were ranked by an overlay score, which calculated how well novel compounds aligned to the original training set alignment. Six top-ranking compounds were purchased and evaluated via in vitro pharmacology to determine the binding affinity at the MATs. Although no significant inhibition was observed at the MATs, compound AC-1 showed a 15% inhibition at the DAT in radioligand binding assays. This result suggests that with further refinement of key pharmacophore features or alteration of the AC-1 structure, more potent MAT inhibitors could be discovered. Pharmacophore-based drug design has become one of the most important tools in drug discovery. Using the molecular modeling approaches described in this study, it is possible to rationally design novel and more selective central nervous system drugs

Tailoring Toll-like Receptor 8 Ligands for Balancing Immune Response and Inflammation

Toll-like receptors (TLRs) play a central role in innate immunity by recognising invading pathogens and host-derived danger signals and initiating the inflammatory response. Aberrant TLR response is involved in the pathogenesis of cancers, infections, autoimmune disorders and allergic diseases. Therefore, TLRs represent attractive targets for novel therapeutic agents. The PhD project's main research aim is to discover novel small molecule modulators of Toll-like receptor 8 (TLR8) and understand their mechanisms of action using computational approaches. TLR8 crystal structure is solved, and several modulators are known from previous drug screens. Therefore, TLR8 is a promising target for rational computer-aided development of novel drug candidates. In the initial phase of the project, the main goal was to study relevant structural features in available crystal structures of TLR8. The focus was on the dimerisation interface because of its role in the binding of ligands and subsequent activation of the receptor. Additionally, we studied the conservation of the relevant structural features across the closely related TLRs. The second part shifts the focus to the binding of the small molecules to TLR8. We investigated interactions between the known ligands and TLR8 and used it to develop the most plausible 3D pharmacophore model. Subsequently, we employed the developed 3D pharmacophore model in virtual screening to identify novel modulators of TLR8. We identified a pyrimidine-based compound that inhibits TLR8-mediated signalling in the micromolar concentration range. The potent anti-inflammatory and dose-dependent response has been confirmed in a series of derivatives of this initial virtual hit, which allowed for a detailed elucidation of structure-activity relationships (SAR) and more precise description of the binding mode. Conclusively, we have developed a novel and promising pyrimidine-based TLR8 inhibitors in silico and confirmed their biological activity, selectivity and low cytotoxicity in vitro. Results from the study on TLR8 represent a solid basis for the future design of small molecule TLR modulators as novel therapeutic agents for modulating immune response and inflammation.Toll-like Rezeptoren (TLRs) spielen eine zentrale Rolle in angeborenen Immunsystem, indem sie eindringende Pathogene sowie endogene Gefahrensignale erkennen und Entzündungsreaktionen einleiten. TLRs sind an der Pathogenese von Krebserkrankungen, Infektionen, Autoimmunerkrankungen und allergischen Erkrankungen beteiligt. Aus diesem Grund stellen TLRs attraktive Ziele für neue, niedermolekulare Wirkstoffe dar. Das Hauptziel dieses Promotionsprojekts ist die Entdeckung neuer niedermolekularer Modulatoren des Toll-like-Rezeptors 8 (TLR8) und das Verständnis ihrer Wirkmechanismen mit Hilfe computergestützter Ansätze. Die Kristallstruktur von TLR8 ist verfügbar und mehrere Modulatoren sind aus früheren Wirkstoffscreens bekannt. Daher ist TLR8 ein vielversprechendes Ziel für die rationale computergestützte Entwicklung neuer Wirkstoffkandidaten. Am Beginn des Projekts bestand das Hauptziel darin, relevante strukturelle Merkmale in den verfügbaren Kristallstrukturen von TLR8 zu untersuchen. Der Fokus lag dabei auf dem Dimerisierungsbereich, da dieser eine wichtige Rolle bei der Bindung von Liganden und der anschließenden Aktivierung des Rezeptors spielt. Zusätzlich untersuchten wir die Konservierung der relevanten Strukturmerkmale über die eng verwandten TLRs hinweg. Der zweite Teil verlagert den Fokus auf die Bindung kleiner Moleküle an TLR8. Wir untersuchten die Interaktionen zwischen den bekannten Liganden und TLR8 und entwickelten daraus systemtisch ein 3D-Pharmakophormodell. Anschließend setzten wir das entwickelte 3D-Pharmakophormodell im virtuellen Screening ein, um neuartige Modulatoren des TLR8 zu identifizieren. Wir identifizierten ein Pyrimidin-Analogon, das die TLR8- vermittelte Signalweiterleitung im mikromolaren Konzentrationsbereich hemmt. Die potente entzündungshemmende und dosisabhängige Wirkung wurde in einer kleinen Serie von Analoga bestätigt. Schließlich optimierten wir die identifizierten Pyrimidinverbindungen weiter, was eine detailliertere Struktur-Aktivitäts-Analyse und eine genauere Aufklärung des Bindungsmodus ermöglichte. Zusammenfassend haben wir neuartige und vielversprechende TLR8-Inhibitoren auf Pyrimidinbasis in silico entwickelt und ihre in vitro biologische Aktivität, Selektivität und geringe Zytotoxizität bestätigt. Die Ergebnisse der Studie zu TLR8 helfen uns, die Prozesse zu verstehen, die für ein erfolgreiches Wirkstoffdesign auch bei anderen TLR notwendig sind und stellen eine gute Ausgangsbasis dar, um in Zukunft optimierte, niedermolekulare TLR- Modulatoren zu entwickeln und damit Entzündung und die Immunreaktion effizient zu modulieren

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued e orts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature

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

Pocket optimization and its application to identify small-molecule inhibitors of protein-protein interactions

Because of their ubiquitous nature in many cellular processes, modulating protein-protein interactions offers tremendous therapeutic potential. However, protein-protein interactions remain a difficult class of drug targets, as most protein interaction sites have not evolved to bind small molecules. Indeed, some protein interaction sites are thought to be simply not amenable to binding any small molecule at all. Other sites feature small molecule binding pockets that simply are not present in the unbound or protein-bound conformations, making structure-based drug discovery difficult. Sometimes, inhibitors bind to multiple family members with high affinity, causing toxicity. In this dissertation I seek to address many of these challenges, by developing methodologies to assess the druggability of a target, assess the selectivity of known inhibitors, identify conformations that are sampled uniquely by a single protein, and identify inhibitors of protein-protein interactions. To assess druggability, I developed the “pocket optimization” protocol which uses a biasing potential to create an ensemble of conformations that contain pockets at a specified location on the protein surface. I showed that low-resolution, low energy inhibitor shapes are encoded at druggable sites and sampled through low-energy fluctuations, whereas they are not present at random sites on protein surfaces. To assess selectivity and screen for inhibitors, I developed “exemplars”, representations of a pocket based on the perfect “non-physical” complementary ligand, allowing the comparison of pocket shapes independent of protein sequence. I predicted the selectivity of an array of inhibitors to a related family of proteins by comparing the exemplars from the known small-molecule bound conformation to the ensemble of exemplars from a “pocket optimized” ensemble. I identified distinct conformations that could be targeted for identifying selective inhibitors de novo by comparing ensembles of exemplars from related family members to one another. Finally, I developed a screening protocol that uses the speed of exemplar versus small molecule comparisons to screen very large compound libraries against ensembles of distinct, “pocket optimized” pocket conformations