101 research outputs found

    The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity

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    © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly re-ported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (e.g., quantita-tive structure-activity relationship/QSAR and Molecular Docking) were used to analyse DBZDs identified online by an automated web crawler (NPSfinder®) and to predict their possible activi-ty/affinity on the gamma-aminobutyric acid receptors (GABA-ARs). The computational software MOE was used to calculate 2D QSAR models, perform docking studies on crystallised GABA-A receptors (6HUO, 6HUP) and generate pharmacophore queries from the docking conformational results. 101 DBZDs were identified online by NPSfinder®. The validated QSAR model predicted high biological activity values for 41% of these DBDZs. These predictions were supported by the docking studies (good binding affinity) and the pharmacophore modelling confirmed the im-portance of the presence and location of hydrophobic and polar functions identified by QSAR. This study confirms once again the importance of web-based analysis in the assessment of drug scenarios (DBZDs), and how computational models could be used to acquire fast and reliable in-formation on biological activity for index novel DBZDs, as preliminary data for further investiga-tions.Peer reviewe

    Novel Strategies for Model-Building of G Protein-Coupled Receptors

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    The G protein-coupled receptors constitute still the most densely populated proteinfamily encompassing numerous disease-relevant drug targets. Consequently, medicinal chemistry is expected to pursue targets from that protein family in that hits need to be generated and subsequently optimized towards viable clinical candidates for a variety of therapeutic areas. For the purpose of rationalizing structure-activity relationships within such optimization programs, structural information derived from the ligand's as well as the macromolecule's perspective is essential. While it is relatively straightforward to define pharmacophore hypotheses based on comparative modelling of structurally and biologically characterized low-molecular weight ligands, a deeper understanding of the molecular recognition event underlying, remains challenging, since the principally available amount of experimentally derived structural data on GPCRs is extremely scarse when compared to, e.g., soluble enzymes. In this context, the protein modelling methodologies introduced, developed, optimized, and applied in this thesis provide structural models that are capable of assisting in the development of structural hypotheses on ligand-receptor complexes. As such they provide a valuable structural framework not only for a more detailed insight into ligand-GPCR interaction, but also for guiding the design process towards next-generation compounds which should display enhanced affinity. The model building procedure developed in this thesis systematically follows a hierarchical approach, sequentially generating a 1D topology, followed by a 2D topology that is finally converted into a 3D topology. The determination of a 1D topology is based on a compartmentalization of the linear amino acid sequence of a GPCR of interest into the extracellular, intracellular, and transmembrane sequence stretches. The entire chapter 3 of this study elaborates on the strengths and weaknesses of applying automated prediction tools for the purpose of identifying the transmembrane sequence domains. Based on an once derived 1D topology, a type of in-plane projection structure for the seven transmembrane helices can be derived with the aide of calculated vectorial property moments, yielding the 2D topology. Thorough bioinformatics studies revealed that only a consensus approach based on a conceptual combination of different methods employing a carefully made selection of parameter sets gave reliable results, emphasizing the danger to fully automate a GPCR modelling procedure. Chapter 4 describes a procedure to further expand the 2D topological findings into 3D space, exemplified on the human CCK-B receptor protein. This particular GPCR was chosen as the receptor of interest, since an enormous experimentally derived and structurally relevant data-set was available. Within the computational refinement procedure of constructed GPCR models, major emphasis was laid on the explicit treatment of a non-isotropic solvent environment during molecular mechanics (i.e. energy minimization and molecular dynamics simulations) calculations. The majority of simulations was therefore carried out in a tri-phasic solvent box accounting for a central lipid environment, flanked by two aqueous compartments, mimicking the extracellular and cytoplasmic space. Chapter 5 introduces the reference compound set, comprising low-molecular weight compounds modulating CCK receptors, that was used for validation purposes of the generated models of the receptor protein. Chapter 6 describes how the generated model of the CCK-B receptor was subjected to intensive docking studies employing compound series introduced in chapter 5. It turned out that by applying the DRAGHOME methodology viable structural hypotheses on putative receptor-ligand complexes could be generated. Based on the methodology pursued in this thesis a detailed model of the receptor binding site could be devised that accounts for known structure-activity relationships as well as for results obtained by site-directed mutagenesis studies in a qualitative manner. The overall study presented in this thesis is primarily aimed to deliver a feasibility study on generating model structures of GPCRs by a conceptual combination of tailor-made bioinformatics techniques with the toolbox of protein modelling, exemplified on the human CCK-B receptor. The generated structures should be envisioned as models only, not necessarily providing a detailed image of reality. However, consistent models, when verified and refined against experimental data, deliver an extremely useful structural contextual platform on which different scientific disciplines such as medicinal chemistry, molecular biology, and biophysics can effectively communicate

    Novel Strategies for Model-Building of G Protein-Coupled Receptors

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    The G protein-coupled receptors constitute still the most densely populated proteinfamily encompassing numerous disease-relevant drug targets. Consequently, medicinal chemistry is expected to pursue targets from that protein family in that hits need to be generated and subsequently optimized towards viable clinical candidates for a variety of therapeutic areas. For the purpose of rationalizing structure-activity relationships within such optimization programs, structural information derived from the ligand's as well as the macromolecule's perspective is essential. While it is relatively straightforward to define pharmacophore hypotheses based on comparative modelling of structurally and biologically characterized low-molecular weight ligands, a deeper understanding of the molecular recognition event underlying, remains challenging, since the principally available amount of experimentally derived structural data on GPCRs is extremely scarse when compared to, e.g., soluble enzymes. In this context, the protein modelling methodologies introduced, developed, optimized, and applied in this thesis provide structural models that are capable of assisting in the development of structural hypotheses on ligand-receptor complexes. As such they provide a valuable structural framework not only for a more detailed insight into ligand-GPCR interaction, but also for guiding the design process towards next-generation compounds which should display enhanced affinity. The model building procedure developed in this thesis systematically follows a hierarchical approach, sequentially generating a 1D topology, followed by a 2D topology that is finally converted into a 3D topology. The determination of a 1D topology is based on a compartmentalization of the linear amino acid sequence of a GPCR of interest into the extracellular, intracellular, and transmembrane sequence stretches. The entire chapter 3 of this study elaborates on the strengths and weaknesses of applying automated prediction tools for the purpose of identifying the transmembrane sequence domains. Based on an once derived 1D topology, a type of in-plane projection structure for the seven transmembrane helices can be derived with the aide of calculated vectorial property moments, yielding the 2D topology. Thorough bioinformatics studies revealed that only a consensus approach based on a conceptual combination of different methods employing a carefully made selection of parameter sets gave reliable results, emphasizing the danger to fully automate a GPCR modelling procedure. Chapter 4 describes a procedure to further expand the 2D topological findings into 3D space, exemplified on the human CCK-B receptor protein. This particular GPCR was chosen as the receptor of interest, since an enormous experimentally derived and structurally relevant data-set was available. Within the computational refinement procedure of constructed GPCR models, major emphasis was laid on the explicit treatment of a non-isotropic solvent environment during molecular mechanics (i.e. energy minimization and molecular dynamics simulations) calculations. The majority of simulations was therefore carried out in a tri-phasic solvent box accounting for a central lipid environment, flanked by two aqueous compartments, mimicking the extracellular and cytoplasmic space. Chapter 5 introduces the reference compound set, comprising low-molecular weight compounds modulating CCK receptors, that was used for validation purposes of the generated models of the receptor protein. Chapter 6 describes how the generated model of the CCK-B receptor was subjected to intensive docking studies employing compound series introduced in chapter 5. It turned out that by applying the DRAGHOME methodology viable structural hypotheses on putative receptor-ligand complexes could be generated. Based on the methodology pursued in this thesis a detailed model of the receptor binding site could be devised that accounts for known structure-activity relationships as well as for results obtained by site-directed mutagenesis studies in a qualitative manner. The overall study presented in this thesis is primarily aimed to deliver a feasibility study on generating model structures of GPCRs by a conceptual combination of tailor-made bioinformatics techniques with the toolbox of protein modelling, exemplified on the human CCK-B receptor. The generated structures should be envisioned as models only, not necessarily providing a detailed image of reality. However, consistent models, when verified and refined against experimental data, deliver an extremely useful structural contextual platform on which different scientific disciplines such as medicinal chemistry, molecular biology, and biophysics can effectively communicate

    Part I, Unified Pharmacophore Protein Models of the Benzodiazepine Receptor Subtypes ; Part II, Subtype

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    Part I. New models of unified pharmacophore/receptors have been constructed guided by the synthesis of subtype selective compounds in light of recent developments both in ligand synthesis and structural studies of the binding site itself. The evaluation of experimental data in combination with comparative models of the α1β2γ2, α2β2γ2, α3β2γ2 and α5β2γ2 GABA(A) receptors has led to an orientation of the pharmacophore model within the benzodiazepine binding site (Bz BS). These results not only are important for the rational design of new selective ligands, but also for the identification and evaluation of possible roles which specific residues may have within the benzodiazepine binding pocket. More importantly, the process summarized here may be used as a general template to help scientists develop novel ligands for receptors for which the three dimensional structure has not yet been confirmed by X-ray crystallography or cryo-electron microscopy. Presented here are new models of the α1β2γ2, α2β2γ2, α3β2γ2 and α5β2γ2 GABA(A) receptors which have incorporated homology models built based on the acetylcholine binding protein. These new models will further our ability to understand structural characteristics of ligands which act as agonists, antagonists, or inverse agonists to the Bz BS of the GABA(A) receptor. This approach will also serve as a powerful model for structure based approaches carried out using ligand-protein docking methods. Part II. An effective strategy to alleviate memory deficits would be to enhance memory and cognitive processes by augmenting the impact of acetylcholine released from cholinergic neurons of the hippocampus. Using the included volume pharmacophore presented in Part I, a number of a5 selective compounds were synthesized, notably PWZ-029. PWZ-029 was examined in rats in the passive and active avoidance, spontaneous locomotor activity, elevated plus maze and grip strength tests which are indicative of the effects on memory acquisition, locomotor activity, anxiety, and muscle tone. Improvement of task learning was shown at a dose of 5mg/kg in passive avoidance test without effect on anxiety or muscle tone. Moderate negative modulation at GABA(A) receptors containing the α5 subunit using a moderate inverse agonist such as PWZ-029, is a sufficient condition for eliciting enhanced encoding/consolidation of declarative memory. Using low temperature NMR and X-ray analysis, it was shown that enhanced selectivity and potent in vitro affinity of α5 selective benzodiazepine dimers was possible with aliphatic linkers of 3 to 5 carbons in length. Although originally proposed to enhance solubility, oxygen-containing linkers caused the dimer to fold back on itself leading to the inability of dimers to enter the binding pocket. In addition, studies of a series of PWZ-029 analogs found that the electrostatic potential near the ligands\u27 terminal substituent correlated with its binding selectivity toward the α5β2γ2 versus α1β2γ2 Bzr/GABA(A) ergic isoform. Investigations further found that compound PWZ-029, which exhibits reasonable binding selectivity toward GABA(A) receptors containing the a5 subunit and possesses a favorable electrophysiological profile, was able to attenuate scopolamine induced contextual memory impairment in mice. This compound appears to be useful (Harris, Delorey et al.) for the treatment of cognitive deficits in rodents as well as primates (Rowlett et al.) and may well be a compound for the treatment of patients with Alzheimers disease

    A New Method for Ligand-supported Homology Modelling of Protein Binding Sites: Development and Application to the neurokinin-1 receptor

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    In this thesis, a novel strategy (MOBILE (Modelling Binding Sites Including Ligand Information Explicitly)) was developed that models protein binding-sites simultaneously considering information about the binding mode of bioactive ligands during the homology modelling process. As a result, protein binding-site models of higher accuracy and relevance can be generated. Starting with the (crystal) structure of one or more template proteins, in the first step several preliminary homology models of the target protein are generated using the homology modelling program MODELLER. Ligands are then placed into these preliminary models using different strategies depending on the amount of experimental information about the binding mode of the ligands. (1.) If a ligand is known to bind to the target protein and the crystal structure of the protein-ligand complex with the related template protein is available, it can be assumed that the ligand binding modes are similar in the target and template protein. Accordingly, ligands are then transferred among these structures keeping their orientation as a restraint for the subsequent modelling process. (2.) If no complex crystal structure with the template is available, the ligand(s) can be placed into the template protein structure by docking, and the resulting orientation can then be used to restrain the following protein modelling process. Alternatively, (3.) in cases where knowledge about the binding mode cannot be inferred by the template protein, ligand docking is performed into an ensemble of homology models. The ligands are placed into a crude binding-site representation via docking into averaged property fields derived from knowledge-based potentials. Once the ligands are placed, a new set of homology models is generated. However, in this step, ligand information is considered as additional restraint in terms of the knowledge-based DrugScore protein-ligand atom pair potentials. Consulting a large ensemble of produced models exhibiting di erent side-chain rotamers for the binding-site residues, a composite picture is assembled considering the individually best scored rotamers with respect to the ligand. After a local force-field optimisation, the obtained binding-site models can be used for structure-based drug design

    In Silico and In Vitro Investigation into the Next Generation of New Psychoactive Substances

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    New Psychoactive Substances (NPS) were designed to be legal alternatives to existing established recreational drugs. They have fast become a very popular and up until 2016, NPS were legal, cheap and freely accessible via the internet and high street “head shops”. The rapid expansion in the number of these drugs has reached epidemic proportions, whereby hundreds of NPS have been developed and sold within the last five-year period. As NPS are synthesized in clandestine laboratories there is little to no control in the manufacture, dosage and packaging of these drugs. The public health risks posed by these drugs are therefore far-reaching. Fatalities and severe adverse reactions associated with these compounds have become an ongoing challenge to healthcare services, primarily because these drugs have not previously been abused and therefore there is little pharmacological information available regarding NPS. There are a number of different biological receptors that are implicated in the effects of NPS and the mechanism of action for the majority of these drugs is still largely unknown. It is of great importance to try and establish an understanding of how various classes of NPS interact on a molecular level. In this thesis, structure-based and ligand-based in Silico methodologies were employed to gain a better understanding of how NPS may interact with monoamine transporters (MAT). Key findings included both molecular docking studies and a number of robust and predictive QSAR models for the dopamine and serotonin transporters provided insight into how promiscuity of NPS between the different MAT isoforms could arise. In addition, pharmacophore models were generated to identify chemical entities that were structurally dissimilar to known existing NPS that had the potential to interact with the cannabinoid 1 receptor (CB1) and hence were hypothesised could elicit similar biological responses to known potent synthetic cannabinoids. Thirteen of these compounds were identified and carried forward for in vitro and ex vivo analyses, where preliminary results have shown that two compounds activate the CB1 receptor. Further optimisation of these compounds could yield a novel SC scaffold that was previously unseen. Additionally, the compounds identified and the methodology employed in the generation of these new chemical scaffolds could be used to guide Early Warning Systems (EWS) and facilitate law enforcement with respect to emergent NPS

    QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY

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    G protein coupled receptors (GPCRs) are the largest receptor family in mammalian genomes and are known to regulate wide variety of signals such as ions, hormones and neurotransmitters. It has been estimated that GPCRs represent more than 30% of current drug targets and have attracted many pharmaceutical industries as well as academic groups for potential drug discovery. Cannabinoid (CB) receptors, members of GPCR superfamily, are also involved in the activation of multiple intracellular signal transductions and their endogenous ligands or cannabinoids have attracted pharmacological research because of their potential therapeutic effects. In particular, the cannabinoid subtype-2 (CB2) receptor is known to be involved in immune system signal transductions and its ligands have the potential to be developed as drugs to treat many immune system disorders without potential psychotic side-effects. Therefore, this work was focused on discovering novel CB2 ligands by developing novel quantitative structure-activity relationship (QSAR) methods and performing virtual and experimental screenings. Three novel QSAR methods were developed to predict biological activities and binding affinities of ligands. In the first method, a traditional fragment-based approach was improved by introducing a fragment similarity concept that enhanced the prediction accuracy remarkably. In the second method, pharmacophoric and morphological descriptors were incorporated to derive a novel QSAR regression model with good prediction accuracy. In the third method, a novel fingerprint-based artificial neural network QSAR model was developed to overcome the similar scaffold requirement of many fragment-based and other 3D-QSAR methods. These methods provide a foundation for virtual screening and hit ranking of chemical ligands from large chemical space. In addition, several novel CB2 selective ligands within nM binding affinities were discovered. These ligands were proven to be inverse agonists as validated by functional assays and could be useful probes to study CB2 signaling as well as potential drug candidates for autoimmune disesases

    Discovery and development of novel inhibitors for the kinase Pim-1 and G-Protein Coupled Receptor Smoothened

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    Investigation of the cause of disease is no easy business. This is particularly so when one reflects upon the lessons taught us in antiquity. Prior to the beginning of the last century, diagnosis and treatment of diseases such as cancers was so bereft of hope that there was little physicians could offer in the way of comfort, let alone treatment. One of the major insights from investigations into cancers this century has been that those involved in research leading to treatments are not dealing with a singular malady but multiple families of diseases with different mechanisms and modes of action. Therefore, despite the end game being similar in cancers, that of uncontrolled growth and replication leading to cellular dysfunction, different diseases require different approaches in targeting them. This leads us to a particular broad treatment approach, that of drug design. A drug is, in the classical sense, a small molecule that, upon introduction into the body, interacts with biochemical targets to induce a wider biological effect, ideally with both an intended target and intended effect. The conceptual basis underpinning this `lock-and-key' paradigm was elucidated over a century ago and the primary occupation of those involved in biochemical research has been to determine as much information as possible about both of these protein locks and drug keys. And, as inferred from the paradigm, molecular shape is all-important in determining and controlling action against the most important locks with the most potent and specific keys. The two most important target classes in drug discovery for some time have been protein kinases and G Protein-Coupled Receptors (GPCRs). Both classes of proteins are large families that perform very different tasks within the body. Kinases activate and inactive many cellular processes by catalysing the transfer of a phosphate group from Adenosine Tri-Phosphate (ATP) to other targets. GPCRs perform the job of interacting with chemical signals and communicating them into a biological response. Dysfunction in both types of proteins in certain cells can lead to a loss of biological control and, ultimately, a cancer. Both of kinases and GPCRs have entirely different chemical structures so structural knowledge therefore becomes crucial in any approach targeting cells where dysfunction has occurred. Thus, for this thesis, a member from each class was investigated using a combination of structural approaches. From the kinase class, the kinase Proviral Integration site for MuLV (Pim-1) and from the GPCR class, the cell membrane-bound Smoothened receptor (SMO). The kinase \pimone\ was the target of various approaches in \autoref{chap:three}. Although a heavily studied target from the mid-2000's, there is a paucity of inhibitors targeting residues more remote from structural characteristics that define kinases. Further limiting extension possibilities is that \pimone\ is constitutively active so no inhibitors targeting an inactive state are possible. An initial project (\pone) used the known binding properties of small molecules, or, `fragments' to elucidate structural and dynamic information useful for targeting \pimone. This was followed by three projects, all with the goal of inhibitor discovery, all with different foci. In \ptwo, fragment binding modes from \pone\ provided the basis for the extension and development of drug-like inhibitors with a focus on synthetic feasibility. In contrast, inhibitors were found in \pthree\ via a large-scale public dataset of purchasable molecules that possess drug-like properties. Finally, \pfour\ took the truncated form of a particularly attractive fragment from \pone\ that was crystallised with \pimone, verified its binding mode and then generated extensions with, again, a focus on synthetic feasibility. The GPCR \smo\ has fewer molecular studies and much about its structural behaviour remains unknown. As the most `druggable' protein in the Hedgehog pathway, structural studies have primarily focussed on stabilising its inactive state to prevent signal transduction. Allied to this is that there are generally few inhibitors for \smo\ and the drugs for cancers related to its dysfunction are vulnerable to mutations that significantly reduce their effectiveness or abrogate it entirely. The elucidation of structural information in therefore of high priority. An initial study attempting to identify an unknown molecule from prior experiments led to insights regarding binding characteristics of specific moieties. This was particularly important to understand not just where favourable moieties bind but also sections of the \smo\ binding pocket with unfavourable binding. In both subsequent virtual screens performed in Chapter 4, the primary aim was to find new drug-like inhibitors of \smo\ using large public datasets of commercially-available molecules. The initial screen retrieved relatively few inhibitors so the binding pocket was modified to find a structural state more amenable to small molecule binding. These modifications led to a significant number of new, chemically novel inhibitors for \smo, some structural information useful for future inhibitors and the elucidation of structure-activity relationships useful for inhibitor design. This underpins the idea that structural information is of critical importance in the discovery and design of molecular inhibitors
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