Mouse 11β-Hydroxysteroid Dehydrogenase Type 2 for Human Application: Homology Modeling, Structural Analysis and Ligand-Receptor Interaction

Mouse (m) 11β-hydroxysteroid dehydrogenase type 2 (11βHSD2) was homology-modeled, and its structure and ligand-receptor interaction were analyzed. The modeled m11βHSD2 showed significant 3D similarities to the human (h) 11βHSD1 and 2 structures. The contact energy profiles of the m11βHSD2 model were in good agreement with those of the h11βHSD1 and 2 structures. The secondary structure of the m11βHSD2 model exhibited a central 6-stranded all-parallel β-sheet sandwich-like structure, flanked on both sides by 3-helices. Ramachandran plots revealed that only 1.1% of the amino acid residues were in the disfavored region for m11βHSD2. Further, the molecular surfaces and electrostatic analyses of the m11βHSD2 model at the ligand-binding site exhibited that the model was almost identical to the h11βHSD2 model. Furthermore, docking simulation and ligand-receptor interaction analyses revealed the similarity of the ligand-receptor bound conformation between the m11βHSD2 and h11βHSD2 models. These results indicate that the m11βHSD2 model was successfully evaluated and analyzed. To the best of our knowledge, this is the first report of a m11βHSD2 model with detailed analyses, and our data verify that the mouse model can be utilized for application to the human model to target 11βHSD2 for the development of anticancer drugs

Structure-based drug design of 11β-hydroxysteroid dehydrogenase type 1 inhibitors

The enzyme 11β-Hydroxysteroid Dehydrogenase 1 (11β-HSD1) catalyses the intracellular biosynthesis of the active glucocorticoid cortisol. Tissue specific dysregulation of the enzyme has been implicated in the development of metabolic syndrome and other associated diseases. Experiments with transgenic mice and prototype inhibitors show that inhibition of 11β-HSD1 in visceral adipose tissue and liver leads to a resistance of diet-induced hyperglycemia and a favourable lipid and lipoprotein profile as compared to controls. 11β-HSD1 inhibition has thus been proposed as an effective strategy to decrease intracellular glucocorticoid levels without affecting circulating glucocorticoid levels that are essential for stress responses. The clinical development of selective and potent drugs has therefore become a priority. In this research, a process of virtual screening employing the novel algorithm UFSRAT (Ultra Fast Shape Recognition with Atom Types) was used to discover compounds which had specific physicochemical and spatial atomic parameters deemed essential for inhibition of 11β-HSD1. The top scoring compounds were assayed for inhibitory activity against recombinant human and mouse enzyme, using a fluorescence spectroscopy approach. In addition, HEK-293 cell based assays with either human, mouse or rat enzymes were carried out using a scintillation proximity assay (SPA). The most potent compound competitively inhibited human 11β-HSD1 with a Kiapp value of 51 nM. Recombinant mouse and human enzyme were expressed, purified and characterised and used in a series of ligand binding assays. Further to this, an X-ray crystal structure of mouse 11β-HSD1 in complex with a tight binding inhibitor – carbenoxolone was solved

Using Protein Homology Models for Structure-Based Studies: Approaches to Model Refinement

Homology modeling is a computational methodology to assign a 3-D structure to a target protein when experimental data are not available. The methodology uses another protein with a known structure that shares some sequence identity with the target as a template. The crudest approach is to thread the target protein backbone atoms over the backbone atoms of the template protein, but necessary refinement methods are needed to produce realistic models. In this mini-review anchored within the scope of drug design, we show the validity of using homology models of proteins in the discovery of binders for potential therapeutic targets. We also report several different approaches to homology model refinement, going from very simple to the most elaborate. Results show that refinement approaches are system dependent and that more elaborate methodologies do not always correlate with better performances from built homology models

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery

Cortisol synthesis by primary human keratinocytes

PhDCortisol analogues have been used to treat skin disorders, such as psoriasis and atopic dermatitis, for over 50 years but the ability of normal human keratinocytes to synthesise cortisol has not been reported. Keratinocytes are capable of de novo cholesterol synthesis, they express P450 enzymes that are required for steroidogenesis and can metabolise androgens and estrogens. In addition, steroidogenic acute regulatory protein (StAR) that controls the rate determining step of acute steroidogenesis has been identified in the epidermis. The aim of this thesis was to identify de novo cortisol steroidogenesis by keratinocytes and investigate the function of cortisol in keratinocytes in vitro. Normal epidermis was shown to express three cholesterol transporters that are associated with promoting steroid synthesis; StAR was identified in the basal layer, metastatic lymph node 64 (MLN64) in the suprabasal layers and translocator protein (TSPO) was detected throughout the epidermal layers. In addition, the nuclear receptor DAX1, a negative regulator of StAR, was identified in the cytoplasm of cells that form normal epidermis. Comparatively, the expression of these proteins was altered in psoriasis and atopic dermatitis, where DAX1 was localised to the nucleus of most diseased tissue and StAR was not detected. This suggests that acute steroid synthesis is ablated in these hyperproliferative skin conditions. The ability of normal primary human keratinocytes to synthesise cortisol was investigated. Radioimmunoassay demonstrated keratinocytes were capable of de novo pregnenolone synthesis, which was promoted with the cortisol analogue dexamethasone (dex). Interestingly, 25-hydroxycholesterol, which bypasses StAR, did not further enhance steroid synthesis. This suggests that there is an alternative rate determining step of steroid synthesis in cultured primary keratinocytes. Thin layer chromatography demonstrated keratinocytes could metabolise pregnenolone to progesterone and progesterone to cortisol. Progesterone metabolism to cortisol was also confirmed with liquid chromatography/mass spectroscopy. 3 Dex was shown to maintain keratinocyte viability and was implicated in promoting cellular redox potential. Since redox potential is a critical regulator of steroidogenesis, this observation could provide a mechanism for dex-induced pregnenolone synthesis in cultured keratinocytes. These observations led to a hypothesis that local cortisol synthesis functions to regulate cellular redox potential to prevent cell death as part of a positive feedback system. Therefore, this thesis has identified the cortisol steroidogenic pathway in primary human keratinocytes and a potential functional mechanism for the pathway

Effect of polyphenols on glucoregulatory biomarkers, blood pressure and lipid profile in overweight and obese subjects

This thesis describes a series of in vitro, animal and humans studies conducted with the aim of investigating the effect of polyphenol-rich green coffee bean extract (GCBE) and dark chocolate (DC) on biomarkers of glucose metabolism, lipid profile and blood pressure (BP) in overweight and obese individuals. Green coffee and Theobroma cacao bean extracts were found to be rich in polyphenols and to act as effective free radical scavenging compounds in vitro. A potential role for GCBE in inhibiting pancreatic lipase was identified in vitro. Preliminary human studies revealed a differential effect of GCBE and DC on fasting glucose, total cholesterol, BP and urinary glucocorticoids. Accordingly, consumption of 200mg GCBE containing 90mg chlorogenic acid (CGA) twice daily for 14 days by healthy overweight and obese volunteers reduced systolic BP (P=0.043), urinary free cortisone (P=0.0015) and waist circumference (-0.78cm; P=0.013) but raised salivary cortisone (P=0.042) without significantly affecting capillary fasting glucose, total cholesterol or urinary antioxidant excretion (P>0.05). The ability of CGA to differentially regulate cortisol metabolism was further highlighted in male C57BL6 mice wherein daily administration of a diet containing 0.15% CGA for 17 days marginally increased cortisol in kidney (P=0.108; eta2=0.26) and reduced hepatic cortisol (P=0.219; eta2=0.14). In the preliminary single-blind randomised cross-over DC study, 2-week consumption of 20g DC containing 500mg or 1000mg polyphenols by overweight and obese individuals produced equal reductions in capillary fasting glucose, systolic and diastolic BP. This was further confirmed by the long-term placebo-controlled trial wherein ingestion of 20g DC (500mg polyphenols) for 4 weeks reduced fasting glucose (P=0.028), insulin resistance (P=0.005), systolic (P=0.020), diastolic BP (P=0.008) and improved insulin sensitivity (QUICKI, P=0.04; revised-QUICKI, P=0.026) and urinary antioxidant capacity (total phenolics, P=0.046; ferric-reducing capacity, P=0.048) without significantly affecting lipid profile (P>0.05). A particular contribution of the main study is the finding that overweight and obese individuals respond more effectively to polyphenol-rich DC, compared to lean individuals, but more adversely to polyphenol-deficient placebo. The latter was marked by the rise in fasting insulin, insulin resistance and salivary cortisol. In conclusion, this thesis supports a role for polyphenol-rich GCBE and DC in counteracting overweight and obesity-related complications. The role of GCBE and CGA in modulating glucocorticoid metabolism emerges as a novel and potentially relevant field of research to the prevention of overweight and obesity-related complications.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Bioinformatic studies of the disposition pathways and targets of synthetic drugs and herbal medicines

Drugs are pharmaceutical compounds administered to treat or diagnose disease. In the human body, drugs have specific reactions with molecular structures or drug targets to produce an effect. Some major concerns of current drugs include undesired side effects or toxicity and poor therapeutic response. Drug discovery is a crucial step to produce new and improved drugs with better efficacy and safety profiles to benefit patients. Generally, drug discovery can be improved in 3 aspects: better understanding of diseases, search for new target classes and the use of new and improved biological and chemical tools. Using advanced bioinformatics tools, this project aimed to study: a) genotype-phenotype relations of Phase II drug metabolising enzyme uridine 5’-diphospho-glucuronosyltransferase (UGT); b) disposition pathways and targets of synthetic drugs and herbal medicines; and c) to design a comprehensive drug and herb target database. We used 2 algorithms, Sorting Intolerant From Tolerant (SIFT) and Polymorphism Phenotyping (Polyphen) to predict the genotype-phenotype relationship of UGTs. Results showed that SIFT and Polyphen are good prediction tools with correct prediction rates of 57.1% and 66.7 %, respectively. Using this method, we can screen for polymorphisms of various genes that may potentially cause diseases and altered drug response or toxicity. Further to this, we used Protein ANalysis THrough Evolutionary Relationships (PANTHER) to classify human therapeutic targets of rheumatoid arthritis and non-insulin-dependent diabetes mellitus (NIDDM), and to identify which classes of molecular targets are most targeted by current medications for these common conditions. The results give us a focus on chief target classes and can be useful in the future to identify new therapeutic targets. Then, we explored targets that are associated with herbal compounds. Using berberine as an example, we collected available human target data and via PANTHER analysis, identified its major target classes. Together with this data and known clinical effects of berberine, we discussed the identification of new therapeutic targets and the development of new drugs from herbal medicines. Finally, we summarised key databases for drug and herbal targets and propose to construct a database containing comprehensive data on drug and herbal targets. Initial stages of the database design are discussed. Findings from these studies suggests that: predicting the phenotypic consequence of nsSNPs in human UGTs using computational algorithms may provide further understanding of genetic differences in susceptibility to diseases and drug response and would be useful information for further genotype-phenotype studies; and b) therapeutic targets of rheumatoid arthritis, NIDDM and berberine can be investigated using a computational approach and has important implications in potential target discovery. Furthermore, the study of current therapeutic targets of drugs and herbal medicine can lead a new direction of future targets identification. Our studies show a promising future for the use of bioinformatics tools in the identification of new therapeutic targets and the exploration of novel drugs. This is valuable for the drug discovery and development process and ultimately, improving drug efficacy and safety profiles to benefit patients

Secbase:A Novel Tool to Correlate Secondary Structure Elements with Ligand Binding

The main focus of this work is the development of new methods for computer aided drug design. This development and the final tools are described together with studies to show possible applications and to prove the usefulness of these tools. The first part of this work describes the integration of secondary structure element information together with geometric descriptions into a protein-ligand database (Relibase), which leads to the new modul Secbase. The python-based interface to Relibase (Reliscript) was used to add the information. Furthermore, the C++ core code of Relibase was extended to get access to this data and to add Secbase constraints to the substructure search. This leads to the opportunity to specifiy Secbase constraints within the substructure search accessible through the Relibase webinterface. The motivation for the development of Secbase is guided through two main ideas: Firstly, Secbase should provide means to analyse protein-ligand interactions with respect to secondary structure elements and, secondly, should allow analysis and discovery of functional similarity within related folding patterns. This is based on the knowledge that the function of a protein is often based on the structure and the spatial structure is more conserved in evolution than amino acid sequence. In general, Secbase, in combination with Relibase, can be used for knowledge discovery about the influence of secondary structural elements on protein-ligand interactions and should be valuable for structure based drug design and molecular modelling. Two major analyses were carried out using Reliscript and the Relibase Webinterface. The first analysis revealed some notable trends in hydrogen bonding geometry across the different secondary structural elements. The mean hydrogen bond length of accumulated hydrogen bonds in α-helices and parallel β-sheets decrease with increasing number of helix-turns and number of β-strands, respectively. The cooperative effect, which leads to a decrease of the mean hydrogen bond length, can be explained by a similar directionality of the peptide bond dipole vectors and the backbone hydrogen bonds in α-helices and parallel β-sheets. The second analysis describes a survey of water molecules next to the N-terminus of an α-helix and shows their involvement in ligand binding. Furthermore, the kinked backbone shows interactions between two neighboured backbone amide groups and carboxylate or phosphate groups, respectively. In agreement with theoretical calculations described in the literature, this analysis suggest that the first/last turn of an α-helix is the main source for charge stabilising effects, mainly by providing hydrogen bonds. This is in contrast to the widely used explanantion that the overall dipole of the helix has an influence. The second part of this work deals with turns as an irregular secondary structure element with a hydrogen bond or a specific Cα-Cα distance between the first and the last residue. Because of the irregularity, the classification into subfamilies changed over the last decades with growing data from protein structures and is not completely adapted to the actual data basis, yet. Additionally, there was a lack of an overall classification for all turn families. Therefore, a uniform classification for all normal (COi - NHi+n hydrogen bond), open (a Cαi-Cαi+n distance up to 10 Å) and reverse (NHi - COi+n hydrogen bond) turn families is presented based on current structural data. The emergent self-organizing maps (ESOM) were used to cluster all turn-conformations of a non-redundant protein chain dataset. In combination with β-sheet and helix classification on average 96% of the given protein chain is now successfully classified. The classification can be used for the identification of similar protein domains or structural motifs within different turn families and accordingly for the understanding of protein-ligand and protein-protein interactions. The created turn classification was used to classify the turn conformations within all protein structures. This information was also added to Secbase. Protein sequence-based turn prediction with high accuracy has already confirmed this new categorization based on machine learning methods as consistent and well-defined. Hopefully, this classification will also be supportive for protein fold and structure prediction