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We have developed a virtual screening procedure to identify potential ligands to the aryl hydrocarbon receptor (AhR) among a set of industrial chemicals. AhR is a key target for dioxin-like compounds, which is related to these compounds’ potential to induce cancer and a wide range of endocrine and immune system related effects. The virtual screening procedure included an initial filtration aiming at identifying chemicals with structural similarities to 66 known AhR binders, followed by three enrichment methods run in parallel. These include two ligand-based methods (structural fingerprints and nearest neighbor analysis) and one structure-based method using an AhR homology model. A set of 6,445 commonly used industrial chemicals was processed, and each step identified unique potential ligands. Seven compounds were identified by all three enrichment methods, and these compounds included known activators and suppressors of AhR. Only approximately 0.7% (41 compounds) of the studied industrial compounds was identified as potential AhR ligands and among these, 28 compounds have to our knowledge not been tested for AhR-mediated effects or have been screened with low purity. We suggest assessment of AhR-related activities of these compounds and in particular 2-chlorotrityl chloride, 3-p-hydroxyanilino-carbazole, and 3-(2-chloro-4-nitrophenyl)-5-(1,1-dimethylethyl)-1,3,4-oxadiazol-2(3H)-one., Originally included in thesis in manuscript form

The HLA–DQ2.5 receptors bind gluten-derived peptides and present them to the T cells in the intestinal mucosa thus inducing the development of immune responses typical of the celiac disease. On the basis of the X-ray structure of the domain of HLA–DQ2.5 bound to the DQ2.5-glia-a1a epitope, fifteen peptides were designed with the aim of lowering the epitope binding affinity, thus reducing the autoimmune response. Hydroxylation of Pro residues was proposed as a suitable functionalization given that both enzymatic and chemical synthetic methods are available. Then, a computational study on the effects of Pro hydroxylation on HLA–DQ2.5 binding was performed by molecular docking. A docking protocol able to reproduce the binding geometry of the known crystallographic complex was set up and applied to the designed DQ2.5-glia-a1a analogues. Among them, the one including four di-hydroxylated Pro residues was predicted to lower the binding affinity to the greatest extent. Therefore, the same functionalization was also computationally tested for other celiac disease relevant epitopes, DQ2.5-glia-a1b and DQ2.5- glia-a2, and their ability for inhibiting the binding to HLA–DQ2.5 was confirmed. On this basis, these hydroxylated peptides are expected to significantly affect the gluten activity involved in celiac disease and, after experimental validation, a synthetic method will be developed for introducing this gluten modification directly in flour. The proposed approach is a promising tool to study the binding of other gliadin and glutenin derived T-cell epitopes as well as their variants.

An approach that combines Self-Organizing maps, hierarchical clustering and network components is presented, aimed at comparing protein conformational ensembles obtained from multiple Molecular Dynamic simulations. As a first result the original ensembles can be summarized by using only the representative conformations of the clusters obtained. In addition the network components analysis allows to discover and interpret the dynamic behavior of the conformations won by each neuron. The results showed the ability of this approach to efficiently derive a functional interpretation of the protein dynamics described by the original conformational ensemble, highlighting its potential as a support for protein engineering

An approach to efficiently cluster protein conformational ensembles from multiple MD simulations is presented. It is based on the combined use of Self-Organizing Maps and hierarchical clustering. In addition to the analysis of the representative conformation of each cluster, the application of the network components to the conformations in each neuron of the map is proposed. The results confirmed the ability of the approach to efficiently extract specific dynamic signatures related to biological function and highlighted the potential of this tool as a support for protein engineering.

The aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor that mediates many of the biological and toxicological actions of structurally diverse chemicals. In this study, we examined the ability of a series of ginsenosides extracted from ginseng, a traditional Chinese medicine, to bind to and activate/inhibit the AHR and AHR signal transduction. Utilizing a combination of ligand and DNA binding assays, molecular docking and reporter gene analysis, we demonstrated the ability of selected ginsenosides to directly bind to and activate the guinea pig cytosolic AHR, and to stimulate/inhibit AHR-dependent luciferase gene expression in a recombinant guinea pig cell line. Comparative studies revealed significant species differences in the ability of ginsenosides to stimulate AHR-dependent gene expression in guinea pig, rat, mouse and human cell lines. Not only did selected ginsenosides preferentially activate the AHR from one species and not others, mouse cell line was also significantly less responsive to these chemicals than rat and guinea pig cell lines, but the endogenous gene CYP1A1 could still be inducted in mouse cell line. Overall, the ability of these compounds to stimulate AHR signal transduction demonstrated that these ginsenosides are a new class of naturally occurring AHR agonists.

The biological function of proteins is strictly related to their molecular flexibility and dynamics: enzymatic activity, protein-protein interactions, ligand binding and allosteric regulation are important mechanisms involving protein motions. Computational approaches, such as Molecular Dynamics (MD) simulations, are now routinely used to study the intrinsic dynamics of target proteins as well as to complement molecular docking approaches. These methods have also successfully supported the process of rational design and discovery of new drugs. Identification of functionally relevant conformations is a key step in these studies. This is generally done by cluster analysis of the ensemble of structures in the MD trajectory. Recently Artificial Neural Network (ANN) approaches, in particular methods based on Self-Organising Maps (SOMs), have been reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data-mining problems. In the specific case of conformational analysis, SOMs have been successfully used to compare multiple ensembles of protein conformations demonstrating a potential in efficiently detecting the dynamic signatures central to biological function. Moreover, examples of the use of SOMs to address problems relevant to other stages of the drug-design process, including clustering of docking poses, have been reported. In this contribution we review recent applications of ANN algorithms in analysing conformational and structural ensembles and we discuss their potential in computer-based approaches for medicinal chemistry

The aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor that mediates the biological and toxic effects of a wide variety of structurally diverse chemicals, including the toxic environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). While significant interspecies differences in AHR ligand binding specificity, selectivity, and response have been observed, the structural determinants responsible for those differences have not been determined, and homology models of the AHR ligand-binding domain (LBD) are available for only a few species. Here we describe the development and comparative analysis of homology models of the LBD of 16 AHRs from 12 mammalian and nonmammalian species and identify the specific residues contained within their ligand binding cavities. The ligand-binding cavity of the fish AHR exhibits differences from those of mammalian and avian AHRs, suggesting a slightly different TCDD binding mode. Comparison of the internal cavity in the LBD model of zebrafish (zf) AHR2, which binds TCDD with high affinity, to that of zfAHR1a, which does not bind TCDD, revealed that the latter has a dramatically shortened binding cavity due to the side chains of three residues (Tyr296, Thr386, and His388) that reduce the amount of internal space available to TCDD. Mutagenesis of two of these residues in zfAHR1a to those present in zfAHR2 (Y296H and T386A) restored the ability of zfAHR1a to bind TCDD and to exhibit TCDD-dependent binding to DNA. These results demonstrate the importance of these two amino acids and highlight the predictive potential of comparative analysis of homology models from diverse species. The availability of these AHR LBD homology models will facilitate in-depth comparative studies of AHR ligand binding and ligand-dependent AHR activation and provide a novel avenue for examining species-specific differences in AHR responsiveness.

The first use of a two-level approach that combines Self-Organizing Maps (SOMs) and hierarchical clustering for the comparison of protein dynamics is presented. The method was developed and tested on the SH3 domain and a group of its mutants and successfully recovered information about the differences in the domain flexibilities that affect their biological function. The approach was then extended to other study cases to analyse the role of conformational flexibility in modulating the domain functions.