Domenico Fraccalvieri

Comparative analysis of homology models of the AH receptor ligand binding domain: verification of structure-function predictions by site-directed mutagenesis of a nonfunctional receptor.

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.

Conformational and functional analysis of molecular dynamics trajectories by self-organising maps.

BackgroundMolecular dynamics (MD) simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs) were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems. We present a novel strategy to analyse and compare conformational ensembles of protein domains using a two-level approach that combines SOMs and hierarchical clustering.ResultsThe conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cαs Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions.ConclusionsThe use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to conformational ensembles from other sources.

Specific Ligand Binding Domain Residues Confer Low Dioxin Responsiveness to AHR1β of Xenopus laevis

The aryl hydrocarbon receptor (AHR) is a Per-ARNT-Sim (PAS) family protein that mediates the toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in vertebrates. Frogs are remarkably insensitive to TCDD, and AHRs from Xenopus laevis bind TCDD with low affinity. We sought to identify structural features of X. laevis AHR1β associated with low TCDD sensitivity. Substitution of the entire ligand binding domain (LBD) with the corresponding sequence from mouse AHR(b-1) dramatically increased TCDD responsiveness in transactivation assays. To identify the amino acid residues responsible, we constructed a comparative model of the AHR1β LBD using homologous domains of PAS proteins HIF2α and ARNT. The model revealed an internal cavity with dimensions similar to those of the putative binding cavity of mouse AHR(b-1), suggesting the importance of side chain interactions over cavity size. Of residues with side chains clearly pointing into the cavity, only two differed from the mouse sequence. When A354, located within a conserved β-strand, was changed to serine, the corresponding mouse residue, the EC50 for TCDD decreased more than 15-fold. When N325 was changed to serine, the EC50 decreased 3-fold. When the mutations were combined, the EC50 decreased from 18.6 to 0.8 nM, the value nearly matching the TCDD sensitivity of mouse AHR. Velocity sedimentation analysis confirmed that mutant frog AHRs exhibited correspondingly increased levels of TCDD binding. We also assayed mutant AHRs for responsiveness to a candidate endogenous ligand, 6-formylindolo[3,2-b]carbazole (FICZ). Mutations that increased sensitivity to TCDD also increased sensitivity to FICZ. This comparative study represents a novel approach to discerning fundamental information about the structure of AHR and its interactions with biologically important agonists.

Artificial Neural Networks for Efficient Clustering of Conformational Ensembles and their Potential for Medicinal Chemistry

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.

Functional annotation of the mesophilic-like character of mutants in a cold-adapted enzyme by self-organising map analysis of their molecular dynamics.

Multiple comparison of the Molecular Dynamics (MD) trajectories of mutants in a cold-adapted α-amylase (AHA) could be used to elucidate functional features required to restore mesophilic-like activity. Unfortunately it is challenging to identify the different dynamic behaviors and correctly relate them to functional activity by routine analysis. We here employed a previously developed and robust two-stage approach that combines Self-Organising Maps (SOMs) and hierarchical clustering to compare conformational ensembles of proteins. Moreover, we designed a novel strategy to identify the specific mutations that more efficiently convert the dynamic signature of the psychrophilic enzyme (AHA) to that of the mesophilic counterpart (PPA). The SOM trained on AHA and its variants was used to classify a PPA MD ensemble and successfully highlighted the relationships between the flexibilities of the target enzyme and of the different mutants. Moreover the local features of the mutants that mostly influence their global flexibility in a mesophilic-like direction were detected. It turns out that mutations of the cold-adapted enzyme to hydrophobic and aromatic residues are the most effective in restoring the PPA dynamic features and could guide the design of more mesophilic-like mutants. In conclusion, our strategy can efficiently extract specific dynamic signatures related to function from multiple comparisons of MD conformational ensembles. Therefore, it can be a promising tool for protein engineering.

Ginsenosides are novel naturally-occurring aryl hydrocarbon receptor ligands.

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.

An aryl hydrocarbon receptor from the salamander ambystoma mexicanum exhibits low sensitivity to 2,3,7,8-tetrachlorodibenzo -p -dioxin

Structural features of the aryl hydrocarbon receptor (AHR) can underlie species- and population-specific differences in its affinity for 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). These differences often explain variations in TCDD toxicity. Frogs are relatively insensitive to dioxin, and Xenopus AHRs bind TCDD with low affinity. Weak TCDD binding results from the combination of three residues in the ligand-binding domain: A354 and A370, and N325. Here we sought to determine whether this mechanism of weak TCDD binding is shared by other amphibian AHRs. We isolated an AHR cDNA from the Mexican axolotl (Ambystoma mexicanum). The encoded polypeptide contains identical residues at positions that confer low TCDD affinity to X. laevis AHRs (A364, A380, and N335), and homology modeling predicts they protrude into the binding cavity. Axolotl AHR bound one-tenth the TCDD of mouse AHR in velocity sedimentation analysis, and in transactivation assays, the EC50 for TCDD was 23 nM, similar to X. laevis AHR1β (27 nM) and greater than AHR containing the mouse ligand-binding domain (0.08 nM). Sequence, modeled structure, and function indicate that axolotl AHR binds TCDD weakly, predicting that A. mexicanum lacks sensitivity toTCDD toxicity. We hypothesize that this characteristic of axolotl and Xenopus AHRs arose in a common ancestor of the Caudata and Anura.

Identification of potential aryl hydrocarbon receptor ligands by virtual screening of industrial chemicals

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 3 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 6445 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.

Self organizing maps to efficiently cluster and functionally interpret protein conformational ensembles

The wide range of protein biological functions, such as enzymatic activity, ligand- and protein-proteininteractions and allosteric regulation, is strictly related to their flexibility and dynamics[1]. To modelthe influence of protein motions across this broad spectrum of events Molecular Dynamics (MD) sim-ulations are now routinely used. The identification of the most functionally relevant conformations isgenerally done by grouping the conformations according to a criterion of geometrical similarity andpopular choices include hierarchical clustering, single, complete and average linkage and k-means [10].These geometrical approaches rely on the assumption that the identified conformational states also corre-spond to the energetic states [10]. Good candidates to improve this match are Artificial Neural Networks(ANNs) which are capable to discover the relationships between the measured variables only from theavailable dataset [9]. Among the class of ANNs, successfully applied to artificial life systems [2], SelfOrganizing Maps (SOMs) [6] represent a particularly powerful data driven model that has been widelyapplied for exploration and clustering of high-dimensional datasets [9, 6]. We recently developed anapproach which combines SOMs and hierarchical clustering to efficiently compare conformational en-sembles obtained from multiple MD simulations of proteins [3]. To reliably apply the SOM analysis tothese specific input data we identified and optimized a small number of SOM parameters. In particular,we confirmed that the map size is a crucial parameter and that a well-selected number of neurons iscrucial both to reduce the computational cost of the analysis and to provide an intermediate topologicalrepresentation of the input conformational space [3]. As a result the original MD trajectories can berepresented by the few conformations that best represent the clusters obtained. Here we make a step fur-ther; the proposed approach consists of processing all the atom positions of each conformation won bya given SOM neuron using a similarity network. Different similarity measures between atoms behaviorare used to compile a similarity matrix which is inputted to a network model [8]. Network algorithmsare then used to automatically discover and interpret the behavior of the original protein conformationalensembles exploiting the atomic coordinates enclosed in the SOM neuron.