Laura Bonati

Ligand binding and activation of the Ah receptor

The Ah receptor (AhR) is a ligand-dependent transcription factor that can be activated by structurally diverse synthetic and naturally-occurring chemicals. Although a significant amount of information is available with respect to the planar aromatic hydrocarbon AhR ligands, the actual spectrum of chemicals that can bind to and activate the AhR is only now being elucidated. In addition, the lack of information regarding the actual three-dimensional structure of the AhR ligand binding domain (LBD) has hindered detailed analysis of the molecular mechanisms by which these ligands bind to and active AhR signal transduction. In this review we describe the current state of knowledge with respect to naturally occurring AhR ligands and present and discuss the first theoretical model of the AhR LBD based on crystal structures of homologous PAS family members.

Predicting the accuracy of protein-ligand docking on homology models

Ligand–protein docking is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of target protein structures have been overcome by the use of theoretical models, especially those derived by homology modeling techniques. While this greatly extended the use of docking simulations, it also introduced the need for general and robust criteria to estimate the reliability of docking results given the model quality. To this end, a large‐scale experiment was performed on a diverse set including experimental structures and homology models for a group of representative ligand–protein complexes. A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target–template alignment and modeling. The obtained models were scored by a selection of the most used model quality indices. The binding geometries were generated using AutoDock, one of the most common docking programs. An important result of this study is that indeed quantitative and robust correlations exist between the accuracy of docking results and the model quality, especially in the binding site. Moreover, state‐of‐the‐art indices for model quality assessment are already an effective tool for an a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics.

New Aryl Hydrocarbon Receptor Homology Model Targeted To Improve Docking Reliability

The aryl hydrocarbon receptor (AhR) is a ligand-dependent, basic helix-loop-helix Per-ARNT-Sim (PAS) containing transcription factor that can bind and be activated by structurally diverse chemicals, including the toxic environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). As no experimentally determined structures of the AhR ligand binding domain (LBD) are available and previous homology models were only derived from apo template structures, we developed a new model based on holo X-ray structures of the hypoxia-inducible factor 2α (HIF-2α) PAS B domain, targeted to improve the accuracy of the binding site for molecular docking applications. We experimentally confirmed the ability of two HIF-2α crystallographic ligands to bind to the mAhR with relatively high affinity and demonstrated that they are AhR agonists, thus justifying the use of the holo HIF-2α structures as templates. A specific modeling/docking approach was proposed to predict the binding modes of AhR ligands in the modeled LBD. It was validated by comparison of the calculated and the experimental binding affinities of active THS ligands and TCDD for the mAhR and by functional activity analysis using several mAhR mutants generated on the basis of the modeling results. Finally the ability of the proposed approach to reproduce the different affinities of TCDD for AhRs of different species was confirmed, and a first test of its reliability in virtual screening is carried out by analyzing the correlation between the calculated and experimental binding affinities of a set of 14 PCDDs.

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.

A hypothesis on the mechanism of PCDD biological activity based on molecular electrostatic potential modeling. Part 2

We have focused our attention on the electrostatic properties of a set of PCDDs, polychlorinated dibenzo-p-dioxins, of known binding affinities for the Ah receptor. In this paper we analyze the molecular electrostatic potential (MEP) of the six selected isomers, 2,3,6-TrCDD, 1,2,4,7,8-PeCDD, 1,2,3,4-TCDD, 1,2,3,4,7-PeCDD, 1,2,4-TrCDD and 1-MCDD, in order to validate the results previously obtained on a test set of eight PCDDs. A visual analysis of the MEP distributions shows that the stepwise differentiation from the electrostatic characteristics typical of the most active isomers is associated with a decrease in binding affinity; however, the simultaneous occurrence of different trends for different electrostatic requirements for activity does not make for a simple definition of the relationships between MEP patterns and affinities for the whole set of isomers. The information retained in the overall MEP distributions is summed up in a few carefully chosen descriptors, i.e. the MEP values in particular points located around the molecules. A statistical analysis performed by linear and non-linear methods elucidates the electrostatic requirements effective in the recognition process with the Ah receptor; from this there also emerges a physical interpretation of the process. In particular, two different types of electrostatic feature associated with two subsets of PCDD isomers appear to be recognized by the Ah receptor.

The combustion of municipal solid wastes and PCDD and PCDF emissions. Part 2. PCDD and PCDF in stack gases

Abstract Literature data on the presence of PCDDs and PCDFs in the feeding as well as in the emissions of municipal solid waste incinerators have been collected and incorporated in a data base. The presence of PCDDs and PCDFs in MSW and their possible sources are discussed. The congener profiles of PCDD and PCDF have been identified and used as fingerprints for the comparison of the various samples. The possibility that PCDDs and PCDFs present in MSW and RDF come from commercial 5CP, as well as from products which use 5CP or its sodium salt in the manufacturing process, is shown here.

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.

Modeling and prediction of molecular properties. Theory of Grid-Weighted Holistic Invariant Molecular (G-WHIM) descriptors

Abstract Recently proposed three-dimensional molecular indices (WHIM descriptors) have been used to search for quantitative structure-activity and structure-property relationships for several classes of compounds. WHIM descriptors contain information about the whole molecular structure in terms of size, shape, symmetry and atom distribution. These indices are calculated from the Cartesian coordinates of the atoms weighted in different manners and represent a very general approach to describe molecules in a unitary conceptual framework. In this paper the WHIM descriptor approach is extended to treat interaction scalar fields. The new G-WHIM (grid-weighted holistic invariant molecular) descriptors are calculated from the coordinates of the grid points where the field has been evaluated, each being weighted by the field value. The fields considered in the proposed example are related to non-bonding, electrostatic, and H-bonding interaction energies, evaluated by classical potentials. Two different sets of descriptors are obtained, for negative and positive potential values separately. The information contained in the whole grid is synthesized in a few parameters to be used in structure-activity relationship studies, thus avoiding difficulties related to chemical information spread out over a great number of grid points and thereby overcoming the problem of the dependence of the results upon molecule alignment. The interpretability of the results is quite evident and is defined by the same mathematical properties of the algorithm used for their calculation. The models obtained confirm the great modeling power of the G-WHIM descriptors.

MinSet: a general approach to derive maximally representative database subsets by using fragment dictionaries and its application to the SCOP database

MOTIVATION The size of current protein databases is a challenge for many Bioinformatics applications, both in terms of processing speed and information redundancy. It may be therefore desirable to efficiently reduce the database of interest to a maximally representative subset. RESULTS The MinSet method employs a combination of a Suffix Tree and a Genetic Algorithm for the generation, selection and assessment of database subsets. The approach is generally applicable to any type of string-encoded data, allowing for a drastic reduction of the database size whilst retaining most of the information contained in the original set. We demonstrate the performance of the method on a database of protein domain structures encoded as strings. We used the SCOP40 domain database by translating protein structures into character strings by means of a structural alphabet and by extracting optimized subsets according to an entropy score that is based on a constant-length fragment dictionary. Therefore, optimized subsets are maximally representative for the distribution and range of local structures. Subsets containing only 10% of the SCOP structure classes show a coverage of >90% for fragments of length 1-4. AVAILABILITY http://mathbio.nimr.mrc.ac.uk/~jkleinj/MinSet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A selected bibliography on PCDD and PCDF formation

Abstract A list of papers appeared in the worldwide literature in the period 1970-1987 is reported. The papers are classified according to a list of subjects which outlines the most significant aspects in the study of PCDD and PCDF formation.