Molecular evolution of adrenoceptors and dopamine receptors: implications for the binding of catecholamines.

We derived homology models for all human catecholamine-binding GPCRs (CABRs; the alpha-1, alpha-2, and beta-adrenoceptors and the D1-type and D2-type dopamine receptor) using the bovine rhodopsin-11-cis-retinal X-ray structure. Interactions were predicted from the endogenous ligands norepinephrine or dopamine and from the binding site and were used to optimize receptor-ligand interactions. Similar binding modes in the complexes agree with a large "binding core" conserved across the CABRs, that is, D3.32, V(I)3.33, T3.37, S5.42, S(A/C)5.43, S5.46, F6.51, F6.52, and W6.48. Model structures and docking simulations suggest that extracellular loop 2 could provide a common attachment point for the ligands' beta-hydroxyl via a hydrogen bond donated by the main-chain NH group of residue xl2.52. The modeled CABRs and docking modes are in good agreement with published experimental studies. Complementarity between the ligand and the binding site suggests that the bovine rhodopsin structure is a suitable template for modeling agonist-bound CABRs.

A priori contact preferences in molecular recognition.

A molecular interaction library modeling favorable non-bonded interactions between atoms and molecular fragments is considered. In this paper, we represent the structure of the interaction library by a network diagram, which demonstrates that the underlying prediction model obtained for a molecular fragment is multi-layered. We clustered the molecular fragments into four groups by analyzing the pairwise distances between the molecular fragments. The distances are represented as an unrooted tree, in which the molecular fragments fall into four groups according to their function. For each fragment group, we modeled a group-specific a priori distribution with a Dirichlet distribution. The group-specific Dirichlet distributions enable us to derive a large population of similar molecular fragments that vary only in their contact preferences. Bayes' theorem then leads to a population distribution of the posterior probability vectors referred to as a "Dickey-Savage"-density. Two known methods for approximating multivariate integrals are applied to obtain marginal distributions of the Dickey-Savage density. The results of the numerical integration methods are compared with the simulated marginal distributions. By studying interactions between the protein structure of cyclohydrolase and its ligand guanosine-5'-triphosphate, we show that the marginal distributions of the posterior probabilities are more informative than the corresponding point estimates.

Model structures of alpha-2 adrenoceptors in complex with automatically docked antagonist ligands raise the possibility of interactions dissimilar from agonist ligands.

Antagonist binding to alpha-2 adrenoceptors (alpha2-ARs) is not well understood. Structural models were constructed for the three human alpha2-AR subtypes based on the bovine rhodopsin X-ray structure. Twelve antagonist ligands (including covalently binding phenoxybenzamine) were automatically docked to the models. A hallmark of agonist binding is the electrostatic interaction between a positive charge on the agonist and the negatively charged side chain of D3.32. For antagonist binding, ion-pair formation would require deviations of the models from the rhodopsin structural template, e.g., a rotation of TM3 to relocate D3.32 more centrally within the binding cavity, and/or creation of new space near TM2/TM7 such that antagonists would be shifted away from TM5. Thus, except for the quinazolines, antagonist ligands automatically docked to the model structures did not form ion-pairs with D3.32. This binding mode represents a valid alternative, whereby the positive charge on the antagonists is stabilized by cation-pi interactions with aromatic residues (e.g., F6.51) and antagonists interact with D3.32 via carboxylate-aromatic interactions. This binding mode is in good agreement with maps derived from a molecular interaction library that predicts favorable atomic contacts; similar interaction environments are seen for unrelated proteins in complex with ligands sharing similarities with the alpha2-AR antagonists.

Conserved structural, pharmacological and functional properties among the three human and five zebrafish alpha 2-adrenoceptors.

1. Zebrafish has five distinct alpha(2)-adrenoceptors. Two of these, alpha(2Da) and alpha(2Db), represent a duplicated, fourth alpha(2)-adrenoceptor subtype, while the others are orthologue of the human alpha(2A)-, alpha(2B)- and alpha(2C)-adrenoceptors. Here, we have compared the pharmacological properties of these receptors to infer structural determinants of ligand interactions. 2. The zebrafish alpha(2)-adrenoceptors were expressed in Chinese hamster ovary cells and tested in competitive ligand binding assays and in a functional assay (agonist-stimulated [(35)S]GTPgammaS binding). The affinity results were used to cluster the receptors and, separately, the ligands using both principal component analysis and binary trees. 3. The overall ligand binding characteristics, the order of potency and efficacy of the tested agonists and the G-protein coupling of the zebrafish and human alpha(2)-adrenoceptors, separated by approximately 350 million years of evolution, were found to be highly conserved. The binding affinities of the 20 tested ligands towards the zebrafish alpha(2)-adrenoceptors are generally comparable to those of their human counterparts, with a few compounds showing up to 40-fold affinity differences. 4. The alpha(2A) orthologues and the zebrafish alpha(2D) duplicates clustered as close pairs, but the relationships between the orthologues of alpha(2B) and alpha(2C) were not clearly defined. Applied to the ligands, our clustering methods segregated the ligands based on their chemical structures and functional properties. As the ligand binding pockets formed by the transmembrane helices show only minor differences among the alpha(2)-adrenoceptors, we suggest that the second extracellular loop--where significant sequence variability is located --might contribute significantly to the observed affinity differences.

A Bayesian molecular interaction library.

We describe a library of molecular fragments designed to model and predict non-bonded interactions between atoms. We apply the Bayesian approach, whereby prior knowledge and uncertainty of the mathematical model are incorporated into the estimated model and its parameters. The molecular interaction data are strengthened by narrowing the atom classification to 14 atom types, focusing on independent molecular contacts that lie within a short cutoff distance, and symmetrizing the interaction data for the molecular fragments. Furthermore, the location of atoms in contact with a molecular fragment are modeled by Gaussian mixture densities whose maximum a posteriori estimates are obtained by applying a version of the expectation-maximization algorithm that incorporates hyperparameters for the components of the Gaussian mixtures. A routine is introduced providing the hyperparameters and the initial values of the parameters of the Gaussian mixture densities. A model selection criterion, based on the concept of a 'minimum message length' is used to automatically select the optimal complexity of a mixture model and the most suitable orientation of a reference frame for a fragment in a coordinate system. The type of atom interacting with a molecular fragment is predicted by values of the posterior probability function and the accuracy of these predictions is evaluated by comparing the predicted atom type with the actual atom type seen in crystal structures. The fact that an atom will simultaneously interact with several molecular fragments forming a cohesive network of interactions is exploited by introducing two strategies that combine the predictions of atom types given by multiple fragments. The accuracy of these combined predictions is compared with those based on an individual fragment. Exhaustive validation analyses and qualitative examples (e.g., the ligand-binding domain of glutamate receptors) demonstrate that these improvements lead to effective modeling and prediction of molecular interactions.

A dissimilarity matrix between protein atom classes based on Gaussian mixtures.

MOTIVATION: Previously, Rantanen et al. (2001; J. Mol. Biol., 313, 197-214) constructed a protein atom-ligand fragment interaction library embodying experimentally solved, high-resolution three-dimensional (3D) structural data from the Protein Data Bank (PDB). The spatial locations of protein atoms that surround ligand fragments were modeled with Gaussian mixture models, the parameters of which were estimated with the expectation-maximization (EM) algorithm. In the validation analysis of this library, there was strong indication that the protein atom classification, 24 classes, was too large and that a reduction in the classes would lead to improved predictions. RESULTS: Here, a dissimilarity (distance) matrix that is suitable for comparison and fusion of 24 pre-defined protein atom classes has been derived. Jeffreys' distances between Gaussian mixture models are used as a basis to estimate dissimilarities between protein atom classes. The dissimilarity data are analyzed both with a hierarchical clustering method and independently by using multidimensional scaling analysis. The results provide additional insight into the relationships between different protein atom classes, giving us guidance on, for example, how to readjust protein atom classification and, thus, they will help us to improve protein--ligand interaction predictions. CONTACT: vira@utu.fi