Peter Milanov

Activity profiles of dalargin and its analogues in μ‐, δ‐ and κ‐opioid receptor selective bioassays

To elucidate the structural features ensuring action of [D‐Ala2, Leu5]‐enkephalyl‐Arg (dalargin), a series of dalargin analogues were tested for their effectiveness in depressing electrically‐evoked contractions of the guinea‐pig myenteric plexus‐longitudinal muscle preparations (μ‐ and κ‐opioid receptors) and the vasa deferentia of the hamster (δ‐opioid receptors), mouse (μ‐, δ‐ and κ‐opioid receptors), rat (similar to μ‐opioid receptors) and rabbit (κ‐opioid receptors). The naloxone KB values in the myenteric plexus were also obtained. [L‐Ala2]‐dalargin was 19 times less potent than dalargin, and its pharmacological activity was peptidase‐sensitive. The ratio of δ‐activity to μ‐activity for [L‐Ala2]‐dalargin was 6.78, and KB was 7.9 nM. This emphasizes the role that D‐configuration of Ala2 plays in determining the active folding of dalargin molecule as well as in conferring resistance to peptidases. [Met5]‐dalargin was equipotent to dalargin in the myenteric plexus, but was more potent in the vasa deferentia of hamster and mouse (KB=5.5 nM). Leu5 and the interdependence of Leu5 and D‐Ala2 are of importance for the selectivity of dalargin for μ‐opioid receptors. Dalarginamide was more potent and selective for μ‐opioid receptors than dalargin, whilst dalarginethylamide, though equipotent to dalarginamide in the myenteric plexus, was more potent at δ‐opioid receptors (KB=5.0 nM). [D‐Phe4]‐dalarginamide and N‐Me‐[D‐Phe4]‐dalarginamide were inactive indicating the contribution of L‐configuration of Phe4 to the pharmacological potency of dalargin. N‐Me‐[L‐Phe4]‐dalarginamide possessed the highest potency and selectivity for μ‐opioid receptors (the ratio of δ‐activity to μ‐activity was 0.00053; KB=2.6 nM). The CONH2 terminus combined with the N‐methylation of L‐Phe4 increased the potency and selectivity of dalargin for μ‐opioid receptors.

Long-Lasting Effects of Oxy- and Sulfoanalogues of l-Arginine on Enzyme Actions

Arginine residues are very important for the structure of proteins and their action. Arginine is essential for many natural processes because it has unique ionizable group under physiological conditions. Numerous mimetics of arginine were synthesized and their biological effects were evaluated, but the mechanisms of actions are still unknown. The aim of this study is to see if oxy- and sulfoanalogues of arginine can be recognized by human arginyl-tRNA synthetase (HArgS)—an enzyme responsible for coupling of L-arginine with its cognate tRNA in a two-step catalytic reaction. We make use of modeling and docking studies of adenylate kinase (ADK) to reveal the effects produced by the incorporation of the arginine mimetics on the structure of ADK and its action. Three analogues of arginine, L-canavanine (Cav), L-norcanavanine (NCav), and L-sulfoarginine (sArg), can be recognized as substrates of HArgS when incorporated in different peptide and protein sequences instead of L-arginine. Mutation in the enzyme active center by arginine mimetics leads to conformational changes, which produce a decrease the rate of the enzyme catalyzed reaction and even a loss of enzymatic action. All these observations could explain the long-lasting nature of the effects of the arginine analogues.

Protein Folding Prediction in a Cubic Lattice in Hydrophobic-Polar Model

The tertiary structure of the proteins determines their functions. Therefore, the predicting of proteins tertiary structure, based on the primary amino acid sequence from long time, is the most important and challenging subject in biochemistry, molecular biology, and biophysics. One of the most popular protein structure prediction methods, called Hydrophobic-Polar (HP) model, is based on the observation that in polar environment hydrophobic amino acids are in the core of the molecule-in contact between them and more polar amino acids are in contact with the polar environment. In this study, we present a new mixed integer programming formulation, exact algorithm, and two heuristic algorithms to solve the protein folding problem stated as a combinatorial optimization problem in a simple cubic lattice. The results from computational runs on a set of benchmarks are favorably compared to known algorithms for solving the 3D lattice HP model as genetic algorithms, ant colony optimization algorithm, and Monte Carlo algorithm.

Modeling the Relationship between Biological Activity of Delta-selective Enkephalin Analogues and Docking Results by Polynomials

One of the areas of bioinformatics is to develop a fast and reliable method for predicting the biological activity of compounds. This will abbreviate the way for design of new compounds and reduce costs. The process of creating the selective ligands of delta opioid receptor (DOR) was directed towards the synthesis of enkephalin analogues. Their biological activity was determined using the in vivo and in vitro methods, allowing establishing the relationship between structure and biological activity. The application of computational methods in the design of this type of compounds reduces the stages of synthesis and biological tests. The relationship of the efficacy with the values of the so-called ChemScore scoring function from GOLD 5.2 and the values of total energy of ligand-receptor complex was modeled with first- to third-degree polynomials and surface fitted method. The polynomial surface of the third degree has the best fit, assessed by least squares method. In our previous study with theoretical model of DOR (PDBid:1ozc) was established the relationship of the efficacy with the values of the GoldScore scoring function and the values of total energy of ligand-receptor complex. This relationship was modeled with third degree of polynomial in Matlab. The GoldScore scoring function is used for the prediction of ligand binding positions and it takes into account factors such as H-bonding energy, van der Waals energy, metal interaction and ligand torsion strain. In contrast to it the Chemscore scoring function incorporates a protein-ligand atom clash term and an internal energy term. It takes account of hydrophobic-hydrophobic contact area, hydrogen bonding, ligand flexibility and metal interaction. Therefore, the aim of presented work is to find an optimal fitting polynomial function by which to model the relationship between quantitative parameters of {\it in vitro} bioassay (efficacy, affinity and potency) and the values obtained from molecular docking with crystal structure of DOR (PDBid:4ej4). The finding, established in this study, suggests that the third degree polynomial could be successfully used for modeling of the relationship between the efficacy of delta-selective enkephalin analogues and the results from the docking experiments. It is described by a polynomial surface of the third degree. This function could serve to predict the biological activity of new analogues and to be very useful in the design of new delta-selective analogues. Acknowledgments: This work is partially supported by the project of the Bulgarian National Science Fund, entitled: “Bioinformatics research: protein folding, docking and prediction of biological activity”, code NSF I02/16, 12.12.14.

Comparative Evolution of four Scoring Functions with Three Models of Delta Opioid Receptor Using Molecular Docking

The present study was performed in order to find the most appropriate scoring functions and the model for docking of enkephalin analogues with delta-opioid receptor (DOR) that correlated well with the results obtained from in vitro tests. The capabilities of the four scoring functions embedded in GOLD were explored with three different models of DOR: a theoretical model published in ePDB (id: 1ozc), a model obtained by as with homology modeling, and a crystal structure of DOR published in PDB (id: 4ej4). Eleven enkephalin analogues were consistently docked with each of the models with each of the four scoring function. The analysis of the obtained results shows that after the docking with our modeled DOR values of scoring functions correlate with the data from in vitro tests at the highest degree. Furthermore, the use of the scoring functions ASP (Astex Statistical Potential) and GoldScore enable more precise docking of the test ligands as correlation coefficients were....

Computer modeling of human delta opioid receptor

Motivations. The development of strong analgesics without potential for abuse and adverse side effects is connected to understanding of the differences in opioid receptor subtypes as well as the model of interaction of ligands whit these receptors. In the absence of crystal structures of opioid receptors, 3D homology models with different templates have been reported in the literature. Methods. The aim of our study is to choose within recently published crystallographic structures templates for homology modeling of the human delta-opioid receptor. We generate several models using different templates and all they were evaluated by docking procedure. Ligands used in this study were already synthesized by our group and their biological activity was evaluated. They are analogues of the endogenous opioid peptides - enkephalins with substitutions in second position. Results. The best model of the human delta-opioid receptor was chosen according to data obtained from docking and in vitro biological activity. Acknowledgments This work was supported by NFSR of Bulgaria project DVU 01/197.