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In the current study we present a monitoring of the MHT network system by using the DUDe. It is an effective monitoring system for administrators covering services to every client. The administrators are constantly striving to maintain smooth operation of their networks. The system enables to monitor the status changes particularly outside the monitoring area. In the observed system, we use the Open Shortest Path First protocol, which is based on the link state routing protocol concept and uses Dijkstra’s shortest path first routing algorithm. An alert system is built to the MHT network. It can be used to supervise the network and can report the state of the network by using an alert system in the monitoring area.

Molecular docking algorithms predict the binding of a macromolecule (receptor) and a small molecule (ligand). Virtual screening by molecular docking has become established as a method for drug lead discovery and optimization. All docking algorithms make use of a scoring function in combination with a method of search. Presuming adequate search strategies, a scoring function’s location performance will dominate behavior with respect to docking accuracy: the degree to which a predicted pose of a ligand matches experimental observation. Approaches to the development of scoring functions have varied widely, with a number of functions yielding similarly high levels of performance relating to the location issue. In this paper a survey of scoring functions for protein-ligand docking is presented.

The smart technologies are becoming more important in many applicative scenarios such as healthcare, environmental monitoring and building automation. The main goal of this work is to investigate a KNX-based home automation system. Such technical systems can be remotely monitored and controlled. By the use of KNX, technical data can be collected and made available at desired location.

The maximum network flow problem is a fundamental problem in network flow theory. The Ford-Fulkerson and Edmonds-Karp algorithm are popular optimization algorithms for computing the network flow. The current paper presents a C# implementation of Ford-Fulkerson and Edmonds-Karp algorithms for solving the maximum flow problem in networking.

Cannabinoid receptors are important class of receptors as they are involved in various physiological processes such as appetite, pain-sensation, mood, and memory. It is important to design receptor-selective ligands in order to treat a particular disorder. The aim of the present study is to model the structure of cannabinoid receptor CB1 and to perform docking between obtained models and known ligands. Two models of CBR1 were prepared with two different methods (Modeller of Chimera and MOE). They were used for docking with GOLD 5.2. It was established a high correlation between inhibitory constant Ki of CB1 cannabinoid ligands and the ChemScore scoring function of GOLD, which concerns both models. This suggests that the models of the CB1 receptors obtained could be used for docking studies and in further investigation and design of new potential, selective and active cannabinoids with the desired effects.

The use of graphs is widely applied in modeling and solving problems in the field of computer science and bioinformatics.Therefore, it is essential to develop and improve algorithms reducing their computational complexity and increasing the precision of the solutions generated by them as well as the size of the input data.In this study two well-known algorithms for solving the problem for finding a minimum Hamiltonian cycle in weighted, undirected and complete graph (also known as Travelling Salesman Problem –- TSP) are analyzed.The first algorithm is based on the backtracking method and it always finds the optimal solution, while with the second one, the genetic algorithm (GA), finding the optimal solution is not always guaranteed.The aims of the study are to determine: (1)which of the algorithms can be used so that the resulting solution is optimal or near-optimal and the execution time be reasonable depending on the size of the input data; (2)the influence of GA parameter values on the quality of the resulting solutions for large size of the input data. The parameters determine the number of solutions in each population and the number of all generations.The analysis of the results revealed that:(1) the algorithm that finds all possible solutions can be used for graphs with a small number of vertices (not more than 20), whereas GA can be used for graphs with a large number of vertices; (2) in graphs with a small number of vertices: n

The μ-opioid receptor (MOR) is an important target in the search for novel selective ligands, including analgesics. Numerous MOR selective derivatives have been identified since determination of the sequence of endogenous enkephalin pentapeptides (Leu5-enkephalin and Met5- enkephalin) and the structure of morphine in 19th century. Most of opioid alkaloids and their derivatives have μ-opioid affinity, while endogenous enkephalins are rather δ- than μ-selective. Morphine is still the drug of choice for treating severe pain caused by cancer or surgical operation, but its side effects are the reason for the searching and development of new, selective MOR agonists. The aim of our study is to choose within recently published crystallographic structures templates for homology modeling of the human μ-opioid receptor. We generated several models using different templates and all of them were evaluated by docking procedure (Gold 5.1) Ligands used in this investigation were synthesized and evaluated for their biological activity in our previous studies. They are enekphalin analogues with substitutions in second position. The best model of the human mu-opioid receptor was chosen according to data obtained from docking and in vitro biological activity of analogues and endogenous enkephalins.

Opioid receptors are an important class of receptors which are involved in the modulation of pain. Compared to the well studied μ-opioid receptor (MOR) δ-opioid receptor (DOR) is relatively less studied. The activation of DOR mediates antinociception and antihiperanalgesia under different conditions, such as stress, chronic pain, opioid-induced hyperalgesia, and tolerance to opiates. The δ-opioid signaling strongly interacts with the μ-opioid system, resulting in a modulation of the transmission of pain. In order to complete the study of DOR different

One of the important topics in structural bioinformatics refers to the analysis of protein sequences and their biological functions, as well as the assessment of protein structural similarities. These investigations play a critical role in the drug design, the homology modelling and the protein structure prediction. Therefore, it is important to evaluate the structures similarity and to identify the similar predictions. The degree of similarity of two protein 3D structures is usually measured by the root-mean-square distance (RMSD) between the equivalent atom pairs. The objective of this research is to present a simple procedure to calculate the RMSD between pairs of 3D structures and to align the structures in order to find the minimal value of RMSD. A web service for calculating the RMSD in Perl programming language is developed. The tool can be used in the field of bioinformatics research and computer modelling of protein structures.

The endocannabinoid system consists of endocannabinoids, cannabinoid receptors and enzymes that synthesise and degrade the endocannabinoids. The cannabinoid receptors (CBR) are an important class of receptors participating in various physiological processes. Many of their effects are mediated by two G protein-coupled receptors (GPCRs), (CB1) and (CB2), although additional receptors may be involved. The purpose of this paper is to present a computer modelling of the CBR and several known cannabinoid ligands aiming to determine the structure-activity relationship by using molecular docking with GOLD software. Four scoring functions provided by GOLD are used for molecular docking of the models of CBR1, its crystal structure and the known cannabinoid ligands. Significant correlations between the biological activity of the ligands and the ChemScore optimization function are established. The results obtained could be further used for in silico experiments of the interaction between CBR and cannabinoid ligands.

The root-mean-square deviation of atomic positions (RMSD) is one of the most commonly used approaches in bioinformatics. It measures the average distance between the atoms of superimposed proteins. The present study describes a program calculating RMSD between two structures. The software developed detects the surfaces of two molecular structures-a convex and a concave one and the area of their interaction by calculating RMSD between them. The program uses fragments of files from Protein Data Bank format. The Python implementation enabling RMSD computation is suggested on the ground of the Kabsh algorithm.

The development of selective agonists of δ-opioid receptor as well as the model of interaction of ligands with this receptor is the subjects of increased interest. In the absence of crystal structures of opioid receptors, 3D homology models with different templates have been reported in the literature. The problem is that these models are not available for widespread use. The aims of our study are: (1) to choose within recently published crystallographic structures templates for homology modeling of the human δ-opioid receptor (DOR); (2) to evaluate the models with different computational tools; and (3) to precise the most reliable model basing on correlation between docking data and in vitro bioassay results. The enkephalin analogues, as ligands used in this study, were previously synthesized by our group and their biological activity was evaluated. Several models of DOR were generated using different templates. All these models were evaluated by PROCHECK and MolProbity and relationship between docking data and in vitro results was determined. The best correlations received for the tested models of DOR were found between efficacy (erel) of the compounds, calculated from in vitro experiments and Fitness scoring function from docking studies. New model of DOR was generated and evaluated by different approaches. This model has good GA341 value (0.99) from MODELLER, good values from PROCHECK (92.6% of most favored regions) and MolProbity (99.5% of favored regions). Scoring function correlates (Pearson r = -0.7368, p-value = 0.0097) with erel of a series of enkephalin analogues, calculated from in vitro experiments. So, this investigation allows suggesting a reliable model of DOR. Newly generated model of DOR receptor could be used further for in silico experiments and it will give possibility for faster and more correct design of selective and effective ligands for δ-opioid receptor.