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Poznań, Poland

Jankowski W.,Adam Mickiewicz University | Jankowski W.,BioInfoBank Institute | Hoffmann M.,Adam Mickiewicz University
Journal of Medical Internet Research | Year: 2016

Background: Predicting the popularity of and harm caused by psychoactive agents is a serious problem that would be difficult to do by a single simple method. However, because of the growing number of drugs it is very important to provide a simple and fast tool for predicting some characteristics of these substances. We were inspired by the Google Flu Trends study on the activity of the influenza virus, which showed that influenza virus activity worldwide can be monitored based on queries entered into the Google search engine. Objective: Our aim was to propose a fast method for ranking the most popular and most harmful drugs based on easily available data gathered from the Internet. Methods: We used the Google search engine to acquire data for the ranking lists. Subsequently, using the resulting list and the frequency of hits for the respective psychoactive drugs combined with the word "harm" or "harmful", we estimated quickly how much harm is associated with each drug. Results: We ranked the most popular and harmful psychoactive drugs. As we conducted the research over a period of several months, we noted that the relative popularity indexes tended to change depending on when we obtained them. This suggests that the data may be useful in monitoring changes over time in the use of each of these psychoactive agents. Conclusions: Our data correlate well with the results from a multicriteria decision analysis of drug harms in the United Kingdom. We showed that Google search data can be a valuable source of information to assess the popularity of and harm caused by psychoactive agents and may help in monitoring drug use trends.

Steczkiewicz K.,University of Warsaw | Muszewska A.,University of Warsaw | Knizewski L.,University of Warsaw | Rychlewski L.,BioInfoBank Institute | Ginalski K.,University of Warsaw
Nucleic Acids Research | Year: 2012

Proteins belonging to PD-(D/E)XK phosphodiesterases constitute a functionally diverse superfamily with representatives involved in replication, restriction, DNA repair and tRNA-intron splicing. Their malfunction in humans triggers severe diseases, such as Fanconi anemia and Xeroderma pigmentosum. To date there have been several attempts to identify and classify new PD-(D/E)KK phosphodiesterases using remote homology detection methods. Such efforts are complicated, because the superfamily exhibits extreme sequence and structural divergence. Using advanced homology detection methods supported with superfamily-wide domain architecture and horizontal gene transfer analyses, we provide a comprehensive reclassification of proteins containing a PD-(D/E)XK domain. The PD-(D/E)XK phosphodiesterases span over 21 900 proteins, which can be classified into 121 groups of various families. Eleven of them, including DUF4420, DUF3883, DUF4263, COG5482, COG1395, Tsp45I, HaeII, Eco47II, ScaI, HpaII and Replic-Relax, are newly assigned to the PD-(D/E)XK superfamily. Some groups of PD-(D/E)XK proteins are present in all domains of life, whereas others occur within small numbers of organisms. We observed multiple horizontal gene transfers even between human pathogenic bacteria or from Prokaryota to Eukaryota. Uncommon domain arrangements greatly elaborate the PD-(D/E)XK world. These include domain architectures suggesting regulatory roles in Eukaryotes, like stress sensing and cellcycle regulation. Our results may inspire further experimental studies aimed at identification of exact biological functions, specific substrates and molecular mechanisms of reactions performed by these highly diverse proteins. © The Author(s) 2012.

Malkowska M.,Center of Oncology of Poland | Kokoszynska K.,Center of Oncology of Poland | Rychlewski L.,BioInfoBank Institute | Wyrwicz L.,Center of Oncology of Poland
Biochimie | Year: 2013

The Transcription Factor IID is a large macromolecular complex composed of the TATA-box binding protein (TBP) and a group of 13-14 conserved TBP-associated factors (TAFs). TAFs are known to regulate transcription at various levels-mediating transcription via interaction with activators, histone modifications; recognition and binding to promoters; acting as a platform for other Transcription Factors and RNA polymerase II. Despite numerous previous studies of the TFIID complex, the knowledge concerning the structure of its components, and thus the exact mechanism of its function, remains undetermined. To carry out an in-depth analysis of TFIID we performed the structural bioinformatic analysis of the TFIID complex. The sequence identity and similarity of 13.74% and 37.56%, respectively (calculated with PAM250 matrix) between M1 aminopeptidase protein and TAF2 and the high similarity of their putative secondary structures allowed us to model a large part of the TAF2 structure. The sequence analysis enabled the mapping of previously not fully characterized structural domains in well-studied TAF proteins (including the full histone domains of TAF4 and 12 or TAF3 and 8). In this study we provided detailed structural models for all the elements of human analyzed in the context of TFIID activity, along with indications of structural alterations within TFIID in various animal model species. © 2012 Elsevier Masson SAS. All rights reserved.

Kurzynski M.,Adam Mickiewicz University | Torchala M.,Adam Mickiewicz University | Torchala M.,BioInfoBank Institute | Chelminiak P.,Adam Mickiewicz University
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2014

Biological molecular machines are proteins that operate under isothermal conditions and hence are referred to as free energy transducers. They can be formally considered as enzymes that simultaneously catalyze two chemical reactions: the free energy-donating (input) reaction and the free energy-accepting (output) one. Most if not all biologically active proteins display a slow stochastic dynamics of transitions between a variety of conformational substates composing their native state. This makes the description of the enzymatic reaction kinetics in terms of conventional rate constants insufficient. In the steady state, upon taking advantage of the assumption that each reaction proceeds through a single pair (the gate) of transition conformational substates of the enzyme-substrates complex, the degree of coupling between the output and the input reaction fluxes has been expressed in terms of the mean first-passage times on a conformational transition network between the distinguished substates. The theory is confronted with the results of random-walk simulations on the five-dimensional hypercube. The formal proof is given that, for single input and output gates, the output-input degree of coupling cannot exceed unity. As some experiments suggest such exceeding, looking for the conditions for increasing the degree of coupling value over unity challenges the theory. Performed simulations of random walks on several model networks involving more extended gates indicate that the case of the degree of coupling value higher than 1 is realized in a natural way on critical branching trees extended by long-range shortcuts. Such networks are scale-free and display the property of the small world. For short-range shortcuts, the networks are scale-free and fractal, representing a reasonable model for biomolecular machines displaying tight coupling, i.e., the degree of coupling equal exactly to unity. A hypothesis is stated that the protein conformational transition networks, as just as higher-level biological networks, the protein interaction network, and the metabolic network, have evolved in the process of self-organized criticality. © 2014 American Physical Society.

Agency: Cordis | Branch: FP7 | Program: MC-ERG | Phase: FP7-PEOPLE-2009-RG | Award Amount: 30.00K | Year: 2010

Beside these well-known molecules there is a vast unknown world of tiny RNAs (RiboNucleic Acids) that might play a crucial role in a number of cellular processes. Those elements are named noncoding RNAs (ncRNA) and they play their function without transcription to the protein product. Here is proposed development of integrated bioinformatics platform that is specifically addressed for detecting, verifying, and classifying of noncoding RNAs. This complex approach to Computational RNomics will provide the pipeline which will be capable of detecting RNA motifs with low sequence conservation. It will also integrate RNA motif prediction which should significantly improve the quality of the RNA homolog search. The first commercial application is the integrated system for detection of new regulatory elements located in the non coding genome parts. Up to now numerous human disorders have been found to be related to some of the noncoding RNAs The second application of the project is so called RNA nanotechnology. It is designing of artificial nanoparticles, which are assembled mainly from ribonucleic acid which possess both the right size and ability to gain entry into cells and halt viral growth or cancers progress or deliver drugs. The project will benefit from latest achievements in High Performance Computing and General-Purpose computing on Graphics Processing Units and Graph theory.

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