Reconstruction of Kauffman networks applying trees

AbstractAccording to Kauffman’s theory [S. Kauffman, The Origins of Order, Self-Organization and Selection in Evolution, Oxford University Press, New York, 1993], enzymes in living organisms form a dynamic network, which governs their activity. For each enzyme the network contains:•a collection of enzymes affecting the enzyme and•a Boolean function prescribing next activity of the enzyme as a function of the present activity of the affecting enzymes.Kauffman’s original pure random structure of the connections was criticized by Barabasi and Albert [A.-L. Barabasi, R. Albert, Emergence of scaling in random networks, Science 286 (1999) 509–512]. Their model was unified with Kauffman’s network by Aldana and Cluzel [M. Aldana, P. Cluzel, A natural class of robust networks, Proc. Natl. Acad. Sci. USA 100 (2003) 8710–8714]. Kauffman postulated that the dynamic character of the network determines the fitness of the organism. If the network is either convergent or chaotic, the chance of survival is lessened. If, however, the network is stable and critical, the organism will proliferate. Kauffman originally proposed a special type of Boolean functions to promote stability, which he called the property canalyzing. This property was extended by Shmulevich et al. [I. Shmulevich, H. Lähdesmäki, E.R. Dougherty, J. Astola, W. Zhang, The role of certain Post classes in Boolean network models of genetic networks, Proc. Natl. Acad. Sci. USA 100 (2003) 10734–10739] using Post classes. Following their ideas, we propose decision tree functions for enzymatic interactions. The model is fitted to microarray data of Cogburn et al. [L.A. Cogburn, W. Wang, W. Carre, L. Rejtő, T.E. Porter, S.E. Aggrey, J. Simon, System-wide chicken DNA microarrays, gene expression profiling, and discovery of functional genes, Poult. Sci. Assoc. 82 (2003) 939–951; L.A. Cogburn, X. Wang, W. Carre, L. Rejtő, S.E. Aggrey, M.J. Duclos, J. Simon, T.E. Porter, Functional genomics in chickens: development of integrated-systems microarrays for transcriptional profiling and discovery of regulatory pathways, Comp. Funct. Genom. 5 (2004) 253–261]. In microarray measurements the activity of clones is measured. The problem here is the reconstruction of the structure of enzymatic interactions of the living organism using microarray data. The task resembles summing up the whole story of a film from unordered and perhaps incomplete collections of its pieces. Two basic ingredients will be used in tackling the problem. In our earlier works [L. Rejtő, G. Tusnády, Evolution of random Boolean NK-models in Tierra environment, in: I. Berkes, E. Csaki, M. Csörgő (Eds.), Limit Theorems in Probability an Statistics, Budapest, vol. II, 2002, pp. 499–526] we used an evolutionary strategy called Tierra, which was proposed by Ray [T.S. Ray, Evolution, complexity, entropy and artificial reality, Physica D 75 (1994) 239–263] for investigating complex systems. Here we apply this method together with the tree–structure of clones found in our earlier statistical analysis of microarray measurements [L. Rejtő, G. Tusnády, Clustering methods in microarrays, Period. Math. Hungar. 50 (2005) 199–221]

CLASSIFICATION OF MULTIGRAPHS VIA SPECTRAL TECHNIQUES

Classification problems of the vertices of large multigraphs (hypergraphs or weighted graphs) can be easily handled by means of linear algebraic tools. For this purpose nocion of the Laplacian of multigraphs will be introduced, the eigenvectors belonging to k consecutive eigenvalues of which define optimal k-dimensional Euclidean representation of the vertices. In this way perturbation results are obtained for the minimal (k+1)-cuts of multigraphs (where k is an arbitrary integer between 1 and the number of vertices). The (k+1)-variance of the optimal k-dimensional representatives is estimated from above by the k smallest positive eigenvalues and by the gap in the spectrum between the kth and (k+1)th positive eigenvalues in increasing order. These results are of statistical character. However, they are useful and well-adopted to automatic computation in the case of large multigraphs when one is not interested in strict structural properties and, on the other hand, usual enumeration algorithms are very time-demanding

Transzmembrán fehérjék in-silico szerkezet vizsgálata. = In-silico study of transmembrane protein's structures.

Ebben a pályázatban a FEBS Letters felkérésére összefoglalót készítettünk a humán ABC fehérjék membrán topológiájáról, továbbfejlesztettük a BiSearch PCR primer-tervező szoftvert (http://bisearch.enzim.hu), amelyről a BMC Bioinformatics-ban számoltunk be. Felkérésre írtunk egy összefoglaló könyvfejezetet is erről a témáról, ami a Methods in Molecular Biology-ban jelent meg. Létrehoztunk továbbá két topológiai adatokat tartalmazó adatbázist, amelyeket TOPDB (http://topdb.enzim.hu) és TOPDOM (http://topdom.enzim.hu) adatbázisnak neveztünk el. A TOPDB adatbázis az ismert szerkezetű transzmembrán fehérjék topológiai adatait, valamint az irodalomban található különböző fizikai-kémiai, molekuláris biológiai módszerrel nyert topológiai adatokat tartalmazza. A TOPDOM adatbázis olyan domén és szekvencia motívum adatokat tartalmaz, amelyek a transzmembrán fehérjékben konzervatív módon találhatóak meg. Ezeket a munkákat a Nucleic Acids Research, illetve a Bioinformatics folyóiratban közöltük. A pályázatban közreműködő Simon István további 16 cikket publikált a jelen pályázat futamidejében, amelyek közül 13 cikk a T049073 sz. OTKA pályázat zárójelentésében kerültek kifejtésre, illetve 3 a T072569 sz. OTKA pályázat első részjelentésében lesz található. | In this project we have written a minireview to the FEBS Letters about the topology of ABC transporters. We improved the BiSearch primer design and PCR algorithm (http://bisearch.enzim.hu), which was reported in BMC Bioinformatics. We also have written a review in this fild in the Methods in Molecular Biology. We have created two databases containing topology data of transmembrane proteins, called TOPDB (http://topdb.enzim.hu) and TOPDOM (http://topdom.enzim.hu), respectively. The TOPDB database contains the topology data of transmembrane proteins with known 3D structure, as well as the details of various physico-chemical and molecular biology experiments carried out to learn about the topology of these proteins. TOPDOM is a collection of domains and sequence motifs located conservatively in one side of transmembrane proteins. These works have been published in Nucleic Acids Research and Bioinformatics, respectively. Moreover, István Simon, the participant of this project has published 16 articles in the time of this project, from which 13 articles have been reported in the final report of the T049073 OTKA project, and 3 articles will be reported in the first progress report of the T072569 OTKA project

Transzmembrán fehérjék bioinformatikai szerkezet vizsgálata = Bioinformatics approaches to transmembrane protein structures

Kidolgoztunk egy eljárást, a TMDET-et, amivel meghatározható a fehérjék elhelyezkedése és orientációja a membránhoz képest (http://tmdet.enzim.hu/). A TMDET felhasználásával elkészitettük az ismert térszerkezetű transzmembrán fehérjék adatbázisát a PDB_TM-et (http://pdbtm.enzim.hu/). Készitettünk továbbá egy transzmembrán fehérje topológiai adatbázist a TOPDB-t (http://topdb.enzim.hu/). Az adatbázisokhoz kereső és elemző programokat is készitettünk. Korábban kifejlesztett topologia becslő algoritmusainkkal valamint a fenti szerverekkel összeállitást készitettünk a humán ABC transzporter fehérjékről. A kizárólag transzmembrán fehérjéket érintő módszerek mellett ki kellett dolgozni, általános minden fehérjét érintőket eljárásokai is. IUPred néven algoritmust dolgoztunk ki fehérjék rendezetlen szegmenseinek becslésére (http://iupred.enzim.hu/). Meghatároztuk, hogyan változott a rendezetlenség mértéke a törzsfejlődés során, milyen szerepet játszanak ezek a rendezetlen szakaszok a fehérjék makromolekularis kölcsönhatásaiban valamint fehérje hálózatok szerveződésében. További algoritmusokat dolgoztunk ki fehérjék stabilitásáért felelős aminosavak azonositására (http://sride.enzim.hu/) és fehérjetervezések segitésére (http://bisearch.enzim.hu/). Meghatároztuk egyes fémionok szerkezet módositásának funkciónalis szerepét és megvizsgáltunk két gyakorlati szempontból fontos fehérjét. Az eredményeket 22 cikkben közöltűk és 6 nyilvános szervert telepitettűnk a világhálóra. | An algorithm, TMDET, have been developed to identify the position and orientation of the proteins relative to the membrane (http://tmdet.enzim.hu/). The TMDET algorithm was used to develop the PBD_TM databank of transmembrane proteins of known X-ray structure (http://pdbtm.enzim.hu/). A transmembrane protein topology database, TOPDB, were also developed (http://topdb.enzim.hu/). The databank have been furnised with search engin and data analysers. With these server and our topology prediction methods, developed earlier, a survey in human ABC transporter protein have been made. Beside these ''transmembrane protein only'' projects, we have developed additional methods which can be use on both transmembrane and water soluble proteins. An algorithm, IUPred, have been developed to predict unstructured protein segments (http://iupred.enzim.hu/). The evolutionary changes of the frequence of desordered protein (segments), and the role of the unstructured segments in the macromolecular interactions and and network building have been determined. Two other algorithms were also developed. One to locate residues responsible for protein stability, the other to support protein engineering. Finally we have determine the role of certain metal ion in function related structural changes in protein and the structure function relationship in to protein of practical interest. The results have been published in 22 papers and 6 public servers were established into the WWW

TMFoldWeb: a web server for predicting transmembrane protein fold class.

BACKGROUND: Here we present TMFoldWeb, the web server implementation of TMFoldRec, a transmembrane protein fold recognition algorithm. TMFoldRec uses statistical potentials and utilizes topology filtering and a gapless threading algorithm. It ranks template structures and selects the most likely candidates and estimates the reliability of the obtained lowest energy model. The statistical potential was developed in a maximum likelihood framework on a representative set of the PDBTM database. According to the benchmark test the performance of TMFoldRec is about 77 % in correctly predicting fold class for a given transmembrane protein sequence. RESULTS: An intuitive web interface has been developed for the recently published TMFoldRec algorithm. The query sequence goes through a pipeline of topology prediction and a systematic sequence to structure alignment (threading). Resulting templates are ordered by energy and reliability values and are colored according to their significance level. Besides the graphical interface, a programmatic access is available as well, via a direct interface for developers or for submitting genome-wide data sets. CONCLUSIONS: The TMFoldWeb web server is unique and currently the only web server that is able to predict the fold class of transmembrane proteins while assigning reliability scores for the prediction. This method is prepared for genome-wide analysis with its easy-to-use interface, informative result page and programmatic access. Considering the info-communication evolution in the last few years, the developed web server, as well as the molecule viewer, is responsive and fully compatible with the prevalent tablets and mobile devices. REVIEWERS: This article was reviewed by Dr. Michael Gromiha, Dr. Sandor Pongor and Dr. Frank Eisenhaber with Wing-Cheong Wong

TMCrys: predict propensity of success for transmembrane protein crystallization

Motivation Transmembrane proteins (TMPs) are crucial in the life of the cells. As they have special properties, their structure is hard to determine––the PDB database consists of 2% TMPs, despite the fact that they are predicted to make up to 25% of the human proteome. Crystallization prediction methods were developed to aid the target selection for structure determination, however, there is a need for a TMP specific service. Results Here, we present TMCrys, a crystallization prediction method that surpasses existing prediction methods in performance thanks to its specialization for TMPs. We expect TMCrys to improve target selection of TMPs