ProCel - Bases Moleculares de la Proliferación Celular - Dept. Biología Molecular y Bioquímica
Universidad de Málaga
Amine system project
Versión española version española

FUNCTIONAL PREDICTION & MOLECULAR NETWORKS MODELLING

RESEARCH LINES

  1. Prediction, modelling and topological analysis of protein networks for the complete proteomes of H. sapiens and Saccharomyces cerevisiae.


In close collaboration with the professor Christine Orengo's group at the University College London (UCL), two kinds of interaction networks amongst proteins have been modelled for the complete proteomes of the H. sapiens and the S. cerevisiae, which we termed KnowledgeGram (KG) and PredictoGram (PG) by the origin of the datasets used in their creation. The KG networks integrate the information about the pairswise functional association retrieved from databases with “biological and/or experimental” knowledge such as: HRPD, MINT, Intact, Reactome, Kegg, Gene Ontology (GO) and FunCat. At the mean time the PG networks, integrate by Fisher's method, ab-initio predictions of interaction amongst proteins obtained by three different bioinformatics methods designed and deployed in this own work, like are: GECO (Gene Expressison COrrelation). CODA (Co-Ocurrence Domain Analysis); and hiPPI (homology inherited Protein-Protein Interactions).

In this job we demonstrate that the PG networks based on predictions resemble the topological features of the experimentally biological KG ones. We have also proved that the PG networks keep functional information key for modelling complex biological systems that make up this two studied organisms, information that in a high percentage (80%-90%) is not gathered in the KG networks based on experimental evidences.

Biotechniques Interview:

Recently our paper Finding the "Dark Matter" in Human and Yeast Protein Network Prediction and Modelling has aroused the interest of Biotechniques.com, The Journal of Life Science Methods settled in New York; as result of such interest the journal has published an interview where gets described our group's insights in this research line doing it in a quite documentary way, but if not for it missing thoroughness in the meantime. Biotechniques. com and "Dark Matter"


  1. Modelling and complex evolutive analysis of proteins in the entire proteomes of Escherichia coli and Saccharomyces cerevisiae.


In collaboration with the UCL we modelled with high precision the protein compounds for the whole proteomes of E. coli and S. cerevisiae, this is based on the protein compounds commonly accepted by the scientific community and on inferred compounds from throughputs assays of interactions amongst proteins.

In this work we prove that there are substantial differences in the way of the protein complexes have evolved between these two studied organisms. A previous suggested model about the evolution of the protein complexes identified complexes with central cores made up by the interaction of homologue proteins.In this job we find some evidences that bear out the relative importance in this way of evolution in yeast (S. cerevisiae), but we also find that this phenomena is much less usual in evolution of the complex being prokaryote (E. coli). Our outcomes point to in-deep differences in the way of the complexes have evolved between these two organisms, suggesting different strategies in the evolution of the protein complexes between prokaryotes and eukaryotes.


  1. Prediction of new proteins implied in the formation of the mitotic spindle in the human proteome.


This work has been developed in collaboration with several multidisciplinar groups of European researcher within the network of European excellence in system biology ENFIN (Experimental Network for Functional Integration; European FP6 Programme; www.enfin.org).

Besides taking part in the development of new predictive tools of mitotic spindle proteins, in this work we created an integrative prediction platform of the mitotic spindle (SPIP, Spindle Prediction Integrated Platform; see Figure I.1a) coordinating the integration of the different predictive series generated by the groups in collaboration such as the group at the University College London (UCL; Prof. C. Orengo), or the Centro Nacional de Investigaciones Oncologicas (CNIO-Madrid; Dr. Alfonso Valencia) and the group at the Technical University of Denmark (TUD; Prof. S. Brunak). The platform of integration and many of the prediction methods created by collaboration with the UCL (see sections 1.1 and 1.2) are the foundation of the methodology applied in this job.

The prediction of the SPIP platform was validated experimentally by the proffessor Erich Nigg's group (Max Planck Institute of Biochemistry – Munich; see Figure I.1b) with a percentage of success higher than 80%, when with former bioinformatics procedures were not ahead of the 35% of maximum success percentage.



Figure I.1. A) Schematic representation of the work-information flow and data handle in the SPIP platform of integration (SPIP, Spindle Prediction Integrated Platform). B) Some images from the experiments of localization in the mitotic spindle proteins predicted by the SPIP platform and validated yet by the Max-Planck's group in Munich.



  1. FuncNet: Integration of bioinformatics methods for the throughput prediction of the proteins function.


This work is currently ongoing and is being developed within a broad collaboration framework amongst group of European research in the biocomputation area. The methodology, procedures and processes unwrapped in the 1.1, 1.2 and 1.3 chapters are the foundations for the creation of the FuncNet web-server knowledge. Deploying of FuncNet was funded by EMBRACE and ENFIN European networks of excellence.

FuncNet is an informatics platform for the functional prediction of proteins, that integrate multiple predictive well-established methods platform interacting that command to this one the outcomes independently obtained for every single associated servers to itself. From the University of Malaga we have collaborated with the deployment of the algorithm and the mathematical integration procedure of this platform.

FuncNet allows users to integrate results of several predictive algorithms in an overall search process, at the meantime the predictive power is getting be on increase over its search by the mixture and/or integration of the different methods (Figure I.2). Tech notes just as How-To Guides are available at: http://www.funcnet.eu/


Figure I.2. A) FuncNet platform of integration's schematic representation and B) its flow and data handle.



  1. 3D modelling of the protein's interacting complex 14-3-3 with KSR1.


This work was carried out in collaboration with the Dr. José Lozano's group at the University of Malaga. We made synergy in the generation of 3D models of interaction amongst the protein 14-3-3 γ and the phosphopeptides RSKpSHE (PS297) and RTEpSVP (PS392) of the protein KSR1 by the use of the known 14-3-3 structure as comparative model γ binded the phosphopeptide type I, RAIpSLP.

The remaining model (Figure I.3) showed that the whole residues of the interaction site in 14-3-3 γ were completely conserve in the 7 isoforms of the 14-3-3 human protein. Some results of this model proved that the specificity of the interaction is mainly caused by the phosphopeptide sequence more than in the interaction truck of the 14-3-3 protein. The comparative study of the different known 14-3-3/phosphopeptide complex structures allowed to decide on the connections between the variation of the residues in different positions of the phosphopeptide sequence and the conformational changes associated with the 14-3-3 γ interation .




Figure I.3. 3D modelling of the interaction between the 14-3-3 protein γ and the phosphopeptides RSKpSHE (PS297) (a and c) and RTEpSVP (PS392) (b). 3D model comformational comparative of 14-3-3 with different phosphopeptides (d).



  1. Systems Microscopy.


The most recent research line where our group has joined up to. Several leading European laboratories in computational Biology including ours have set up an European Network of Excellence or briefly NoE in Systems Microscopy that has been funded by the European committee for the research within the EU-FP7 framework.

Said network springs up aiming at the idea of automatize complex processes of genotype and its later phenotype recognition by microscopy images treatment. For this purpose get to join their efforts some of the most remarkable European leading researcher in this topic, such as Jan Ellenberg, Daniel Gerlich either Wolfgang Huber from EMBL in Germany, Uri Alon and Benny Geiger from the Weizmann Institute in Israel, Olli Kallioniemi from the University of Helsinki in Finland or Jason Swedlow from the University of Dundee in Scotland.




Figure I.4. Provisional poster corresponding to the Systems Microscopy incoming kick off symposium to be held in the University of Malaga (UMA) the next 21st February 2011. As invited speakers it finds Steven Altschuler and Lani Wu from the University of Texas in USA either Rick Horwitz coming from the University of Virgina in USA as well.

LINKS OF INTEREST & COLLABORATIONS:

CIBER-ER - KHAOS - UCL - COST - CSIC - REMA - ICIII - MICINN - UMA - PAI - FUNCNET - ENFIN - Univ.Utah - Matlab - Biotechniques.com

Molecular basis of cell proliferation. Departamento de Biology and Biochemistry. University of Malaga Campus de Teatinos s/n 29071 MALAGA. Phone Number: (+34) 952 13 71 35. Fax: (+34) 952 13 16 74 - 20 00.
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