Éditeur(s) : HAL CCSD Elsevier
Résumé : The Duffy antigen/receptor for chemokine (DARC) is an erythrocyte receptor for malaria parasites (Plasmodium vivax and Plasmodium knowlesi) and for chemokines. In contrast to other chemokine receptors, DARC is a promiscuous receptor that binds chemokines of both CC and CXC classes. The four extracellular domains (ECDs) of DARC are essential for its interaction with chemokines, whilst the first (ECD1) is sufficient for the interaction with malaria erythrocyte-binding protein. In this study, we elaborate and analyze structural models of the DARC. The construction of the 3D models is based on a comparative modeling process and on the use of many procedures to predict transmembrane segments and to detect far homologous proteins with known structures. Threading, ab initio, secondary structure and Protein Blocks approaches are used to build a very large number of models. The conformational exploration of the ECDs is performed with simulated annealing. The second and fourth ECDs are strongly constrained. On the contrary, the ECD1 is highly flexible, but seems composed of three consecutive regions: a small beta-sheet, a linker region and a structured loop. The chosen structural models encompass most of the biochemical features and reflect the known experimental data. They may be used to analyze functional interaction properties.
ISSN: 0006-3002

inserm-00143373
http://www.hal.inserm.fr/inserm-00143373
http://www.hal.inserm.fr/inserm-00143373/document
http://www.hal.inserm.fr/inserm-00143373/file/de_Brevern_BBA_2005.pdf
DOI : 10.1016/j.bbagen.2005.05.016

Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
A relevant and accurate description of three-dimensional (3D) protein structures can be achieved by characterizing recurrent local structures. In a previous study, we developed a library of 120 3D structural prototypes encompassing all known 11-residues long local protein structures and ensuring a good quality of structural approximation. A local structure prediction method was also proposed. Here, overlapping properties of local protein structures in global ones are taken into account to characterize frequent local networks. At the same time, we propose a new long local structure prediction strategy which involves the use of evolutionary information coupled with Support Vector Machines (SVMs). Our prediction is evaluated by a stringent geometrical assessment. Every local structure prediction with a Calpha RMSD less than 2.5 A from the true local structure is considered as correct. A global prediction rate of 63.1% is then reached, corresponding to an improvement of 7.7 points compared with the previous strategy. In the same way, the prediction of 88.33% of the 120 structural classes is improved with 8.65% mean gain. 85.33% of proteins have better prediction results with a 9.43% average gain. An analysis of prediction rate per local network also supports the global improvement and gives insights into the potential of our method for predicting super local structures. Moreover, a confidence index for the direct estimation of prediction quality is proposed. Finally, our method is proved to be very competitive with cutting-edge strategies encompassing three categories of local structure predictions. Proteins 2009. (c) 2009 Wiley-Liss, Inc.
ISSN: 0887-3585

inserm-00348740
http://www.hal.inserm.fr/inserm-00348740
http://www.hal.inserm.fr/inserm-00348740/document
http://www.hal.inserm.fr/inserm-00348740/file/Bornot_Proteins_2009_preprint_version.pdf
http://www.hal.inserm.fr/inserm-00348740/file/inserm-00348740_edited.pdf

Éditeur(s) : HAL CCSD IOS Press
Résumé : International audience
Recent work has used graphs to modelize expression data from microarray experiments, in view of partitioning the genes into clusters. In this paper, we introduce the use of a decomposition by clique separators. Our aim is to improve the classical clustering methods in two ways: first we want to allow an overlap between clusters, as this seems biologically sound, and second we want to be guided by the structure of the graph to define the number of clusters. We test this approach with a well-known yeast database (Saccharomyces cerevisiae). Our results are good, as the expression profiles of the clusters we find are very coherent. Moreover, we are able to organize into another graph the clusters we find, and order them in a fashion which turns out to respect the chronological order defined by the the sporulation process.
ISSN: 1386-6338

hal-00521936
https://hal.archives-ouvertes.fr/hal-00521936