|Doctorant :||Iyanar VETRIVEL|
|Équipe :||Bioinformatique Structurale|
|Directeur de thèse :||Bernard OFFMANN , Professeur Université|
|co-directeur de thèse :||Frédéric Cadet, Professeur, Université de La Réunion|
|Financement :||Région Réunion|
|Date de la soutenance :||lundi 30 octobre 2017, 10h00|
Structural motifs found in protein structures. They help in approximating protein structure as a 1D string with minimal loss of structural information. Here I have employed a widely used structural alphabet called Protein Blocks (PB) for various applications like predicting, comparing and analyzing protein structures. PBs were used to study the structural variations in proteins with identical primary structure and also during the course of molecular dynamics (MD) simulations. The results from these analyses were summarized in the form of substitution matrices and showed striking similarities to previously established matrices for homologous proteins and NMR ensembles. I improved kPRED, a knowledge-based prediction of protein backbone in terms of PBs, by taking into consideration the neighboring local structures. The new version of the algorithm also privileges structural information from homologous proteins when available reaching an average accuracy of 66.3% on a benchmark dataset. The scope of the PENTAdb database has been expanded to cover the entire protein structure space in an automated manner. I show that the effect of this 950% increase in the contents of PENTAdb improves our understanding of the sequence-structure relationship at the pentapeptide level. I also used PB-ALIGN, a fast and efficient protein structure comparison tool, to compare all protein structures in PDB in an all-vs-all manner and to investigate PB-based structural similarities. This generated a huge collection of alignment data and I discuss its use for functional annotation and identification of possible evolutionary relationships.
Dans: Byska, Jan; Krone, Michael; Sommer, Björn (Ed.): Workshop on Molecular Graphics and Visual Analysis of Molecular Data, The Eurographics Association, 2019, ISBN: 978-3-03868-085-7.
Dans: Biochimie, vol. 167, p. 162–170, 2019, ISSN: 61831638.
Dans: PLoS ONE, vol. 12, no. 11, 2017, ISSN: 19326203.