Laboratory of Computational and Quantitative Biology (LCQB)
UMR 7238 CNRS - Sorbonne Université
You are here
Computational methods for molecular biology
Date:
Thursday, May 26, 2011 - 00:00
Speaker:
Emidio Capriotti
Address:
Campus des Cordeliers
15, rue de l'école de médecine 75006 Paris
Salle Delarue
Affiliation:
Department of Bioengineering - Stanford University, Palo Alto (CA) - United States of America & Department of Mathematics and Computer Sciences - University of Balearic Islands - Palma de Mallorca - Spain
Abstract:
The advances in experimental techniques are allowing the collection of large amount of biological data. The accurate analysis of the available information is important to model biological processes and reduce the costs of the experiments, performing selected computationally driven tests. Two interesting examples of this trend are the large amount of human genetic variation data from high-throughput sequencing experiments and the increasing number of macromolecular structures known at atomic level deposited in the Protein Data Bank. In this talk, I will summarize the results of my research activity during the last years. First, I will present new algorithms for the prediction of effects of single amino acid polymorphisms (SAPs), describing different methods for the prediction of the protein stability change upon single point mutation [1] and for the prediction of their effects on human health [2,3] using protein sequence and structural information. In the last part of the talk I will present a new method for RNA structural alignment [4] and its possible application in prediction of the RNA structure and function [4,5].
References:
1. Capriotti E, Fariselli P, Casadio R. (2005). I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33 (Web Server issue): W306-W310.
2. Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. (2009). Functional annotations improve the predictive score of human disease-related mutations in proteins. Human Mutation. 30; 1237-1244.
3. Capriotti E, Altman RB. (2011). Improving the prediction of disease-related variants using protein three-dimensional structure. BMC Bioinformatics. In press.
4. Capriotti E, Marti-Renom MA. (2009). SARA: a server for function annotation of RNA structures. Nucleic Acids Res. 37 (Web Server issue); W260-W265.
5. Capriotti E, Marti-Renom MA. (2010). Quantifying the relationship between sequence and threedimensional structure conservation in RNA. BMC Bioinformatics. 11; 322.
Type:
Interdisciplinary Seminar
Export:
