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Nicolas Terrapon
 
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Name: Nicolas Terrapon
Status: Post Doc
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Curriculum
Teaching
  • Computer Networks
  • Database Management System
  • Object-Oriented Programming
  • Office automation
  • Web Development

Research Interests

I am mainly interested in protein domains. I am notably studying the improvement of domain detection, the evolution of domain architectures and the functional annotation of proteins thanks to domains.

Protein domains are structural and functional subunits of proteins. As such, they have a key position in function prediction in bioinformatics. Nowadays, numerous protein domains databases can be found online (e.g. Interpro, Pfam, etc). These databases offers probabilistic models, mainly Hidden Markov Models (HMMs). The models are built from alignments of manually curated families of homologous domains. Given a new protein sequence, these models allow to establish the domain architecture of the protein.

However, there is a obvious limit in this approach. Many domains may be missed due to the high phylogenetic distance of their sequences in comparison with those used to building the models. My first researches thus consisted to overcome this limit by developping statistical and learning methods (Ph.D Thesis). On the one hand, my work involved the enhancing of domain detection thanks to co-occurrence properties of protein domains. My approach allowed to detect numerous new Pfam domains in Plasmodium falciparum with low error rates. It also brought new Gene Ontology (GO) annotations, notably by annotating domain groups. By applying the approach to ten other human pathogens, I ceated the EuPathDomains database. On the other hand, my researches consist in developing methods to correct probabilistic models, unsuitable to the compositional bias. I put forward different corrections based on numerical, evolutionary, statistical and taxonomic techniques.

I am now interested in the emergence of new protein domains and of new domain arrangements.


Database
  • EuPathDomains
    EuPathDomains is an extended database of protein domains in several eukaryotic pathogens from EuPathDB
    The EuPathDomains database gathers known Interpro domains occurrences and new Pfam domain occurrences found by the CODD procedure [Terrapon et al., 2009]. CODD improves the sensitivity of Pfam domain detection by exploiting the domain tendency to appear preferentially with a few other favorite domains in a protein. This property enables CODD to certify the presence of a divergent domain on the basis of the presence of another domain in the same protein.
    The database contains domains for Giardia lamblia, Trypanosoma brucei, three Leishmania species, and five apicomplexan species including three Plasmodium species, Toxoplasma gondii and Cryptosporidium parvum.

Journal Publications
  • Ghouila A.*, Terrapon N.*, Gascuel O., Guerfali F., Laouini D., Maréchal É. and Bréhélin L. (* first authors)
    EuPathDomains : the divergent domain database for eukaryotic pathogens.
    Infection, Genetics and Evolution. E-published on Nov. 2010. Pubmed link.

  • Terrapon N., Gascuel O., Maréchal É. and Bréhélin L.
    Detection of New Protein Domains by Co-occurrence: Application to Plasmodium falciparum.
    Bioinformatics. 2009 Dec. 25(23):3077-8. Pubmed link.

Conference Proceedings
  • Terrapon N., Gascuel O., Maréchal É. and Bréhélin L.
    Detection of new protein domains with co-occurrence: application to Plasmodium falciparum.
    Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM Nantes 2009). Article - Oral Communication

Conferences
 
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