I am currently carrying out a PhD at the University of Rennes 1, in Dyliss team,
under the supervision of François Coste
and Jacques Nicolas.
My thesis focuses on protein homology search and protein family modelling. As of today, the standard way of modelling a set of protein sequences is to use profile Hidden Markov Models. These statistical models represent protein families with positional information and insertion and deletion states. Widely used packages such as HH-suite provide tools to perform sequence-HMM alignment and HMM-HMM alignment and the similarity score they yield is used to determine whether two protein sequences are homologous or not. While powerful, HMMs are limited by their positional nature. Yet, it is well-known that two residues that are distant in the sequence can co-evolve, for instance because they are in contact in the 3D structure. In our work, we propose to use a different type of model: the Potts model. This model was introduced by Direct Coupling Analysis, originally to predict contacts and protein-protein interactions. Its parameters can describe both positional conservation and couplings between positions, which make it a great candidate to model sets of homologous proteins.
During my PhD, I designed ComPotts, a method to align two Potts models and give a similarity score for this alignment. Preliminary experiments to assess the quality of our alignments
I presented ComPotts at the conference JOBIM 2020 during the Protein and Structure parallel session on July 3 2020, my slides are available here.
Early in my thesis, I also had the opportunity to work with Witold Dyrka from Politechnika Wrocławska (Wrocław, Poland) on probabilistic context-free grammars with contact map constraints on proteins
|||Hugo Talibart and François Coste. Compotts: Optimal alignment of coevolutionary models for protein sequences. In JOBIM 2020-Journées Ouvertes Biologie, Informatique et Mathématiques, 2020. [ bib ]|
|||Hugo Talibart and François Coste. Compotts: Optimal alignment of coevolutionary models for protein sequences. unpublished, 2020. [ bib ]|
|||Witold Dyrka, Mateusz Pyzik, François Coste, and Hugo Talibart. Estimating probabilistic context-free grammars for proteins using contact map constraints. PeerJ, 7:e6559, 2019. [ bib ]|
As part of the 2018 Sciences en Cour[t]s festival, I explained my PhD project in a short video using slips of paper. (Video in french)
Download full resume here
Dyliss team, Irisa / Inria Rennes-Bretagne Atlantique,
Campus de Beaulieu, 35042 Rennes Cedex, France.
Tel: +33 (0) 2 99 84 22 32