Laboratory of Bioinformatics Analyses for Genomics and Metabolism
A major challenge in modern biology is the discovery of in vivo metabolic or physiological functions of unknown proteins. Our institute has developed an integrated strategy based on in silico prediction of enzymatic activities and in vitro screening of enzymes
MetX and MetA are two phylogenetically unrelated protein families, but both are implied in the first step of the L-methionine biosynthesis pathway. MetX proteins are known as L-homoserine O-acetyltransferases (HAT), while MetA proteins are generally annotated as L-homoserine O-succinyltransferase (HST).
We developed a method to classify proteins of a family based on their conserved genomic contexts. Each protein genomic context is compared against all others to determine syntenies. A graph is generated where nodes are input proteins connected by edges
Nearly 35% of the proteins from large-scale sequencing of microbial genomes are annotated with unknown function. Our objective is to explore, by analogy, active sites from proteins of unknown function using 3D tools in order to suggest enzymatic activities. The
There is a massive amount of sequence and structural data available, and the accumulation rate exceeds the pace of functional studies. One way to enhance functional assessment is to mine the available data to inform a strategy that can be
The proportion of protein sequences of unknown function in public databases stills very important (42% of UniProt sequences are labelled as “hypothetical”, “uncharacterized”, “unknown” or “putative”). On the other hand, a number of enzyme activities (about 30%) remain orphan (i.e.
Eddy Elisée had the honor of being interviewed by the TV program l’esprit sorcier as part of the MODAMDH project, which is being carried out in collaboration between the LCOB and LABGEM. The film can be viewed here : https://www.youtube.com/watch?v=KOU4pRxS9Wc
Mots-clefs : pangénome ; microbiologie environnementale ; évolution des génomes ; contexte génomique ; graphes La pangénomique vise à comprendre la variabilité génétique d’un groupe d’organismes, généralement d’une même espèce, en agrégeant l’information issue de l’ensemble des génomes disponibles plutôt que