Predicting the function of a protein is one core challenge in biology. Protein function is typically inferred using sequence comparison with proteins whose function is known. This is problematic in many ways. In particular, proteins can have very different sequences, and yet share the same function. Machine learning approaches are thus more and more investigated to go beyond sequence similarity when annotating protein functions in new organism.
This project is about investigating machine learning strategies to infer whether a protein is involved in a secretion system.
Secretion systems are complexes of proteins enabling a number of essential functions for prokaryotic organisms (bacteria and archaea) such as acquiring nutrients or invading host cells. There are currently 12 types of secretion systems known, but we have very strong evidence to believe there are more secretion systems types: annotating the entier set of genomes with those 12 types of secretion systems shows that some bacteria don’t have any. Because of the importance of secretion systems to interact with the environment and with each other, as well as the difficulty of finding novel secretion systems (12 years of research in an experimental lab for the last type of secretion system found), we believe that there are entire types of secretion systems yet to discover.
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