Prediction of bacterial surface exposed proteins
Aleksandr Barinov1, Valentin Loux2, Pierre Nicolas2, Philippe Langella3, Emmanuelle
Maguin1, and Maarten van de Guchte1.
INRA, 1Génétique Microbienne, 2Mathématique Informatique et Génome, 3Ecologie et
Physiologie du Système Digestif, 78352 Jouy en Josas Cedex, France
Proteins that are exposed at the bacterial cell surface can mediate physical interactions
with the biotic and abiotic environment and are important in bacteria-host interactions like
adhesion to eukaryotic cells, pathogenicity and immunomodulation.
Existing in silico methods predict the global localization of proteins in bacteria,
omitting the potential surface exposition of membrane proteins. We therefore developed a
prediction scheme with the particular aim of identification of potentially surface exposed
proteins from Gram-positive bacteria. This scheme integrates information of the wellestablished algorithms HMMsearch, LipoP, SignalP, and TMMOD. Dataflow through the
scheme is directed by logical decisions and the structure of the scheme allows easy updating
through the integration of additional data analysis modules or customized parameter settings.
The performance of the prediction scheme was evaluated using experimental results
that have been reported for the surface proteome of Streptoccoccus pyogenes. The analysis
scheme was then applied to lactobacilli of the acidophilus group. This group of lactobacilli is
of particular interest as it contains commensal species from the human GI tract, several of
which are used as probiotics, as well as the dairy bacterium Lactobacillus delbrueckii ssp.
bulgaricus. The comparative analysis of these lactobacilli revealed qualitative and
quantitative differences with respect to potentially surface exposed and secreted proteins.
Using the results of the SurfG+ analysis, a number of surface exposed and secreted
proteins were selected for the further studies. The corresponding genes are expressed in
Lactococcus
lactis,
and
resulting
recombinant
strains
are
tested
in
vitro
for
immunomodulation and adhesion to GI tract epithelial cells.
The future application of this method to GI tract metagenome data should facilitate the
identification of proteins that may be involved in host-commensal interactions.