An adaptation of Pfam profiles to predict protein sub-cellular localization in Gram positive bacteria

Predicting the sub-cellular localization of a protein can provide useful information to uncover its molecular functions. In this sense, numerous prediction techniques have been developed, which usually have been focused on global information of the protein or sequence alignments. However, several studies have shown that the functional nature of proteins is ruled by conserved sub-sequence patterns known as domains. In this paper, an alternative methodology (PfamFeat) for gram-positive bacterial sub-cellular localization was developed. PfamFeat is based on information provided by Pfam database, which stores a series of HMM-profiles describing common protein domains. The likelihood of a sequence, to be generated by a given HMM-profile, can be used to characterize sequences in order to use pattern recognition techniques. Success rates obtained with a simple one-nearest neighbor classifier demonstrate that this method is competitive with popular sub-cellular prediction algorithms and it constitutes a promising research trend.