Abstract
Antibiotic resistance is a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially-produced antimicrobial peptide products, are candidates for broadening our pool of antimicrobials. The discovery of new bacteriocins by genomic mining is hampered by their sequences' low complexity and high variance, which frustrates sequence similarity-based searches. Here we use word embeddings of protein sequences to represent bacteriocins, and subsequently apply Recurrent Neural Networks and Support Vector Machines to predict novel bacteriocins from protein sequences without using sequence similarity. We developed a word embedding method that accounts for sequence order, providing a better classification than a simple summation of the same word embeddings. We use the Uniprot/TrEMBL database to acquire the word embeddings taking advantage of a large volume of unlabeled data. Our method predicts, with a high probability, six yet unknown putative bacteriocins in Lactobacillus. Generalized, the representation of sequences with word embeddings preserving sequence order information can be applied to protein classification problems for which sequence homology cannot be used.
