Abstract
MotivationAntibiotic resistance constitutes 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 the available choices of antimicrobials. However, the discovery of new bacteriocins by genomic mining is hampered by their sequences’ low complexity and high variance, which frustrates sequence similarity-based searches.ResultsHere we use word embeddings of protein sequences to represent bacteriocins, and apply a word embedding method that accounts for amino acid order in protein sequences, to predict novel bacteriocins from protein sequences without using sequence similarity. 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 peptide and protein classification problems for which sequence similarity cannot be used.Availability and implementationData and source code for this project are freely available at: https://github.com/nafizh/NeuBI.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
The discovery of antibiotics ranks among the greatest achievements of modern medicine
Our results show that a word embedding representation with Recurrent Neural Network (RNN) can classify bacteriocins better than current tools and algorithms for biological sequence classification
We developed a machine learning approach for predicting bacteriocins
Summary
The discovery of antibiotics ranks among the greatest achievements of modern medicine. Antibiotics have eradicated many infectious diseases and enabled many medical procedures that would have otherwise been fatal, including modern surgery, organ transplants, and immunosupressive treatments. Due to the prevalent use of antibiotics in healthcare and agriculture, antibiotic resistant bacteria have been emerging in unprecedented scales. 23,000 people in the US alone die from infections caused by antibiotic resistant bacteria [1]. One strategy to combat antibiotic resistance is to search for antimicrobial compounds other than antibiotics, and which may not be as prone to resistance. A promising class of such compounds are the peptide-based antimicrobials known as bacteriocins [2, 3]. Bacteriocins generally have a narrow killing spectrum making them attractive antimicrobials that would generate less resistance [4]
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