Dataset Information

Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.

ABSTRACT:

Motivation

Antibiotic 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.

Results

Here 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 implementation

Data and source code for this project are freely available at: https://github.com/nafizh/NeuBI.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Hamid MN

PROVIDER: S-EPMC6581433 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.

Hamid Md-Nafiz MN Friedberg Iddo I

Bioinformatics (Oxford, England) 20190601 12

<h4>Motivation</h4>Antibiotic 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.<h4>Results</h4>H ...[more]

PMID: 30418485

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Dataset Information

Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.

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