RT Journal Article SR Electronic T1 MACREL: antimicrobial peptide screening in genomes and metagenomes JF bioRxiv FD Cold Spring Harbor Laboratory SP 2019.12.17.880385 DO 10.1101/2019.12.17.880385 A1 Célio Dias Santos-Junior A1 Shaojun Pan A1 Xing-Ming Zhao A1 Luis Pedro Coelho YR 2020 UL http://biorxiv.org/content/early/2020/07/16/2019.12.17.880385.abstract AB Motivation Antimicrobial peptides (AMPs) have the potential to tackle multidrug-resistant pathogens in both clinical and non-clinical contexts. The recent growth in the availability of genomes and metagenomes provides an opportunity for in silico prediction of novel AMP molecules. However, due to the small size of these peptides, standard gene prospection methods cannot be applied in this domain and alternative approaches are necessary. In particular, standard gene prediction methods have low precision for short peptides, and functional classification by homology results in low recall.Results Here, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime). We demonstrate that Macrel recovers high-quality AMP candidates using realistic simulations and real data.Availability Macrel is implemented in Python 3. It is available as open source at https://github.com/BigDataBiology/macrel and through bioconda. Classification of peptides or prediction of AMPs in contigs can also be performed on the webserver: http://big-data-biology.org/software/macrel.Competing Interest StatementThe authors have declared no competing interest.