Macrel: antimicrobial peptide screening in genomes and metagenomes
Célio Dias Santos-Júnior1,2, Shaojun Pan1,2, Xing-Ming Zhao1,2
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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: https://big-data-biology.org/software/macrel.
Insights
Macrel is a new tool for discovering antimicrobial peptides (AMPs) from genomic data. It improves the prediction of these important molecules, aiding the fight against drug-resistant bacteria.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- Antimicrobial peptides (AMPs) offer a promising strategy against multidrug-resistant pathogens.
- Genomic and metagenomic data enable in silico discovery of novel AMPs.
- Standard gene prediction methods struggle with short peptides, necessitating alternative approaches.
Purpose of the Study:
- To develop an effective pipeline for identifying high-quality AMP candidates from (meta)genomic data.
- To address the limitations of existing gene prediction methods for short peptides.
Main Methods:
- Introduction of a novel set of 22 peptide features.
- Development of classifiers utilizing these features for AMP prediction.
- Implementation of an end-to-end pipeline named Macrel.
Main Results:
- Macrel achieves state-of-the-art performance in predicting antimicrobial and hemolytic activity.
- The pipeline demonstrates enhanced precision in AMP prediction compared to existing methods.
- Macrel successfully identifies high-quality AMP candidates from both simulated and real genomic data.
Conclusions:
- Macrel provides a robust solution for AMP discovery from (meta)genomic sequences.
- The tool enhances the precision of AMP prediction, crucial for tackling antimicrobial resistance.
- Macrel is available as open-source software and via a webserver for broader accessibility.
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