amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool

Travis J Lawrence; Dana L Carper; Margaret K Spangler; Alyssa A Carrell; Tomás A Rush; Stephen J Minter; David J Weston; Jessy L Labbé

doi:10.1093/bioinformatics/btaa917

amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool

Bioinformatics. 2021 Aug 4;37(14):2058-2060. doi: 10.1093/bioinformatics/btaa917.

Authors

Travis J Lawrence¹, Dana L Carper¹, Margaret K Spangler¹, Alyssa A Carrell¹, Tomás A Rush¹, Stephen J Minter², David J Weston¹, Jessy L Labbé¹

Affiliations

¹ Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
² Cryomagnetics, Inc., Oak Ridge, TN 37830, USA.

PMID: 33135060
DOI: 10.1093/bioinformatics/btaa917

Abstract

Summary: Antimicrobial peptides (AMPs) are promising alternative antimicrobial agents. Currently, however, portable, user-friendly and efficient methods for predicting AMP sequences from genome-scale data are not readily available. Here we present amPEPpy, an open-source, multi-threaded command-line application for predicting AMP sequences using a random forest classifier.

Availability and implementation: amPEPpy is implemented in Python 3 and is freely available through GitHub (https://github.com/tlawrence3/amPEPpy).

Supplementary information: Supplementary data are available at Bioinformatics online.

Published by Oxford University Press 2020.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Genome*
Pore Forming Cytotoxic Proteins
Software*

Substances

Pore Forming Cytotoxic Proteins

Abstract

Publication types

MeSH terms

Substances

Grants and funding