Antimicrobial peptides (AMPs) constitute a diverse group of bioactive molecules that provide multicellular organisms with protection against microorganisms, and microorganisms with weaponry for competition. Some AMPs can target cancer cells and they are called anticancer peptides (ACPs). Due to their small size, positive charge, hydrophobicity and amphipathicity, AMPs and ACPs interact with negatively charged components of biological membranes. AMPs preferentially permeabilize microbial membranes, but ACPs additionally target mitochondrial and plasma membrane of cancer cells. Taking into account the therapeutic potential of ACPs and millions of deaths due to cancer annually, it is of vital importance to find new cationic peptides that selectively destroy cancer cells. Therefore, efficient computational tools for ACP prediction are essential to identify the best ACP candidates without undertaking expensive experimental studies. CancerGram is a novel tool that uses stacked random forests and n-gram analysis for prediction of ACPs.
CancerGram requires the external package, CancerGramModel, which
contains models necessary to perform the prediction. The model
can be installed using
Maintainer: Michal Burdukiewicz <firstname.lastname@example.org>
Burdukiewicz M, Sidorczuk K, Rafacz D, Pietluch F, Bakala M, Slowik J, Gagat P. (2020) CancerGram: an effective classifier for differentiating anticancer from antimicrobial peptides. (submitted)