CancerGram: An Effective Classifier for Differentiating Anticancer from Antimicrobial Peptides

被引:25
作者
Burdukiewicz, Michal [1 ,2 ]
Sidorczuk, Katarzyna [3 ]
Rafacz, Dominik [2 ,4 ]
Pietluch, Filip [3 ]
Bakala, Mateusz [4 ]
Slowik, Jadwiga [4 ]
Gagat, Przemyslaw [3 ]
机构
[1] Brandenburg Univ Technol Cottbus Senftenberg, Fac Nat Sci, D-01968 Senftenberg, Germany
[2] Why R Fdn, PL-03214 Warsaw, Poland
[3] Univ Wroc Aw, Fac Biotechnol, Dept Bioinformat & Genom, PL-50383 Wroclaw, Poland
[4] Warsaw Univ Technol, Fac Math & Informat Sci, PL-00662 Warsaw, Poland
关键词
anticancer peptide (ACP); antimicrobial peptide (AMP); anticancer peptides; antimicrobial peptides; host defense peptides; prediction; random forest; IN-VITRO; MITOCHONDRIA; APOPTOSIS; DATABASE; ORIGIN; POWER; VENOM;
D O I
10.3390/pharmaceutics12111045
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
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; thus, 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 membranes of cancer cells. The preference towards mitochondrial membranes is explained by their membrane potential, membrane composition resulting from alpha-proteobacterial origin and the fact that mitochondrial targeting signals could have evolved from AMPs. 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, to reduce the costs of experimental research, we have created a robust computational tool, CancerGram, that uses n-grams and random forests for predicting ACPs. Compared to other ACP classifiers, CancerGram is the first three-class model that effectively classifies peptides into: ACPs, AMPs and non-ACPs/non-AMPs, with AU1U amounting to 0.89 and a Kappa statistic of 0.65. CancerGram is available as a web server and R package on GitHub.
引用
收藏
页码:1 / 14
页数:14
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