CancerGram: An Effective Classifier for Differentiating Anticancer from Antimicrobial Peptides

Abstract

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 α-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.

Peptidome Profiling of <em>Bubalus Bubalis</em> Urine and Assessment of its Antimicrobial Activity against Mastitis-Causing Pathogens

“…While, hydrophobic Leu and Ile constituted 19% of the ACP sequences [48].Like AMPs, both positive net charge and hydrophobicity is needed for the functioning of ACPs, however, the anticancer activity does not improve by increasing net charge and hydrophobicity beyond a certain limit. A reciprocity between these two factors are important for the proper functioning ACPs [49].Our finding showed Cys, Gly, and Pro as frequently occurring amino acids The amino acid distribution pattern was aberrant considering the reports made by previous studies but a recent study backed Gly and Cys as abundant amino acids in ACPs [50].…”

Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides

“…In the ACPs datasets, KNN and RF of the classical algorithm are excellent, while AdaBoostM1, Bagging, Vote and Stacking of the ensemble algorithm show high accuracy. Compared with the previous model prediction methods for ACPs [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ], features were extracted based on the 3D structure of peptides for the first time, which can be used as a supplementary method for the prediction of ACPs. The extraction of features from the 3D structure of peptides can better reflect the state of peptides in organisms and analyze the properties of peptides from different perspectives.…”

“…Then, Hajisharififi et al [ 9 ] developed a model of SVM based on Chou’s pseudo-amino acid composition (PseAAC) and local alignment kernel. After that, researchers have established many predictors, for example, ACPP [ 10 ], iACP [ 11 ], MLACP [ 12 ], iACP-GAEnsC [ 13 ], ACPred-FL [ 14 ], SAP [ 15 ], TargetACP [ 16 ], ACPred [ 17 ], mACPpred [ 18 ], ACPred-Fuse [ 19 ], PTPD [ 20 ], ACP-DL [ 21 ], PEPred-suite [ 22 ], AntiCP 2.0 [ 23 ], CancerGram [ 24 ], DeepACP [ 25 ] and ENNAACT [ 26 ], most of them adopt diverse feature extraction methods combined with various machine learning algorithms. All of the above predictors performed well in distinguishing between ACPs and non-ACPs.…”

Mitochondria-Targeted Drug Delivery

“…Alternatively, the drugs maybe designed specifically. For instance, Burdukiewicz et al have showed a robust computational tool, CancerGram helping researchers to find appropriate anticancer peptides to target mitochondria in the cancer cells [12].…”

“…In conclusion, this Special Issue underlines importance of mitochondria in pathophysiological situation such as: genetic disorders [1,2], cardiovascular diseases [3,4], inflammation [5] and cancer [6][7][8][9][10]. Indicates new tools to selective target mitochondria in order to: rescue aberrant cell phenotype, change cell metabolism or induces mitochondria dependent cell death [1][2][3][4][5][6][7][8][9][10][11][12].…”