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Open AccessArticle

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

1
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
2
Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
*
Author to whom correspondence should be addressed.
Molecules 2019, 24(10), 1973; https://doi.org/10.3390/molecules24101973
Received: 1 April 2019 / Revised: 7 May 2019 / Accepted: 17 May 2019 / Published: 22 May 2019
Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online. View Full-Text
Keywords: anticancer peptide; therapeutic peptides; support vector machine; random forest; machine learning; classification anticancer peptide; therapeutic peptides; support vector machine; random forest; machine learning; classification
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MDPI and ACS Style

Schaduangrat, N.; Nantasenamat, C.; Prachayasittikul, V.; Shoombuatong, W. ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides. Molecules 2019, 24, 1973.

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