ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides
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 t...
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doaj-ddc539045ec54361bf05f4fdcab858f42020-11-25T01:17:09ZengMDPI AGMolecules1420-30492019-05-012410197310.3390/molecules24101973molecules24101973ACPred: A Computational Tool for the Prediction and Analysis of Anticancer PeptidesNalini Schaduangrat0Chanin Nantasenamat1Virapong Prachayasittikul2Watshara Shoombuatong3Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandDepartment of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandAnticancer 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.https://www.mdpi.com/1420-3049/24/10/1973anticancer peptidetherapeutic peptidessupport vector machinerandom forestmachine learningclassification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nalini Schaduangrat Chanin Nantasenamat Virapong Prachayasittikul Watshara Shoombuatong |
spellingShingle |
Nalini Schaduangrat Chanin Nantasenamat Virapong Prachayasittikul Watshara Shoombuatong ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides Molecules anticancer peptide therapeutic peptides support vector machine random forest machine learning classification |
author_facet |
Nalini Schaduangrat Chanin Nantasenamat Virapong Prachayasittikul Watshara Shoombuatong |
author_sort |
Nalini Schaduangrat |
title |
ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides |
title_short |
ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides |
title_full |
ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides |
title_fullStr |
ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides |
title_full_unstemmed |
ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides |
title_sort |
acpred: a computational tool for the prediction and analysis of anticancer peptides |
publisher |
MDPI AG |
series |
Molecules |
issn |
1420-3049 |
publishDate |
2019-05-01 |
description |
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. |
topic |
anticancer peptide therapeutic peptides support vector machine random forest machine learning classification |
url |
https://www.mdpi.com/1420-3049/24/10/1973 |
work_keys_str_mv |
AT nalinischaduangrat acpredacomputationaltoolforthepredictionandanalysisofanticancerpeptides AT chaninnantasenamat acpredacomputationaltoolforthepredictionandanalysisofanticancerpeptides AT virapongprachayasittikul acpredacomputationaltoolforthepredictionandanalysisofanticancerpeptides AT watsharashoombuatong acpredacomputationaltoolforthepredictionandanalysisofanticancerpeptides |
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