EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides

Ge, Ruiquan; Feng, Guanwen; Jing, Xiaoyang; Zhang, Renfeng; Wang, Pu; Wu, Qing

doi:10.3389/fgene.2020.00760

Cited by 34 publications

(25 citation statements)

References 59 publications

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“…A total of 1,390 experimentally confirmed ACPs were collected from the published literatures 12 , 16 , 29 , 36 and public databases, including APD3 17 , CancerPPD 18 and SATPdb 19 . Although we had collected the experimentally validated ACPs from published databases and literatures as well as possible, but with only slightly more than a thousand records.…”

Section: Methodsmentioning

confidence: 99%

Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties

Tseng

Kao

Chen

et al. 2021

Sci Rep

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Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity to normal cells. As the number of experimentally verified bioactive peptides has increased significantly, various of in silico approaches are imperative for investigating the characteristics of ACPs. However, the lack of methods for investigating the differences in physicochemical properties of ACPs. In this study, we compared the N- and C-terminal amino acid composition for each peptide, there are three major subtypes of ACPs that are defined based on the distribution of positively charged residues. For the first time, we were motivated to develop a two-step machine learning model for identification of the subtypes of ACPs, which classify the input data into the corresponding group before applying the classifier. Further, to improve the predictive power, the hybrid feature sets were considered for prediction. Evaluation by five-fold cross-validation showed that the two-step model trained with sequence-based features and physicochemical properties was most effective in discriminating between ACPs and non-ACPs. The two-step model trained with the hybrid features performed well, with a sensitivity of 86.75%, a specificity of 85.75%, an accuracy of 86.08%, and a Matthews Correlation Coefficient value of 0.703. Furthermore, the model also consistently provides the effective performance in independent testing set, with sensitivity of 77.6%, specificity of 94.74%, accuracy of 88.99% and the MCC value reached 0.75. Finally, the two-step model has been implemented as a web-based tool, namely iDACP, which is now freely available at http://mer.hc.mmh.org.tw/iDACP/.

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Section: Methodsmentioning

confidence: 99%

Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties

Tseng

Kao

Chen

et al. 2021

Sci Rep

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“…They used feature representation, composed by AAC, autocorrelation, pseudoAAC, and profile-based features generating 19 kinds of feature patterns, which were first classified using light gradient boosting machine. Then, the predicted results were the input into an SVM classifier to obtain the final prediction [136].…”

Section: Automated Computational Methods For Anticancer Peptide Predictionmentioning

confidence: 99%

Peptides with Dual Antimicrobial–Anticancer Activity: Strategies to Overcome Peptide Limitations and Rational Design of Anticancer Peptides

2020

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Peptides are naturally produced by all organisms and exhibit a wide range of physiological, immunomodulatory, and wound healing functions. Furthermore, they can provide with protection against microorganisms and tumor cells. Their multifaceted performance, high selectivity, and reduced toxicity have positioned them as effective therapeutic agents, representing a positive economic impact for pharmaceutical companies. Currently, efforts have been made to invest in the development of new peptides with antimicrobial and anticancer properties, but the poor stability of these molecules in physiological environments has triggered a bottleneck. Therefore, some tools, such as nanotechnology and in silico approaches can be applied as alternatives to try to overcome these obstacles. In silico studies provide a priori knowledge that can lead to the development of new anticancer peptides with enhanced biological activity and improved stability. This review focuses on the current status of research in peptides with dual antimicrobial–anticancer activity, including advances in computational biology using in silico analyses as a powerful tool for the study and rational design of these types of peptides.

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“…In 2020, Ge Ruiquan et al proposed a machine model called EnACP, which introduces sequence composition, sequence-order, physicochemical properties, etc. to encode a peptide sequence and input the important feature selected by multiple ensemble classifiers to an SVM model to predict ACPs [ 9 ]. These methods try to find effective and useful features to represent a peptide and combine a high-performance machine model to identify ACPs.…”

Section: Introductionmentioning

confidence: 99%

ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information

Sun

Yang

et al. 2022

Molecules

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Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs.

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EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides

Cited by 34 publications

References 59 publications

Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties

Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties

Peptides with Dual Antimicrobial–Anticancer Activity: Strategies to Overcome Peptide Limitations and Rational Design of Anticancer Peptides

ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information

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