ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides

Timmons, P.B.; Hewage, Chandralal M.

doi:10.1016/j.biopha.2020.111051

Cited by 33 publications

(24 citation statements)

References 86 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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. Compared with the existing prediction models such as ENNAACT [ 26 ] and AntiCP 2.0 [ 23 ], the ACPs model developed by us is very robust only by its accuracy and MCC. However, due consideration has to be given to the differences caused by different methods of extracting features, so it is uncertain whether our model is superior to other models.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Wang

Fei

et al. 2021

IJMS

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Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop accurate machine learning models for ACP prediction. In this study, features were extracted from the three-dimensional (3D) structure of peptides to develop the model, compared to most of the previous computational models, which are based on sequence information. In order to develop ACPs with more potency, more selectivity and less toxicity, the model for predicting ACPs, hemolytic peptides and toxic peptides were established by peptides 3D structure separately. Multiple datasets were collected according to whether the peptide sequence was chemically modified. After feature extraction and screening, diverse algorithms were used to build the model. Twelve models with excellent performance (Acc > 90%) in the ACPs mixed datasets were used to form a hybrid model to predict the candidate ACPs, and then the optimal model of hemolytic peptides (Acc = 73.68%) and toxic peptides (Acc = 85.5%) was used for safety prediction. Novel ACPs were found by using those models, and five peptides were randomly selected to determine their anticancer activity and toxic side effects in vitro experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhao

Wang

Fei

et al. 2021

IJMS

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“…Machine learning techniques, including deep learning, have previously been applied to other bioinformatic problems: DeepPPISP for the prediction of protein-protein interaction sites [34] , SCLpred and SCLpred-EMS for protein subcellular localization prediction [35,36] , CPPpred for the prediction of cell-penetrating peptides [37] , HAPPENN for the prediction of peptide hemolytic activity [38] , ENNAACT for the prediction of peptide anticancer activity, [39] and ENNAVIA for the prediction of peptide antiviral activity [ ? ] .…”

Section: Introductionmentioning

confidence: 99%

APPTEST is an innovative new method for the automatic prediction of peptide tertiary structures

Timmons

Hewage²

2021

Preprint

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Good knowledge of a peptide's tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational method that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5-40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9\r{A} from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97\% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and Peplook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more-native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.

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“…The most popular machine learning methods employed are support vector machines or random forests, although a number of others have also been trialled. Many areas of bioinformatics have benefited from the predictive power of deep learning; neural network-based methods exist for many tasks, such as DeepPPISP for the prediction of protein-protein interaction sites [38] , SCLpred and SCLpred-EMS for protein subcellular localization prediction [39,40] , CPPpred for the prediction of cell-penetrating peptides [41] , HAPPENN for the prediction of peptide hemolytic activity [42] , ENNAACT for the prediction of peptide anticancer activity, [43] and APPTEST for the prediction of peptide tertiary structure [44] . As the quantity of antiviral peptide sequence data continuously increases, we have exploited the available data to create a deep neural network method for the identification of antiviral peptides from the primary sequence.…”

Section: Introductionmentioning

confidence: 99%

ENNAVIA is an innovative new method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides

Timmons

Hewage

2021

Preprint

Self Cite

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Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method out-performs the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.

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ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides

Cited by 33 publications

References 86 publications

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

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

APPTEST is an innovative new method for the automatic prediction of peptide tertiary structures

ENNAVIA is an innovative new method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides

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