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

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

doi:10.1101/2021.03.25.436982

Cited by 4 publications

(1 citation statement)

References 103 publications

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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 [ 35 ], SCLpred and SCLpred-EMS for protein subcellular localization prediction [ 36 , 37 ], CPPpred for the prediction of cell-penetrating peptides [ 38 ], HAPPENN for the prediction of peptide hemolytic activity [ 39 ], ENNAACT for the prediction of peptide anticancer activity, [ 40 ] and ENNAVIA for the prediction of peptide antiviral activity [ 41 ]. Indeed, deep learning has been applied to the prediction of protein secondary structures [ 42–44 ].…”

Section: Introductionmentioning

confidence: 99%

APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures

Timmons

Hewage

2021

Briefings in Bioinformatics

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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 protocol 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 1.9Å 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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Section: Introductionmentioning

confidence: 99%

APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures

Timmons

Hewage

2021

Briefings in Bioinformatics

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ACP-Dnnel: anti-coronavirus peptides’ prediction based on deep neural network ensemble learning

Liu

et al. 2023

Amino Acids

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HybAVPnet: a novel hybrid network architecture for antiviral peptides identification

Xia

Jiang

et al. 2022

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Motivation: The virus poses a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laborious wet chemical experiment methods, accurate and rapid identification of antiviral peptides using computational methods is critical. However, it is still challenging to extract effective feature representations from the sequences for the accurate identification of antiviral peptides. Results: This study introduces a novel two-step approach, named HybAVPnet, with a hybrid network architecture to identify antiviral peptides based on neural networks and traditional machine learning methods. Firstly, eighteen kinds of features are extracted to predict labels and probabilities by the neural network classifier and LightGBM classifier, respectively. Secondly, the support vector machine classifier is carried out using the predicted probability of the first step to make the final prediction. The experimental result shows HybAVPnet can achieve better and more robust performance compared with the state-of-the-art methods, especially on independent datasets, which makes it useful for the research and development of antiviral drugs. Meanwhile, it can also be extended to other peptide recognition problems because of its generalization ability. Availability and implementation: The predicted model could be downloaded from: https://github.com/greyspring/HybAVPnet Contact: gespring@hdu.edu.cn; yp.cai@siat.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

show abstract

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

Cited by 4 publications

References 103 publications

APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures

APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures

ACP-Dnnel: anti-coronavirus peptides’ prediction based on deep neural network ensemble learning

HybAVPnet: a novel hybrid network architecture for antiviral peptides identification

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