Predicting protein-peptide binding sites with a deep convolutional neural network

[Display omitted] Interactions between proteins and peptides influence biological functions. Predicting such bio-molecular interactions can lead to faster disease prevention and help in drug discovery. Experimental methods for determining protein-peptide binding sites are costly and time-consuming....

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Bibliographic Details
Published in:Journal of theoretical biology 2020-07, Vol.496, p.110278, Article 110278
Main Authors: Wardah, Wafaa, Dehzangi, Abdollah, Taherzadeh, Ghazaleh, Rashid, Mahmood A., Khan, M.G.M., Tsunoda, Tatsuhiko, Sharma, Alok
Format: Article
Language:English
Subjects:
Artificial intelligence
Convolutional neural network
Deep learning
Protein sequence
Protein-peptide binding
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Summary:[Display omitted] Interactions between proteins and peptides influence biological functions. Predicting such bio-molecular interactions can lead to faster disease prevention and help in drug discovery. Experimental methods for determining protein-peptide binding sites are costly and time-consuming. Therefore, computational methods have become prevalent. However, existing models show extremely low detection rates of actual peptide binding sites in proteins. To address this problem, we employed a two-stage technique - first, we extracted the relevant features from protein sequences and transformed them into images applying a novel method and then, we applied a convolutional neural network to identify the peptide binding sites in proteins. We found that our approach achieves 67% sensitivity or recall (true positive rate) surpassing existing methods by over 35%.
ISSN:0022-5193
1095-8541
DOI:10.1016/j.jtbi.2020.110278