A deep learning approach with data augmentation to predict novel spider neurotoxic peptides

Byungjo Lee, Min Kyoung Shin, In Wook Hwang, Junghyun Jung, Yu Jeong Shim, Go Woon Kim, Seung Tae Kim, Wonhee Jang, Jung Suk Sung

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these peptides. However, the major hurdle in developing a deep learning model is the limited data on neurotoxic peptides. Here, we present a peptide data augmentation method that improves the recognition of neurotoxic peptides via a convolutional neural network model. The neurotoxic peptides were augmented with the known neurotoxic peptides from UniProt database, and the models were trained using a training set with or without the generated sequences to verify the augmented data. The model trained with the augmented dataset outperformed the one with the unaugmented dataset, achieving accuracy of 0.9953, precision of 0.9922, recall of 0.9984, and F1 score of 0.9953 in simulation dataset. From the set of all RNA transcripts of Callobius koreanus spider, we discovered neurotoxic peptides via the model, resulting in 275 putative peptides of which 252 novel sequences and only 23 sequences showing homology with the known peptides by Basic Local Alignment Search Tool. Among these 275 peptides, four were selected and shown to have neuromodulatory effects on the human neuroblastoma cell line SH-SY5Y. The augmentation method presented here may be applied to the identification of other functional peptides from biological resources with insufficient data.

Original languageEnglish
Article number12291
JournalInternational Journal of Molecular Sciences
Volume22
Issue number22
DOIs
StatePublished - 1 Nov 2021

Keywords

  • Convolutional neural network
  • Data augmentation
  • Deep learning
  • Neurotoxic peptide prediction
  • Spider transcriptome

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