A Deep Learning Approach with Data Augmentation to Predict Novel Spider Neurotoxic Peptides.

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

SUBMITTER: Lee B 

PROVIDER: S-EPMC8619404 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature