TOXIFY: a deep learning approach to classify animal venom proteins
- Subject Areas
- Bioinformatics, Computational Biology, Genomics
- Venom, Deep Learning, Protein Classification, Transcriptome, Proteome
- © 2019 Cole et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. TOXIFY: a deep learning approach to classify animal venom proteins. PeerJ Preprints 7:e27498v1 https://doi.org/10.7287/peerj.preprints.27498v1
In the era of Next-Generation Sequencing and shotgun proteomics, the sequences of animal toxigenic proteins are being generated at rates exceeding the pace of traditional means for empirical toxicity verification. To facilitate the automation of toxin identification from protein sequences, we trained Recurrent Neural Networks with Gated Recurrent Units on publicly available datasets. The resulting models are available via the novel software package TOXIFY, allowing users to infer the probability of a given protein sequence being a venom protein. TOXIFY is more than 20X faster and uses over an order of magnitude less memory than previously published methods. Additionally, TOXIFY is more accurate, precise, and sensitive at classifying venom proteins.
This is a submission to PeerJ for review.