TOXIFY: a deep learning approach to classify animal venom proteins

PeerJ

T. Jeffrey Cole¹  Michael S. Brewer¹ 

[1] Department of Biology, East Carolina University, Greenville, NC, United States of America;

关键词: Venom;    Deep learning;    Protein classification;    Transcriptome;    Proteome;   

DOI  :  10.7717/peerj.7200

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown