AMPFinder: A computational model to identify antimicrobial peptides and their functions based on sequence-derived information

Antimicrobial peptides (AMPs) called host defense peptides have existed among all classes of life with 5–100 amino acids generally and can kill mycobacteria, envelop viruses, bacteria, fungi, cancerous cells and so on. Owing to the non-drug resistance of AMP, it has been a wonderful agent to find novel therapies. Therefore, it is urgent to identify AMPs and predict their function in a high-throughput way. In this paper, we propose a cascaded computational model to identify AMPs and their functional type based on sequence-derived and life language embedding, called AMPFinder. Compared with other state-of-the-art methods, AMPFinder obtains higher performance both on AMP identification and AMP function prediction. AMPFinder shows better performance with improvement of F1-score (1.45%–6.13%), MCC (2.92%–12.86%) and AUC (5.13%–8.56%) and AP (9.20%–21.07%) on an independent test dataset. And AMPFinder achieve lower bias of R2 on a public dataset by 10-fold cross-validation with an improvement of (18.82%–19.46%). The comparison with other state-of-the-art methods shows that AMP can accurately identify AMP and its function types. The datasets, source code and user-friendly application are available at https://github.com/abcair/AMPFinder.