In Bioinformatics (Oxford, England)
MOTIVATION : Antimicrobial peptides (AMP) have the potential to inhibit multiple types of pathogens and to heal infections. Computational strategies can assist in characterizing novel AMPs from proteome or collections of synthetic sequences and discovering their functional abilities towards different microbial targets without intensive labor.
RESULTS : Here, we present a deep learning-based method for computer-aided novel AMP discovery that utilizes the transformer neural network architecture with knowledge from natural language processing to extract peptide sequence information. We implemented the method for two AMP-related tasks: the first is to discriminate AMPs from other peptides, and the second task is identifying AMPs functional activities related to seven different targets (gram-negative bacteria, gram-positive bacteria, fungi, viruses, cancer cells, parasites, and mammalian cell inhibition), which is a multi-label problem. In addition, asymmetric loss was adopted to resolve the intrinsic imbalance of dataset, particularly for the multi-label scenarios. The evaluation showed that our proposed scheme achieves the best performance for the first task (96.85% balanced accuracy) and has a more unbiased prediction for the second task (79.83% balanced accuracy averaged across all functional activities) when compared to that of strategies without imbalanced learning or deep learning.
AVAILABILITY : The source code of this study is available at https://github.com/BiOmicsLab/TransImbAMP.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.
Pang Yuxuan, Yao Lantian, Xu Jingyi, Wang Zhuo, Lee Tzong-Yi
2022-Nov-03
