AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning
In: Briefings in Bioinformatics, Jg. 24 (2023)
Online
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Zugriff:
Recently, peptide-based drugs have gained unprecedented interest in discovering and developing antifungal drugs due to their high efficacy, broad-spectrum activity, low toxicity and few side effects. However, it is time-consuming and expensive to identify antifungal peptides (AFPs) experimentally. Therefore, computational methods for accurately predicting AFPs are highly required. In this work, we develop AFP-MFL, a novel deep learning model that predicts AFPs only relying on peptide sequences without using any structural information. AFP-MFL first constructs comprehensive feature profiles of AFPs, including contextual semantic information derived from a pre-trained protein language model, evolutionary information, and physicochemical properties. Subsequently, the co-attention mechanism is utilized to integrate contextual semantic information with evolutionary information and physicochemical properties separately. Extensive experiments show that AFP-MFL outperforms state-of-the-art models on four independent test datasets. Furthermore, the SHAP method is employed to explore each feature contribution to the AFPs prediction. Finally, a user-friendly web server of the proposed AFP-MFL is developed and freely accessible at http://inner.wei-group.net/AFPMFL/, which can be considered as a powerful tool for the rapid screening and identification of novel AFPs.
Titel: |
AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning
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Autor/in / Beteiligte Person: | Fang, Yitian ; Xu, Fan ; Wei, Lesong ; Jiang, Yi ; Chen, Jie ; Wei, Leyi ; Wei, Dong-Qing |
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Zeitschrift: | Briefings in Bioinformatics, Jg. 24 (2023) |
Veröffentlichung: | Oxford University Press (OUP), 2023 |
Medientyp: | unknown |
ISSN: | 1477-4054 (print) ; 1467-5463 (print) |
DOI: | 10.1093/bib/bbac606 |
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