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A Deep Learning Method for Predicting the Minimum Inhibitory Concentration of Antimicrobial Peptides against Escherichia coli using Multi-Branch-CNN and Attention
Yan, J.; Zhang, B.; Zhou, M.; Campbell-Valois, F.-X.; Siu, S.W.I. A Deep Learning Method for Predicting the Minimum Inhibitory Concentration of Antimicrobial Peptides against
Escherichia Coli
Using Multi-Branch-CNN and Attention. mSystems 2023, doi:10.1128/msystems.00345-23.
Yan, J.; Zhang, B.; Zhou, M.; Campbell-Valois, F.-X.; Siu, S.W.I. A Deep Learning Method for Predicting the Minimum Inhibitory Concentration of Antimicrobial Peptides against
Escherichia Coli
Using Multi-Branch-CNN and Attention. mSystems 2023, doi:10.1128/msystems.00345-23.
Yan, J.; Zhang, B.; Zhou, M.; Campbell-Valois, F.-X.; Siu, S.W.I. A Deep Learning Method for Predicting the Minimum Inhibitory Concentration of Antimicrobial Peptides against
Escherichia Coli
Using Multi-Branch-CNN and Attention. mSystems 2023, doi:10.1128/msystems.00345-23.
Yan, J.; Zhang, B.; Zhou, M.; Campbell-Valois, F.-X.; Siu, S.W.I. A Deep Learning Method for Predicting the Minimum Inhibitory Concentration of Antimicrobial Peptides against
Escherichia Coli
Using Multi-Branch-CNN and Attention. mSystems 2023, doi:10.1128/msystems.00345-23.
Abstract
Antimicrobial peptides (AMPs) are a promising alternative to antibiotics to combat drug resistance in pathogenic bacteria. However, the development of AMPs with high potency and specificity remains a challenge, and new tools to evaluate antimicrobial activity are needed to accelerate the discovery process. As a step toward direct prediction of the experimental minimum inhibitory concentration (MIC) of AMPs, we proposed MBC-Attention, a combination of a multi-branch CNN architecture and attention mechanism. Using a curated dataset of 3929 AMP against Escherichia coli, the optimal MBC-Attention model achieved an average Pearson correlation coefficient of 0.775 and an RMSE of 0.533 (log μM) in three independent tests of 393 sequences each. This results in a 5–12% improvement in PCC and 7–13% improvement in RMSE compared with RF and SVM models. Ablation studies confirmed that both attention mechanisms contributed to performance improvement.
Keywords
Minimum Inhibitory Concentrations; Deep Learning; Regression; Antimicrobial peptides; Drug Discovery
Subject
Biology and Life Sciences, Immunology and Microbiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
