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Updated: Sep 9, 2025
Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes
Published on: March 3, 2023
DeepSEA: an alignment-free explainable approach to annotate antimicrobial resistance proteins
Tiago Cabral Borelli1,2,3, Alexandre Rossi Paschoal4,5, Ricardo Roberto da Silva6,7
1Computational Chemical Biology Laboratory, Department of BioMolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, 14040-900, Brazil.
Abstract:
Antimicrobial resistance (AMR) is one of the most concerning modern threats as it places a greater burden on health systems than HIV and malaria combined. Current surveillance strategies for tracking antimicrobial resistance (AMR) rely on genomic comparisons and depend on sequence alignment with strict similarity cutoffs of greater than 95%. Therefore, these methods have high false-negative error rates due to a lack of reference sequences with a representative coverage of AMR protein diversity. Deep learning has been used as an alternative to sequence alignment, as artificial neural networks can extract abstract features from data, thereby limiting the need for sequence comparisons. Here, a convolutional neural network (CNN) was trained to differentiate between antimicrobial resistance proteins and non-resistance proteins, and to annotate them in nine resistance classes. Our model demonstrated higher recall values (> 0.9) than the alignment-based approach for all protein classes tested. Additionally, our CNN architecture allowed us to investigate internal states and explain the model classification regarding protein domain feature importance related to antimicrobial molecule inactivation. Finally, we built an open-source bioinformatic tool ( https://github.com/computational-chemical-biology/DeepSEA-project ) that can be used to annotate antimicrobial resistance proteins and provide information on protein domains without sequence alignment.
Insights
Antimicrobial resistance (AMR) poses a significant global health threat. A new deep learning model accurately identifies AMR proteins, outperforming traditional methods and offering insights into resistance mechanisms.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Antimicrobial resistance (AMR) is a critical global health challenge, exceeding the impact of diseases like HIV and malaria.
- Current AMR surveillance relies on genomic comparisons with high similarity thresholds (>95%), leading to significant false-negative rates due to incomplete reference databases.
Purpose of the Study:
- To develop a deep learning approach for accurate identification and classification of antimicrobial resistance (AMR) proteins.
- To overcome the limitations of traditional sequence alignment methods in AMR surveillance.
Main Methods:
- A convolutional neural network (CNN) was trained to distinguish AMR proteins from non-resistance proteins.
- The CNN was designed to classify proteins into nine distinct resistance classes.
- The model's internal states were analyzed to understand feature importance in protein classification.
Main Results:
- The CNN model achieved high recall values (> 0.9) across all tested protein classes, surpassing alignment-based methods.
- The model successfully identified key protein domains involved in antimicrobial inactivation.
- An open-source bioinformatics tool, DeepSEA, was developed for annotating AMR proteins.
Conclusions:
- Deep learning, specifically CNNs, offers a superior alternative to sequence alignment for AMR protein surveillance.
- The developed tool provides valuable insights into AMR mechanisms and aids in tracking resistance.
- Accurate annotation of AMR proteins is crucial for effective global health strategies.
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