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Background <p>Efflux proteins (EPs) are key transporters responsible for expelling antibiotics and other harmful substances from bacterial cells, thus contributing significantly to antimicrobial resistance. As such, the development of a reliable and efficient computational method for identifying EPs in bacteria and archaea is of utmost importance. However, current computational methods for EP identification are limited and suffer from various performance and utility constraints.</p> Results <p>This study presents EPDiscovery, a stacking-based ensemble classifier designed to predict prokaryotic EPs directly from their amino acid sequence. EPDiscovery employs the MLP algorithm to integrate the predictions of fifteen baseline classifiers. The test results demonstrated that EPDiscovery accurately identified EPs, achieving AUROC, accuracy, F-value, and Matthews correlation coefficient scores of 0.970, 0.926, 0.927, and 0.852, respectively. Additionally, EPDiscovery outperformed the existing representative method and demonstrated consistent and satisfactory performance, with AUROC scores exceeding 0.9 across three previously proposed datasets. Feature importance analysis using local interpretable model-agnostic explanations further confirmed EPDiscovery’s ability to discern the characteristics of EPs from protein sequence information.</p> Conclusions <p>EPDiscovery offers a reliable alternative for annotating efflux proteins in large-scale bacterial genome studies, thus accelerating the discovery of potential EPs and contributing to the advancement of antimicrobial resistance research.</p>

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A stacked ensemble classifier for the discovery of prokaryotic efflux proteins based on sequence information

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Qian Wang,
  • Qiang Yue,
  • Zhangbin Tao,
  • Teng Xu,
  • Jianchao Ying

摘要

Background

Efflux proteins (EPs) are key transporters responsible for expelling antibiotics and other harmful substances from bacterial cells, thus contributing significantly to antimicrobial resistance. As such, the development of a reliable and efficient computational method for identifying EPs in bacteria and archaea is of utmost importance. However, current computational methods for EP identification are limited and suffer from various performance and utility constraints.

Results

This study presents EPDiscovery, a stacking-based ensemble classifier designed to predict prokaryotic EPs directly from their amino acid sequence. EPDiscovery employs the MLP algorithm to integrate the predictions of fifteen baseline classifiers. The test results demonstrated that EPDiscovery accurately identified EPs, achieving AUROC, accuracy, F-value, and Matthews correlation coefficient scores of 0.970, 0.926, 0.927, and 0.852, respectively. Additionally, EPDiscovery outperformed the existing representative method and demonstrated consistent and satisfactory performance, with AUROC scores exceeding 0.9 across three previously proposed datasets. Feature importance analysis using local interpretable model-agnostic explanations further confirmed EPDiscovery’s ability to discern the characteristics of EPs from protein sequence information.

Conclusions

EPDiscovery offers a reliable alternative for annotating efflux proteins in large-scale bacterial genome studies, thus accelerating the discovery of potential EPs and contributing to the advancement of antimicrobial resistance research.