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Systematic analysis of supervised machine learning as an effective approach to predicate β-lactam resistance

Chaodong Zhang1,2, Yingjiao Ju1,3, Na Tang1,3

  • 1State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.

Briefings in Bioinformatics
|June 14, 2019
PubMed

Insights

Machine learning accurately predicts Streptococcus pneumoniae resistance to beta-lactam antibiotics using penicillin-binding protein gene fragments. This approach aids in understanding antimicrobial resistance and pneumococcal epidemiology.

Area of Science:

  • Microbiology
  • Genetics
  • Computational Biology

Background:

  • Streptococcus pneumoniae is a major human respiratory pathogen.
  • Increasing beta-lactam antibiotic resistance in S. pneumoniae is linked to altered penicillin-binding proteins (PBPs).
  • PBP variability and mosaic gene structures complicate resistance prediction.

Purpose of the Study:

  • To develop a machine learning strategy for predicting S. pneumoniae antimicrobial susceptibility to beta-lactam antibiotics.
  • To identify key PBP gene fragments for accurate resistance prediction.
  • To associate resistance phenotypes with serotypes and sequence types for epidemiological insights.

Main Methods:

  • Utilized supervised machine learning with published PBP sequences and minimum inhibitory concentration (MIC) values as labeled data.
  • Incorporated unlabeled PBP sequences from the NCBI database.
  • Focused on specific fragments of pbp2x and pbp2b genes to predict resistance to cefuroxime and amoxicillin.

Main Results:

  • Developed a predictive model for S. pneumoniae beta-lactam resistance using limited PBP gene fragments.
  • Validated the model's performance through mutant construction and testing of clinical isolates.
  • Established associations between resistance phenotypes, serotypes, and sequence types.

Conclusions:

  • Supervised machine learning offers a robust method for predicting antimicrobial susceptibility in S. pneumoniae.
  • This approach simplifies resistance prediction by using specific PBP gene fragments.
  • The findings contribute to understanding pneumococcal epidemiology and guiding treatment strategies.
Keywords:
Streptococcus pneumoniagenotypic antimicrobial susceptibility testingpenicillin-binding proteinsupervised machine learningβ-lactam resistance

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