Scholarlink

<p>The challenge of assessing intracellular accumulation represents a major hurdle to the discovery of new antibiotics with Gram-negative activity. To address this, a high-throughput assay was developed to measure compound uptake and retention in <i>Escherichia coli</i> using LC/MS. 13,056 diverse small molecules were screened with two isogenic <i>E. coli</i> strains, a wild-type and a TolC-deleted mutant. Cell-associated concentrations of 8,410 compounds were determined and 6,416 compounds were classified either as retention-positive or -negative, with 45% (2,885) positives in the TolC mutant. Of these, 60% were not retained in the wild-type strain, indicating efficient efflux. No individual structural feature or physicochemical property explained the retention behavior. Machine learning (ML) models were trained using these results, and a gradient-boosted-tree model using 36 physicochemical compound descriptors proved most accurate. The ML model demonstrated robust performance across similar and dissimilar molecule subsets, showcasing its strong generalization and real-world predictive potential. An experimental validation of the tool was performed with a set of 540 new compounds and correctly predicted retention-positive cases in 77.8% and retention-negative in 74.4%. This assay and prediction tool could enhance Gram-negative antibiotic discovery, aiding in screening library design, computational structure-based drug design, and exploration of chemical space before synthesis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Measurement and prediction of small molecule retention by Gram-negative bacteria based on a large-scale LC/MS screen

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月14日

查看全文
  • François Le Goff,
  • Julien Hazemann,
  • Lukas Christen,
  • Geoffroy Bourquin,
  • Gabin Pierlot,
  • Roland Lange,
  • Philippe Panchaud,
  • Cornelia Zumbrunn,
  • Oliver Peter,
  • Georg Rueedi,
  • Daniel Ritz

摘要

The challenge of assessing intracellular accumulation represents a major hurdle to the discovery of new antibiotics with Gram-negative activity. To address this, a high-throughput assay was developed to measure compound uptake and retention in Escherichia coli using LC/MS. 13,056 diverse small molecules were screened with two isogenic E. coli strains, a wild-type and a TolC-deleted mutant. Cell-associated concentrations of 8,410 compounds were determined and 6,416 compounds were classified either as retention-positive or -negative, with 45% (2,885) positives in the TolC mutant. Of these, 60% were not retained in the wild-type strain, indicating efficient efflux. No individual structural feature or physicochemical property explained the retention behavior. Machine learning (ML) models were trained using these results, and a gradient-boosted-tree model using 36 physicochemical compound descriptors proved most accurate. The ML model demonstrated robust performance across similar and dissimilar molecule subsets, showcasing its strong generalization and real-world predictive potential. An experimental validation of the tool was performed with a set of 540 new compounds and correctly predicted retention-positive cases in 77.8% and retention-negative in 74.4%. This assay and prediction tool could enhance Gram-negative antibiotic discovery, aiding in screening library design, computational structure-based drug design, and exploration of chemical space before synthesis.