深度学习揭示环境 β-内酰胺酶的多样性与突变潜力
Deep learning-guided discovery reveals the diversity and mutational potential of environmental beta-lactamases
AI 导读
研究者结合蛋白质语言模型（PLM）与集成学习，从环境宏基因组中鉴定出超过 46,000 条横跨全部四类 Ambler 分类的候选 β-内酰胺酶。与已知酶约 30% 序列同源的候选者在大肠杆菌中表达后赋予多种 β-内酰胺类抗生素耐药性；结合逆折叠模型进一步发现可改变耐药谱的突变，包括增强碳青霉烯耐药性的替换。这些结果揭示了环境微生物组中未被充分表征的 β-内酰胺酶多样性及其突变灵活性。
正文 · 原文
Abstract:Beta-lactamases are a major mechanism of bacterial resistance to beta-lactam antibiotics; however, their functional diversity and mutational potential in environmental microbial communities remain partially understood. This study aimed to characterize environmental beta-lactamase diversity and investigate how sequence variation influences antibiotic resistance. By integrating protein language model (PLMs) with ensemble learning, we identified more than 46,000 putative beta-lactamases across all four Ambler classes from environmental metagenomes, widely distributed across diverse habitats, with distinct habitat-associated class compositions. The candidates sharing approximately 30% sequence identity with known beta-lactamases conferred resistance to multiple beta-lactam antibiotics when expressed in Escherichia coli. Combining inverse folding model with PLMs further identified mutations that altered resistance profiles, including substitutions that increased resistance to carbapenems. Predicted high-fitness sites exhibited greater natural sequence variability overall, with higher variability in wastewater-derived than ocean-derived enzymes. These findings reveal undercharacterized beta-lactamase diversity and substantial mutational flexibility in environmental microbiomes, providing a basis for investigating their potential contribution to the diversification of antibiotic resistance.
| Subjects: | Genomics (q-bio.GN); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.14568 [q-bio.GN] |
| (or arXiv:2611.14568v1 [q-bio.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14568 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qiandi Gao [view email]
[v1]
Mon, 9 Nov 2026 14:29:14 UTC (9,098 KB)
来源：arXiv cs.AI · arxiv.org