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English(EN) Deep learning-guided discovery reveals the diversity and mutational potential of environmental beta-lactamases

AI识别出46,000种新的β-内酰胺酶,具有抗生素耐药性潜力

研究人员利用蛋白质语言模型(PLMs)和集成学习,从环境宏基因组中识别出超过46,000种潜在的β-内酰胺酶。这些酶分布在各种生境中,属于所有四个Ambler类别,在E. coli中表达时可赋予对多种β-内酰胺类抗生素的耐药性。通过逆向折叠模型进行的进一步分析揭示了可改变耐药性谱系的特定突变,包括增强对碳青霉烯类抗生素的耐药性,并表明高适应性位点显示出更大的自然序列变异性,特别是与来自海洋的酶相比,来自废水的酶表现出更高的变异性。这项工作突显了环境中β-内酰胺酶广泛的、被低估的多样性及其突变适应性。 AI

影响 这项研究展示了AI在发现新生物实体和理解抗生素耐药性机制方面的强大能力。

排序理由 该集群包含一篇详细介绍生物信息学新研究方法和发现的科学论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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AI识别出46,000种新的β-内酰胺酶,具有抗生素耐药性潜力

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该集群包含一篇详细介绍生物信息学新研究方法和发现的科学论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Liangjun Ge, Qiandi Gao, Haiming Xu, Zhiwei Qin, Heqian Zhang ·

    深度学习指导的发现揭示了环境β-内酰胺酶的多样性和突变潜力

    arXiv:2611.14568v1 Announce Type: cross 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 …