DeepMineLys:从人类微生物组中深度挖掘菌素素
Yiran Fu1, Shuting Yu1, Jianfeng Li1
1School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.
Cell reports
|August 7, 2024
概括
一个新的AI框架DeepMineLys从人类微生物组数据中识别出强大的菌素溶酶. 这种方法显著优于现有的方法,发现了潜在的治疗应用的高度活性酶.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 巨大的 shotgun metagenomics 数据代表了一种尚未开发的资源,用于发现新的酶.
- 目前用于蛋白质开采的人工智能 (AI) 模型往往缺乏对未知数据集的强大外推能力.
研究的目的:
- 开发和验证一个深度学习框架,DeepMineLys,用于在人类微生物组数据集中识别菌素素.
- 评估DeepMineLys的性能与使用独立验证数据集的现有方法相比.
主要方法:
- 一个基于卷积神经网络 (CNN) 的框架,DeepMineLys,被开发用于挖掘菌素氨酸.
- 该框架应用于三个人类微生物组数据集.
- 素候选物被表达在大肠杆菌中,它们的酶活性经过实验验证.
主要成果:
- 在一个独立的数据集上,DeepMineLys获得了84.00%的F1分数,比目前的方法高20.84%.
- 在16种表达的lysin候选物中,有11种被证实是活性的.
- 鉴定到的最强效的氨酸表现出比蛋白酶高6.2倍的活性.
结论:
- DeepMineLys提供了一种强大而有效的框架,用于从元基因组数据中发现新型菌素氨酸.
- 鉴定到的氨酸代表了治疗应用的有希望的候选人,特别是对抗细菌感染.
- 该研究强调了人工智能在生物发现中的潜力,并建议该框架适用于挖掘其他蛋白质家族.
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