使用蛋白序列数据预测微生物代谢中碳和源偏好
Zhenfeng Wang1, Shuzhen Li1, Haixia Pan1
1School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Liaoning, China.
Journal of microbiological methods
|September 18, 2025
概括
这项研究使用深度学习来从蛋白质序列预测微生物的营养需求. 它准确地确定了碳和的需求,帮助环境污染物修复工作.
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.
- 环境科学 环境科学
背景情况:
- 微生物培养对于环境污染物修复至关重要.
- 准确量化微生物营养需求对于优化生长和生产率至关重要.
- 了解碳和的需求是精确种植的关键.
研究的目的:
- 研究微生物蛋白序列与它们的特定营养需求 (碳/来源,C/N比) 之间的联系.
- 通过深度学习开发微生物营养利用的预测模型.
- 为优化微生物培养条件提供见解.
主要方法:
- 从数据库 (Ensembl Bacteria,DSMZ,NCBI Protein) 收集了432种微生物和61种培养基的数据.
- 使用位置特定得分矩阵 (PSSM) 和伪位置特定得分矩阵 (PsePSSM) 将蛋白质序列转换为数值特征矩阵.
- 应用机器学习算法和夏普利添加式解释 (SHAP) 用于预测和解释性.
主要成果:
- 在预测碳与 (C/N) 比率 (99.6%) 方面取得了很高的准确性.
- 预测的碳源利用率准确度为82.76%,源利用率准确度为70.05%.
- 证明微生物蛋白序列与它们的C/N需求之间存在强烈的相关性.
结论:
- 蛋白质序列包含有关微生物营养需求的内在信息.
- 结合蛋白质注释和序列分析的综合框架可以预测微生物的营养需求.
- 这种方法为优化微生物种植环境应用提供了宝贵的见解.
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