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相关概念视频

Protein Glycosylation01:25

Protein Glycosylation

9.2K
Glycosylation, the most common post-translational modification for proteins, serves diverse functions. Adding sugars to proteins makes the proteins more resistant to proteolytic digestion. Glycosylated proteins can act as markers and receptors to promote cell-cell adhesion. Additionally, they have many essential quality control functions in the cell, such as correct protein folding and facilitating transport of misfolded proteins to the cytosol, which can be degraded.
Glycosylation occurs in...
9.2K

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相关实验视频

Updated: Jan 9, 2026

A Quantitative Glycomics and Proteomics Combined Purification Strategy
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GlySitePred:基于深度特征融合和NCR-CC采样技术识别甘修饰地点

Jiayue Liu1, Yun Zuo1, Youxu Tan2

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China.

Journal of chemical information and modeling
|December 11, 2025
PubMed
概括

GlySitePred使用先进的机器学习和蛋白质语言模型准确地预测蛋白质糖化位点. 这种计算工具克服了实验方法和现有算法的局限性,为糖化研究提供了实际支持.

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科学领域:

  • 生物化学和分子生物学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 蛋白质糖化是一种关键的翻译后修改,影响蛋白质功能和疾病.
  • 实验性糖化位点检测是耗时且昂贵的.
  • 现有的计算方法与不平衡的数据和特征冗余性作斗争,限制了预测准确性.

研究的目的:

  • 开发一个准确可靠的计算模型来预测蛋白质糖化位点.
  • 为了应对不平衡的数据和特征提取在糖化部位预测中的挑战.
  • 为研究蛋白质糖化研究的研究人员提供实用工具.

主要方法:

  • 从PLMD数据库中构建了一个高质量的人类蛋白质 lysine glycation 数据集.
  • 集成的传统 (AAC,Kmer,One-hot) 和先进 (ESM2,ProstT5) 功能提取方法与多层次的功能融合.
  • 使用NCR-CC低采样算法处理不平衡数据和XGBoost进行预测.
  • 使用SHAP和LIME进行模型解释性分析.

主要成果:

  • 与现有方法相比,GlySitePred模型在所有评估指标上显示出优异的预测性能.
  • 该模型通过透明的决策过程实现了精确的预测能力.
  • 该NCR-CC算法有效地缓解了数据不平衡问题.
  • SHAP和LIME分析提供了对该模型预测机制的见解.

结论:

  • GlySitePred在计算蛋白质糖化位点预测方面取得了重大进展.
  • 该模型的准确性,可解释性和实用的工具支持增强了糖化修饰研究.
  • 开源代码和交互式预测工具促进了更广泛的学术和实际应用.