一个深度学习框架用于使用进化特征表示的氨酸2-氧化聚基位预测
Heba M Elreify1, Fathi E Abd El-Samie2,3, Moawad I Dessouky2
1Department of Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt. hebamohamedibrahim09@gmail.com.
Scientific reports
|November 6, 2025
概括
一个新的深度学习工具BLOS-Khib准确地预测了物种间的氨酸2-氧化基化 (Khib) 位点. 这种计算方法增强了对各种生物体中这种关键的翻译后修饰的理解.
科学领域:
- 生物化学和分子生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- lysine 2-hydroxyisobutyrylation (Khib) 是一种重要的翻译后修饰 (PTM),涉及基因表达和代谢调节.
- 目前对Khib遗址的精确计算预测是有限的,这阻碍了该领域的研究.
研究的目的:
- 开发和验证一种新的深度学习框架BLOS-Khib,用于对Khib遗址进行准确的跨物种预测.
- 为了确定与Khib修饰相关的保存和特定物种的序列特征.
主要方法:
- 开发了一个深度学习框架 (卷积神经网络 - CNN),将BLOSUM62矩阵的进化信息纳入其中.
- 系统优化确定了顺序上下文的最佳长 (43个氨基酸).
- 评估了6种不同的生物体的性能,包括人类,小麦,大米,T. gondii,Candida albicans和Botrytis cinerea.
主要成果:
- BLOS-Khib实现了高预测性能,ROC曲线下的面积 (AUC) 值在独立测试组中从0.885到0.913不等.
- 该框架的性能优于现有的最先进的方法,包括传统的机器学习和其他深度学习架构.
- 序列特征分析显示了保存的氨酸丰富区域和特定物种的侧面氨基酸偏好.
- 观察到模型在进化上遥远的生物体之间具有很高的可转移性.
结论:
- BLOS-Khib提供了一个强大而准确的计算工具,用于预测各种物种的Khib位置.
- 这项研究提供了对Khib修饰序列决定因素的进化见解,表明了融合进化.
- BLOS-Khib推进了PTM预测和了解Khib监管角色的领域.
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