DOGpred:一种新型的深度学习框架,用于准确识别人类O-链接的氨酸糖化位点
Ki Wook Lee1, Nhat Truong Pham1, Hye Jung Min1
1Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Republic of Korea.
Journal of molecular biology
|February 3, 2025
概括
我们开发了DOGpred,这是一种深度学习工具,可以准确地预测人类蛋白质上的O-链接氨酸糖化 (OTG) 位点. 这种计算方法通过补充实验方法来帮助疾病研究和治疗目标的发现.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 与O结合的糖基化是一种重要的翻译后修改,调节蛋白质功能.
- 与O相关的糖基化失调与各种疾病有关,需要准确的部位识别.
- 识别O结合的三氨酸糖化 (OTG) 位点的实验方法通常是复杂和昂贵的.
研究的目的:
- 开发一种精确的计算工具,用于预测人类O-链接的三氨酸糖化 (OTG) 位点.
- 利用深度学习和高级功能表示来改善OTG站点预测.
- 提供一个免费可访问的工具,以帮助研究与糖化相关的疾病.
主要方法:
- 开发了DOGpred,这是一个使用常规特征描述器 (CFD) 和蛋白质语言模型 (PLM) 嵌入的深度学习预测器.
- 将特征编码为2D张量,以捕捉顺序和固有的特征.
- 使用堆叠卷积神经网络 (CNN) 进行空间特征和循环神经网络 (RNN) 进行时间特征,通过基于注意力的融合进行集成.
主要成果:
- 最优的DOGpred模型,使用堆叠的1D CNN和基于注意力的RNN与交叉注意力融合,实现了卓越的性能.
- 在独立数据集上,DOGpred显著超过了现有的机器学习模型和最先进的方法.
- 该工具在精确识别人类OTG站点方面表现出高准确度.
结论:
- DOGpred提供了一种基于深度学习的强大而准确的方法,用于预测人类OTG站点.
- 该工具可以显著增强对疾病OTG失调的研究,并有助于识别治疗点.
- DOGpred是公开的,促进了在生物研究中的更广泛的可访问性和应用.
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