深度学习的预测提出了大量的蛋白质-碳水化合物相互作用
Samuel W Canner1, Ronald L Schnaar2,3, Jeffrey J Gray1,4
1Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, United States.
bioRxiv : the preprint server for biology
|March 31, 2025
概括
我们开发了计算工具,碳水化合物预测蛋白相互作用 (PiCAP) 和碳水化合物蛋白位点识别器2 (CAPSIF2),以识别蛋白质碳水化合物相互作用. 这些工具可以高精度地预测碳水化合物结合蛋白及其相互作用部位,揭示关键蛋白质组中的广泛相互作用.
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:
- 识别蛋白质-碳水化合物相互作用对于理解生物过程至关重要,但实验方法资源密集.
- 目前的估计表明,只有1.5-5%的蛋白质与碳水化合物结合,与已知的50-70%的蛋白质糖化相比.
- 在准确预测碳水化合物结合蛋白及其相互作用部位方面存在很大的差距.
研究的目的:
- 开发新的计算工具来预测蛋白质-碳水化合物结合,并识别特定的相互作用部位.
- 建立一个强大的数据集,用于培训和验证蛋白质碳水化合物相互作用的预测模型.
- 评估不同蛋白质组中碳水化合物结合蛋白的流行程度,并调查它们的生物学相关性.
主要方法:
- 开发了碳水化合物预测器 (PiCAP) 的蛋白相互作用,这是一个在已知的碳水化合物结合剂和非结合剂上训练的神经网络模型.
- 创建了碳水化合物蛋白位点标识符2 (CAPSIF2) 模型,以预测参与碳水化合物结合的特定蛋白质残留物.
- 利用一个精心策划的数据集,包括已知的结合剂,DNA结合蛋白,细胞骨成分,抗体和小分子结合剂,用于训练和验证.
主要成果:
- 在预测蛋白质水平碳水化合物结合/非结合方面,PiCAP实现了90%的平衡精度.
- 在CAPSIF2中,残留物水平预测的Dice系数为0.57,表现优于现有模型.
- 对人类神经细胞和蛋白质组 (大肠杆菌,肌肉菌,智人菌) 的应用预测了35-40%的蛋白质与碳水化合物结合.
结论:
- PiCAP 和 CAPSIF2 是有效的计算工具,用于预测蛋白质-碳水化合物相互作用和结合部位.
- 预计各种蛋白质组中的大量蛋白质参与碳水化合物结合.
- 这些发现强调了蛋白质-碳水化合物相互作用在细胞功能中的重要作用,并为进一步研究开辟了道路.
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