基于蛋白质预训练的大型语言模型和分数顺序卷积神经网络的蛋白质-ATP结合位点的准确预测
Mengyao Guo1, Yuhang Tu1, Jiali Yu1
1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China.
Scientific reports
|November 25, 2025
概括
一种新的基于大型语言模型 (LLM) 的方法,PFDCNN,只使用蛋白质序列,准确地预测蛋白质-腺三酸盐 (ATP) 结合点. 这一进步有助于药物设计和疾病治疗,改善了对蛋白质-ATP相互作用的理解.
科学领域:
- 生物化学 生物化学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 腺三酸盐 (ATP) 对于细胞能量至关重要. 蛋白质-ATP相互作用对生物功能至关重要,因此准确的结合部位预测对于药物设计和疾病研究至关重要.
- 目前用于预测蛋白质-ATP结合位点的方法往往不灵活,需要复杂的特征提取,并可能丢失序列信息.
研究的目的:
- 开发一种新,准确和高效的模型,仅使用蛋白序列信息来预测蛋白质-ATP结合位点.
- 通过整合大型语言模型 (LLM) 和深层卷积神经网络来克服现有方法的局限性.
主要方法:
- 开发了一种基于大型语言模型 (LLM) 的新型模型,即预训练的分数顺序深卷积神经网络 (PFDCNN).
- 该模型利用由预训练的蛋白质LLM提取的序列信息,并使用具有分数顺序反向传播的深 convolutional 神经网络.
- 使用修改后的损失函数来解决数据不平衡问题.
主要成果:
- 该PFDCNN模型在蛋白质-ATP结合部位数据集上表现出卓越的概括能力.
- 该模型在两个基准数据集上实现了高精度 (0.99和0.984) 和AUC值 (0.965和0.941).
- 性能超过了大多数现有的蛋白质结合部位预测模型.
结论:
- 根据序列数据,PFDCNN模型在准确高效地预测蛋白质-ATP结合位点方面取得了重大进展.
- 这种方法有望通过提高对蛋白质-ATP相互作用的理解来改善疾病预防,诊断,治疗和药物设计.
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