深度ProBind:用基于变压器的深度学习模型进行蛋白质结合预测
Salman Khan1, Sumaiya Noor2, Hamid Hussain Awan3
1Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, KPK, Pakistan.
BMC bioinformatics
|March 23, 2025
概括
通过整合序列和结构数据,Deep-ProBind准确地预测蛋白质结合. 这种新的计算模型为研究人员提供了可靠和有效的工具,推进了药理学研究.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
背景情况:
- 结合蛋白对细胞过程至关重要,调节DNA,RNA和相互作用.
- 通过实验识别蛋白结合是昂贵和耗时的.
- 由于特征集成有限,现有的基于序列的方法缺乏准确性.
研究的目的:
- 开发Deep-ProBind,一种用于预测蛋白结合的新型计算模型.
- 整合序列和结构信息,以提高预测准确度.
- 为加速药物发现和药理学研究提供可靠的工具.
主要方法:
- 利用来自变压器的双向编码器表示 (BERT) 和伪位置特定得分矩阵 - 离散波段变换 (PsePSSM -DWT) 进行编码.
- 采用变压器和基于进化的注意力机制来提取特征.
- 应用了SHapley添加式扩展算法 (SHAP) 进行最佳特征选择和深度神经网络 (DNN) 进行分类.
主要成果:
- 通过十倍的交叉验证,Deep-ProBind实现了92.67%的准确性,在独立样本上达到93.62%的准确性.
- 在培训数据上表现比现有模型高3.57%,在独立测试中表现比现有模型高1.52%
- 在对蛋白质结合的分类中证明了高可靠性和有效性.
结论:
- 深度ProBind在预测蛋白质结合方面取得了重大进展.
- 序列和结构数据的整合提高了预测性能.
- 该模型是药理学研究和治疗开发的宝贵资源.
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