预DBP-PLMs:基于预训练的蛋白语言模型和卷积神经网络的DNA结合蛋白的预测
Dawei Qi1, Chen Song1, Taigang Liu1
1College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China.
Analytical biochemistry
|July 10, 2024
概括
我们开发了PreDBP-PLMs,这是一个计算工具,可以准确预测DNA结合蛋白 (DBPs). 这种方法将蛋白质语言模型嵌入与进化特征融合在一起,以改善DBP识别,帮助理解生物过程.
科学领域:
- 计算生物学是一种计算生物学.
- 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:
- 鉴定DNA结合蛋白 (DBPs) 对于理解基因调节,DNA修复和复制至关重要.
- 传统的用于DBP识别的实验方法昂贵且耗时.
- 开发用于DBP预测的快速计算工具至关重要.
研究的目的:
- 开发一种新的计算预测器,PreDBP-PLMs,用于增强DNA结合蛋白质的识别.
- 通过集成先进的计算技术来提高DBP预测的准确性.
- 为实验性DBP识别方法提供更快,更有效的替代方案.
主要方法:
- 融合预训练的蛋白质语言模型 (PLM) ProtT5嵌入物与来自位置特定评分矩阵 (PSSM) 的进化特征.
- 使用卷积神经网络 (CNN) 分类器用于DBP预测.
- 采用五重交叉验证 (CV),一次性离开CV (LOOCV) 和独立的测试组进行绩效评估.
主要成果:
- 与现有方法相比,PreDBP-PLMs在PDB186数据集上实现了0.5%的精度改进,在PDB2272数据集上达到5.2%.
- ProtT5嵌入式和PSSM衍生的进化特征的最佳组合显著提高了预测准确性.
- 该预测器在多个验证策略中表现出强的性能.
结论:
- 预DBP-PLMs为DNA结合蛋白识别提供了一个高度准确和高效的计算方法.
- 该方法有效地整合了蛋白质语言模型和进化信息,以获得更好的生物学见解.
- 这种工具可以显著帮助研究人员研究DBP在各种细胞过程中的作用.
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