预测四个小分子连接体的结合残留物,通过使用附加相关性特征的集成算法来预测
Sujuan Gao1, Huimin Hu1, Xiuzhen Hu2
1College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China.
Scientific reports
|November 10, 2025
概括
预测蛋白质-小分子结合残留物对于药物设计至关重要. 这项研究引入了新的基于序列的相关性特征,显著提高了对ATP,ADP,GDP和NAD等联体的预测准确度.
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.
- 药物发现 药物发现 药物发现
背景情况:
- 精确预测蛋白质-小分子联体结合残留物对于蛋白质功能注释和分子药物设计至关重要.
- 现有的基于序列的预测方法在提高预测准确性方面存在局限性.
- 准确地识别ATP,ADP,GDP和NAD等联体的结合残留物仍然是一个挑战.
研究的目的:
- 开发一种新的计算方法,精确识别蛋白质小分子结合残留物.
- 根据序列信息引入和评估四个新的相关性特征.
- 为了提高ATP,ADP,GDP和NAD配体的结合残留的预测准确度.
主要方法:
- 开发了一种结合四个相关性特征的预测方法:邻近相关性,残留对,中心动机和PSSM相关性.
- 为了验证,使用了自己构建的数据集和以前研究的数据集.
- 进行了废弃性实验,以评估每个相关性特征的贡献.
主要成果:
- 提出的方法在独立测试数据集上取得了有利的预测结果.
- 对特定的配体观察到的最高性能指标是:对GDP的敏感性 (54.93%),ATP的特异性 (98.68%),NAD的精度 (53.41%) 和NAD的MCC (0.5341).
- 废弃性研究证实,引入的相关性特征显著提高了预测性能.
结论:
- 新的相关性特征显著改善了对蛋白质和小分子结合残留物的预测.
- 开发的方法提供了一个强大的方法来识别结合部位,帮助功能注释和药物设计.
- 选择的特征参数和算法对于构建有效的预测模型至关重要.
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