结合邻近蛋白质特征以提高药物向相互作用预测:基于相似性的对齐方法的比较分析
Xiaoqing Ru1, Chao Zha1, Xin Gao1,2,3
1Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
Journal of chemical information and modeling
|July 4, 2025
概括
这项研究引入了一种新的药物向相互作用 (DTI) 预测框架,该框架使用类似蛋白邻居的特征来克服数据稀疏性并提高药物发现的准确性.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 药物向相互作用 (DTI) 的预测对于药物发现至关重要,但面临着数据稀疏性和分子相互作用复杂性的有限捕获等挑战.
- 由于这些局限性,现有的DTI预测方法往往在准确性和可解释性方面扎.
研究的目的:
- 开发一种新的DTI预测框架,以提高准确性和可解释性.
- 通过结合高度相似的蛋白质邻居的特征来解决数据稀疏性.
主要方法:
- 从药物标结合亲缘关系数据中提取化学和物理化学特征.
- 通过对齐算法 (BLAST,MUSCLE,MAFFT,Clustal Omega,Foldseek) 来识别的非常相似的邻近蛋白质的综合相互作用特征.
主要成果:
- 对戴维斯和KIBA数据集的实验显示,通过结合邻近特征,预测准确度得到了大幅改善.
- 排名最高的邻居提供了最显著的性能增长,验证了基于相似性的功能增强.
- 对比表明了邻居选择中的对齐方法的稳定性,案例研究证实了生物相关性.
结论:
- 拟议的框架为DTI预测中的数据稀疏性提供了一个新的解决方案.
- 它显著提高了预测性能,并提高了模型的可解释性.
- 这项工作为精确的DTI预测提供了基础,并推进了计算药物发现方法.
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