基于蛋白质语言模型嵌入的融合特征和手工制作的特征的蛋白质-DNA接口热点预测
Xiang Li1, Gang-Ao Wang1, Zhuoyu Wei1
1School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China.
Computational biology and chemistry
|October 22, 2023
概括
预测蛋白质-DNA结合热点残留物对于药物发现至关重要. 我们的PDH-EH模型将蛋白质语言模型嵌入与手工制作的功能相结合,优于现有的准确热点残留物识别方法.
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.
- 药物发现 药物发现
背景情况:
- 识别蛋白质-DNA接口上的热点残留物对于药物发现和疾病治疗至关重要.
- 实验方法,如氨酸扫描突变发生,对于热点残留物确定是低效和昂贵的.
- 现有的计算方法往往依赖于有限的手工制作的功能.
研究的目的:
- 开发一种高效准确的计算方法,用于预测蛋白质-DNA接口上的热点残留物.
- 为了利用蛋白质语言模型 (PLMs) 的功能提取的力量.
- 改进现有的最先进的预测模型.
主要方法:
- 提出了一种新型模型,PDH-EH,它结合了蛋白质语言模型 (PLM) 和手工制作的功能.
- 提取了1141维的特征,并利用mRMR进行最佳特征子集选择.
- 使用随机森林,支持矢量机和XGBoost算法训练和评估模型.
主要成果:
- 随机森林模型,利用最佳特征子集,在培训数据集上实现了最高的AUROC.
- 与现有最先进的模型相比,PDH-EH在一个独立的测试集上表现出更高的性能.
- 该研究证实了PLM提取的嵌入的有效性和可解释性.
结论:
- PDH-EH模型在准确有效地预测蛋白质-DNA结合热点残留物方面取得了重大进展.
- 将PLM嵌入式与传统功能的整合增强了预测能力.
- 这种方法有望加速药物发现和了解疾病机制.
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