一个新的可解释性框架,用于预测酶循环数量,并通过预先训练的酶嵌入和自适应门网络来增强
Bing-Xue Du1, Haoyang Yu2, Bei Zhu2
1School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China; Institute for Infocomm Research (I(2)R), Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore.
Methods (San Diego, Calif.)
|February 28, 2025
概括
这项研究介绍了GELKcat,这是一个新的深度学习框架,用于预测酶周转数 (kcat). 通过有效地整合酶和基质特征并识别关键分子驱动因素,GELKcat提高了预测准确性.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 酶周转数 (kcat) 对合成生物学和药物发现至关重要.
- 深度学习模型显示了使用酶基质数据进行kcat预测的前景.
- 目前的方法在有效的特征提取,融合和可解释性方面扎.
研究的目的:
- 开发一个可解释的深度学习框架 (GELKcat),用于准确的kcat预测.
- 改进酶和基质数据的特征提取和融合策略.
- 确定影响kcat.cat.的关键分子亚结构.
主要方法:
- 使用图形变压器进行基质分子编码.
- 用于酶word2vec嵌入的卷积神经网络 (CNN).
- 集成功能使用自适应门网络,以实现最佳权重.
主要成果:
- 与最先进的方法相比,GELKcat表现出卓越的性能.
- 废弃性研究证实了框架核心组件的重要性.
- 案例研究成功地确定了显著影响kcat.cat的基质功能组.
结论:
- GELKcat提供了一种有效和可解释的方法来进行kcat预测.
- 该框架推进了用于药物发现和合成生物学的酶基质表示.
- 确定关键的分子决定因素可以指导酶工程和药物设计.
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