DeepMolecules:一个用于预测酶和传送器-小分子相互作用的Web服务器
Alexander Kroll1, Yvan Rousset1, Thomas Spitzlei1
1Heinrich-Heine-University, Institute for Computer Science and Department of Biology, Universitätsstraße 1, 40225 Düsseldorf, Germany.
Nucleic acids research
|April 29, 2025
概括
DeepMolecules使用先进的深度学习模型预测蛋白质-小分子相互作用. 这个网络服务器通过识别基质和预测酶动力学来帮助药物发现和生物催化剂优化.
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:
- 了解蛋白质和小分子相互作用对于各种生物过程和药物开发至关重要.
- 精确预测酶动力学 (kcat,KM) 和基质识别对于优化生物催化剂和药物疗效至关重要.
研究的目的:
- 开发并提供可访问的Web服务器DeepMolecules,用于预测蛋白质-小分子相互作用和酶动态参数.
- 将多个最先进的预测模型集成到一个用户友好的平台中.
主要方法:
- 利用深度学习对蛋白质和小分子的数值表示.
- 采用渐变增强的决策树模型进行交互和动力预测.
- 开发了一个支持各种输入格式 (SMILES,InChI,KEGG ID) 和提交类型 (单个,批量) 的Web界面.
主要成果:
- 在酶基质识别,输送基质识别,酶周转数 (kcat) 和迈凯利斯常数 (KM) 中实现了高预测性能.
- 综合实验数据,全面了解蛋白质和小分子之间的关系.
结论:
- DeepMolecules为代谢工程,药物发现和生物催化剂研究人员提供了一个强大的,免费可访问的工具.
- 服务器有助于识别潜在的基板和量化它们的催化性能,加速研发.
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