使用ALLSites准确识别所有药物模式的蛋白质结合部位.
Minjie Mou1,2, Mingkun Lu2, Zhimeng Zhou2
1Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|December 27, 2025
概括
ALLSites只使用序列数据准确地预测所有药物类型的蛋白质结合部位. 这一突破扩大了药物发现潜力,通过在蛋白质组中识别更多可用药的点.
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:
- 识别蛋白质结合部位对于药物发现至关重要,但目前的方法有限.
- 现有的计算工具往往缺乏特异性,需要结构数据,或不够准确.
研究的目的:
- 开发一个统一的,基于序列的框架,用于在所有药物模式中预测全蛋白质组的结合位点.
- 克服现有的绑定站点预测方法的局限性.
主要方法:
- 开发了ALLSites,这是一个基于序列的框架,利用ESM-2嵌入式.
- 集成了一个封闭的卷积网络与变压器架构,以捕获全球和本地序列特征.
- 从蛋白质序列数据直接建模的残留物相互作用.
主要成果:
- 在各种药物模式 (蛋白质,,小分子,碳水化合物,DNA,RNA) 中,ALLSites实现了卓越的预测性能.
- 在基于序列的方法中展示了最先进的性能.
- 与最好的基于结构的预测工具的准确度相匹配.
结论:
- ALLSites可用于所有药物模式的准确,无结构的结合部位预测.
- 预计将显著扩大可药物化蛋白质组.
- 为加速药物发现工作提供了一个强大的新资源.
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