EquiPNAS:使用蛋白语言模型信息等同变量深图神经网络改进了蛋白质核酸结合部位的预测
Rahmatullah Roche1, Bernard Moussad1, Md Hossain Shuvo1
1Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America.
bioRxiv : the preprint server for biology
|September 25, 2023
概括
EquiPNAS是一个新的框架,通过将蛋白质语言模型 (pLMs) 与E(3) 等价深图神经网络集成,提高了蛋白质-核酸结合部位的预测. 这种方法显著提高了准确性,并减少了对进化数据的依赖.
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.
- 结构生物学是结构生物学.
背景情况:
- 预测蛋白质与核酸结合点对于理解分子相互作用至关重要.
- 现有的方法经常在可扩展性和通用性方面扎.
- 蛋白质语言模型 (pLMs) 是有前途的,但尚未完全用于此任务.
结论:
- EquiPNAS在预测蛋白质与核酸结合部位方面取得了重大进展.
- 该框架展示了将plm与高级图形神经网络相结合的力量.
- EquiPNAS是开源的,这有助于进一步的研究和应用.
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