pLMMoRF:一个通过采用蛋白质语言模型准确预测膜相互作用分子识别特征的Web服务器
Máté Csepi1, Blanka Berta1, Sushmita Basu2
1Department of Biophysics and Radiation Biology, Semmelweis University, Budapest H-1094, Hungary.
Journal of molecular biology
|May 29, 2025
概括
我们开发了pLMMoRF,它是膜分子识别特征 (MemMoRFs) 的快速和准确预测器,这些特征是与脂质相互作用的内在无序区域. 该工具通过分析人类蛋白质组,有助于理解膜蛋白的功能.
科学领域:
- 生物化学和分子生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质-脂质相互作用对于细胞功能至关重要.
- 内在无序区域 (IDR) 经常调解这些相互作用,有时在结合时获得结构.
- 经过实验识别的与脂质相互作用的IDR被称为膜分子识别特征 (MemMoRF).
研究的目的:
- 开发和验证一个准确的,快速的,基于序列的MemMoRF预测器,命名为pLMMoRF.
- 支持识别MemMoRF,减少对耗时的实验方法的依赖.
- 分析人类蛋白质组的MemMoRF预测,以更好地了解膜合蛋白.
主要方法:
- 收集并策划了一组实验性注释的MemMoRF数据集.
- 利用蛋白质语言模型 (pLMs) 和深层卷积神经网络进行预测.
- 应用特征选择以优化对紧神经网络的pLM输出,选择基于Ankh的模型.
- 对低相似性数据集的现有预测因素进行pLMMoRF评估.
主要成果:
- pLMMoRF表现出比目前最先进的预测器CoMemMoRFPred.com更高的准确性.
- 该预测器由于其紧的网络大小和高效的处理,具有相对较小的计算足迹.
- 为整个人类蛋白质组生成了MemMoRF预测,并公开提供.
结论:
- pLMMoRF是一个准确而高效的工具,用于从蛋白质序列中预测MemMoRF.
- 选择关键的嵌入功能对于提高预测性能和降低计算成本至关重要.
- 公开可用的预测可以更深入地了解膜相关蛋白质的作用.
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