基于图形神经网络和SMILES编码的Cas蛋白识别的新策略
Gaoxiang Chen1, Liya Hou2, Zhanwei Li2
1Zhejiang Laboratory, Research Center for Life Sciences Computing, Hangzhou, 311100, China. hncschengaoxiang@126.com.
Scientific reports
|April 30, 2025
概括
研究人员开发了一种新的计算方法来识别Cas1蛋白质,这对于CRISPR基因编辑至关重要. 这种方法使用机器学习和SMILES编码来准确识别蛋白质,并且在更广泛的蛋白质研究中具有潜在的应用.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- CRISPR-Cas系统为 prokaryotes 提供了适应性免疫力,也是一个强大的基因组编辑工具.
- 不同的Cas蛋白对于CRISPR-Cas系统功能和基因组编辑至关重要.
- 准确识别特定的Cas蛋白,如Cas1,对于理解和应用CRISPR技术至关重要.
研究的目的:
- 开发用于挖掘和识别Cas1蛋白质的新策略.
- 为 Cas1 蛋白质识别创建一个高精度的计算模型.
- 为了证明蛋白质大语言模型和SMILES编码在这个过程中的实用性.
主要方法:
- 在一个蛋白质大型语言模型中分析了14种Cas蛋白类型的嵌入空间.
- 将蛋白质数据转换为简化分子输入线输入系统 (SMILES) 格式,用于图形构造.
- 使用两个定向消息传递神经网络 (DMPNN) 模型设计和训练一个整体模型.
主要成果:
- 整体DMPNN模型在训练和新数据集上实现了Cas1蛋白的高精度识别.
- 与CRISPRCasFinder等现有工具相比,开发的方法显示出更高的有效性.
- 该模型成功地在Ensemble数据库中识别了潜在的Cas1蛋白,证明了它的稳定性.
结论:
- 开发的整体模型为Cas1蛋白识别提供了强大的和实用的方法.
- 使用的策略,包括SMILES编码,可以扩展到研究其他Cas蛋白.
- 这项研究促进了蛋白质研究和生物宏分子研究的计算应用.
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