基于分子碎片的亚结构描述器预测蛋白质二次结构
Oleg S Zakharov1, Anastasia V Rudik2, Dmitry A Filimonov2
1Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia.
International journal of molecular sciences
|December 17, 2024
概括
精确的蛋白质二次结构预测 (SSP) 使用新的以原子为中心的多层次邻近原子 (MNA) 描述器得到了改进. 这种方法提高了各种蛋白质结构的预测准确性.
科学领域:
- 分子生物学分子生物学
- 结构生物信息学 结构生物信息学
- 计算化学的计算化学
背景情况:
- 在分子生物学和结构生物信息学中,准确预测蛋白质二次结构 (SSPs) 是至关重要的.
- 现有的方法面临挑战,需要新的方法来提高准确性.
研究的目的:
- 开发一种新的SSP预测方法,使用以原子为中心的亚结构的多层次邻近原子 (MNA) 描述器.
- 为SSP预测创建和评估分类序列-结构-属性关系 (SSPR) 模型.
主要方法:
- 从蛋白质数据库 (PDB) 记录中整理了超过335,000个SSP的大数据集.
- 使用RDKit处理了蛋白质碎片,并生成了MNA描述符.
- 在MultiPASS软件中使用贝叶斯算法来构建SSPR模型.
主要成果:
- 开发的SSPR模型实现了高预测准确性,八种SSP类型的平均AUC为0.902.
- 在ASTRAL和CB513数据集的独立测试中,AUC值高达0.889%,Q8值高达74.74%.
- 开发了一个网页应用程序MNA-PSS-Pred,以便公众访问预测模型.
结论:
- 在蛋白质二次结构预测方面,MNA描述器方法提供了显著的进步.
- 开发的模型和网络应用程序为分子生物学和生物信息学研究人员提供了宝贵的工具.
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