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相互关联的蛋白质组序列特征增强预测建模:应用于COVID-19严重程度

Radwa El-Awadi¹, Oscar D Gomez¹, Daniel Castillo-Secilla²

¹Department of Computer Engineering, Automation and Robotics, University of Granada, 18071 Granada, Spain.

|February 27, 2026

概括

INPROF是一个新的Web服务器,它使用多重序列对齐 (MSA) 分析蛋白质序列,以提取46个特征. 这种工具准确地预测疾病的严重程度,优于使用表达数据的传统方法.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

蛋白质序列比较对于生物医学,植物遗传和进化至关重要.
开放式数据库扩大了生物数据的可用性.
多重序列对齐 (MSA) 仍然是分析保存的蛋白质区域的关键方法.

研究的目的:

介绍INPROF,一个新的网络服务器,用于集中和自动化计算蛋白质特征.
通过MSA实现蛋白质关系的深入分析和保存信息.
为人工智能驱动的蛋白质建模提供全面的功能目录.

主要方法:

开发了INPROF,一个从蛋白质序列计算多达46个不同的指标的Web服务器.
使用MSA来比较蛋白质序列并识别保存的元素.
跨多个蛋白质层面的综合特征:序列,结构,域和本体学术语.

主要成果:

通过预测COVID-19患者RNA-Seq数据集中的疾病严重程度来验证INPROF.
从与差异表达基因相关的正规蛋白质中提取INPROF特征.
与传统表达式数据方法相比,INPROF的优越分类性能得到了证明.

结论:

INPROF是一个多功能平台,用于计算蛋白质相互作用的46个定量指标.
这些指标为下游预测建模提供了具有生物学意义的特征.
在INPROF网络服务器促进先进的蛋白质分析和AI应用程序.

关键词:

在 COVID-19 疫情中,这就是SARS-CoV-2病毒.这是分类分类的分类.功能提取特性提取多重序列对齐 (MSA) 是一种方法.蛋白质相互关系的相互关系蛋白质是一种蛋白质.网络服务器是Web服务器.

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