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使用基于深度学习的序列分析,对抗微生物中的血液溶解活性进行了增强的预测

Ibrahim Abdelbaky¹, Mohamed Elhakeem², Hilal Tayara³

¹Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt. ibrahim.abdelbaky@fci.bu.edu.eg.

BMC bioinformatics

|November 28, 2024

概括

一个新的深度学习模型预测了抗微生物 (AMP) 溶血,减少红细胞损伤. 这促进了对细菌感染的更安全的AMP药物开发.

科学领域:

生物化学生物化学
计算生物学计算生物学
药理学药理学是指药理学的学科.

背景情况:

抗微生物 (AMP) 显示出广泛的抗微生物活性,为新疗法提供了潜力.
AMP的临床使用受到其溶血活性的阻碍,导致红细胞的破坏.
开发具有降低血液溶解的AMP对于安全有效的治疗应用至关重要.

研究的目的:

开发一种深度学习模型,用于预测抗微生物的溶血活性.
利用卷积神经网络 (CNN) 在AMP中准确预测血解.
促进设计更安全的AMP用于临床使用.

主要方法:

序列使用一次热编码进行编码.
采用了一个卷积神经网络 (CNN) 架构,具有卷积和完全连接的层.
该模型在六个不同的数据集 (HemoPI-1,HemoPI-2,HemoPI-3,RNN-Hem,Hlppredfuse,AMP-Combined) 上进行了训练和验证.

主要成果:

在多个数据集中,CNN模型实现了高预测性能.
马修的相关系数从0.5614到0.9274不等,显示出强大的准确性.
开发的模型超过了预测AMP溶血活性的现有方法.

结论:

深度学习模型有效地预测AMP溶血活性.
这种预测能力有助于设计具有最小毒性的AMP.
这项研究支持AMP作为更安全的治疗药物对抗细菌感染的发展.

关键词:

抗微生物是一种抗微生物.深度学习是一种深度学习.血液溶解活性血液溶解活性治疗性类的治疗性类.

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