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保险丝作为一个:用于AMP一般识别的跨模式框架

Wentao Zhang¹, Yanchao Xu¹, Aowen Wang¹

¹College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China.

Briefings in bioinformatics

|October 2, 2023

概括

一个新的框架SenseXAMP通过结合语义嵌入和蛋白质描述器来改进抗菌 (AMP) 识别. 这种方法通过更好地分析复杂的序列来增强抗生素的发现.

科学领域:

生物化学和生物信息学
药物发现和开发药物发现和开发
计算生物学计算生物学

背景情况:

抗微生物 (AMP) 显示出作为新型抗生素的潜力,因为它们具有广泛的疗效.
鉴定有效的AMP是具有挑战性的,因为复杂的结构和不同的序列.
现有的方法与大量潜在的AMP候选人作斗争.

研究的目的:

开发一个先进的计算框架,SenseXAMP,用于更好地识别抗菌 (AMP).
通过整合语义嵌入和蛋白质描述器 (PDs) 来增强AMP识别.
建立新的基准数据集并扩展AMP识别任务,包括活动预测回归.

主要方法:

开发了SenseXAMP,这是一个利用语义嵌入和蛋白质描述器 (PD) 的交叉模式框架.
包含多输入对齐模块和交叉表示融合模块,用于功能集成.
精选更新注释的AMP数据以创建全面的基准数据集.
引入了一项AMP回归任务,用于预测抗微生物活性.

主要成果:

在各种AMP数据集上,包括新的基准,SenseXAMP在各种AMP数据集上表现出优于最先进的模型的性能.
实验证实了传统PD和蛋白质预训练模型的互补益处.
SenseXAMP有效地结合了PD的优势,以提高蛋白质预训练模型在AMP任务中的性能.

结论:

SenseXAMP在抗微生物的计算识别方面取得了重大进展.
该框架能够融合多种特征类型,从而提高预测准确度.
这项工作为加速发现新抗生素剂提供了有价值的工具和数据集.

关键词:

抗微生物的识别和识别.跨模式的框架.深度学习是一种深度学习.融合模型融合模型融合模型

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