AVPpred-BWR:通过生物词汇表示来预测抗病毒
Zhuoyu Wei1, Yongqi Shen1, Xiang Tang1
1School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui 230036, China.
Bioinformatics (Oxford, England)
|March 28, 2025
概括
我们开发了AVPpred-BWR,这是一种用于预测抗病毒 (AVP) 的新型计算模型. 这种方法通过使用从二级结构模式获得的生物词来表示序列来提高预测准确性.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 抗病毒 (AVP) 显示出作为治疗剂的前景.
- 目前用于识别AVP的方法要么慢,资源密集 (湿实验),要么缺乏足够的准确性 (计算方法).
研究的目的:
- 开发一个更准确的计算模型来预测抗病毒.
- 引入一种基于其二次结构的序列表示的新方法.
主要方法:
- 这项研究提出了AVPpred-BWR,一种利用生物词汇表示的模型.
- 序列被分解成1mer (循环) 和4mer (α-螺旋) 的生物词.
- Word2Vec模型代表了连续的信息,CNN-Transformer框架从这些嵌入中提取了多尺度的特征.
主要成果:
- 与现有方法相比,AVPpred-BWR在预测准确度方面取得了显著的改进.
- 该模型在独立测试组中与UniDL4BioPep相比,实现了4.6%的AUROC改进和11.0%的MCC改进.
- 这项工作开创了基于二次结构规律性的蛋白质初级结构的单词细分.
结论:
- AVPpred-BWR提供了一种更准确,更有效的方法来识别抗病毒.
- 新的生物词汇表示和CNN-Transformer框架在类药物发现中推进了计算方法.
- 该模型的性能突显了整合二次结构信息以改进序列分析的潜力.
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