使用变压器衍生的蛋白质嵌入物和离散的共弦转换增强的进化特征与生成对抗性囊双向时卷积神经网络的防御素的识别
Farman Ali1, Tamim Alkhalifah2, Raed Alsini3
1Department of Computer Science, Bahria University Islamabad Campus, Islamabad, Pakistan.
International journal of biological macromolecules
|October 23, 2025
概括
防御素是一种抗微生物 (AMP),对抗药物耐药细菌具有有前途的作用. 一个新的预测器,GAC-BTCNN-Pred,通过使用高级功能和混合深度学习模型来改进防御蛋白识别.
科学领域:
- 生物化学和分子生物学
- 计算生物学和生物信息学
- 药物发现和开发 药物发现和开发
背景情况:
- 德芬辛是具有广泛活性和低耐药性潜力的阴离子抗菌 (AMP),对抗多药耐药细菌非常有价值.
- 目前用于 defensin 预测的计算模型缺乏准确性,原因是特征表示不足和模型设计不足.
- 需要改进的计算工具来准确识别治疗应用中的防御蛋白.
研究的目的:
- 开发一种新的,准确的防御蛋白识别预测器,解决现有计算模型的局限性.
- 整合先进的特征提取技术和混合深度学习架构,以提高预测性能.
- 建立一个可扩展的工具,通过精确的防御蛋白识别来发现新型抗菌剂.
主要方法:
- 使用分段位置特定得分矩阵 (seg-PSSM) 增强的离散等边变换 (DCT) 来创建Seg-PSSM-DCT描述符的特征提取.
- 整合了基于变压器的蛋白质语言模型ProtGen-LLM,以编码上下文,物理化学和长距离蛋白质序列依赖性.
- 开发一种混合生成对抗囊双向时间卷积神经网络 (GAC-BTCNN) 模型,集成Seg-PSSM-DCT和ProtGen-LLM特征 (PGL-PD).
主要成果:
- 拟议的GAC-BTCNN-Pred预测器与传统的机器学习和现有的深度学习方法相比,表现优越.
- 综合特征集 (PGL-PD) 有效地捕获了来自蛋白质序列的多方面的信息.
- GAC-BTCNN模型在防御蛋白识别中的各种评估指标中实现了更高的准确性.
结论:
- GAC-BTCNN-Pred代表了计算防御蛋白识别的重大进步,提供了更好的准确性和可扩展性.
- 新型特征表示和混合深度学习架构对于预测抗微生物是有效的.
- 这种工具有可能加速新型抗菌剂的发现,特别是针对多抗药性病原体.
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