机器和深度学习可以预测病毒融合
A M Sequeira1, M Rocha1, Diana Lousa2
1Department of Informatics, School of Engineering, University of Minho, Braga, Portugal.
Computational and structural biotechnology journal
|March 14, 2025
概括
机器学习模型现在可以预测病毒融合,对于病毒进入和潜在的治疗来说至关重要. 这种生物信息学方法比实验方法更有效地识别这些细分,有助于发现新的抗病毒策略.
科学领域:
- 病毒学 病毒学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 像SARS-CoV-2这样的包裹病毒利用表面融合蛋白进入宿主细胞.
- 这些蛋白质中的融合 (FPs) 对于病毒融合至关重要,并代表治疗点.
- 实验性FP识别是艰苦而昂贵的,需要计算预测工具.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测病毒融合蛋白中的融合位.
- 探索各种ML方法,序列表示和特征组合,以准确识别FP.
- 识别新型假定融合,特别是对于具有有限实验数据的病毒.
主要方法:
- 使用机器和深度学习模型的代币分类和滑动窗技术.
- 评估了各种蛋白质序列表示:一热编码,物理化学特征,NLP嵌入和变压器.
- 测试了50多种ML模型和序列特征的组合.
主要成果:
- 使用ML取得了有希望的结果,特别是用于氨基酸令牌分类的基于变压器的模型.
- 变压器模型在预测聚变位置方面表现出高效.
- 成功预测了SARS-CoV-2的假设融合,并分析了现有的注释.
结论:
- 开发了有效的ML模型,用于预测病毒融合蛋白中的融合位.
- 基于变压器的方法显示出识别FP的巨大潜力,即使实验数据有限.
- 这种计算策略可以加速用于治疗开发的新聚变的发现.
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