预测未经研究的病毒的蛋白质-蛋白质相互作用的挑战:竞技病毒-人类相互作用
Harshita Sahni1,2, Sarah Michelle Crotzer1,3, Juston Moore4
1Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, USA.
Computational and structural biotechnology journal
|December 17, 2025
概括
预测病毒-宿主蛋白-蛋白相互作用 (PPI) 对了解感染至关重要. 这项研究揭示了标准评估方法对研究不足的病毒具有误导性,并提出了准确PPI预测模型评估的新框架.
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:
- 了解病毒-宿主蛋白-蛋白相互作用 (PPI) 对于破译感染机制和确定治疗点至关重要.
- 由于数据的局限性,将PPI的预测模型推广到多种多样和未经研究的病毒中仍然是一个重大挑战.
研究的目的:
- 为了调查在概括蛋白质-蛋白质相互作用 (PPI) 预测模型的挑战,用于arenavirus-人类相互作用.
- 评估负采样策略的影响,并识别标准模型评估技术中的偏差.
- 提出一个强大的评估框架来评估PPI模型在研究不足的病毒系统中的概括性.
主要方法:
- 雇员转移学习使用预先训练的人类-人体和经过充分研究的病毒-人体PPI的模型.
- 策划和评估了四种类型的负采样数据集,用于模型培训和评估.
- 在平衡的数据集上使用k-fold交叉验证和独立盲测试来揭示性能限制.
- 开发了一个病毒蛋白特异性评估框架,将蛋白质分为多数类和少数类.
主要成果:
- 标准评估指标 (准确性,AUPRC) 可能具有误导性,由于数据泄露,偏差和过度匹配,显示高性能 (93-99%),特别是代表性不足的病毒蛋白.
- 在平衡数据集上进行的独立盲测试显示,准确度下降到50%以下,凸显了标准方法的不足.
- 使用均衡准确度的拟议病毒蛋白特异性框架表明,对模型通用性的评估更为现实.
结论:
- 标准评估中的高性能指标不能保证对研究不足的病毒的可靠PPI预测模型.
- 为了克服数据不平衡和偏差问题,需要一个新的,蛋白质特定的评估框架.
- 这一框架提高了PPI预测模型的可靠性,这对于推进研究研究不足的病毒病原体至关重要.
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