使用蛋白质语言模型推进病毒性因子预测
Yitong Liu1, Xin Cao1, Jiani Li1
1School of Life and Health Sciences, Hainan Province Key Laboratory of One Health, Collaborative Innovation Center of Life and Health, Hainan University, Haikou, 570228, Hainan, China.
BMC biology
|October 15, 2025
概括
这项研究引入了pLM4VF,这是一个用于预测细菌毒性因子 (VFs) 的新框架,无论是阳性细菌还是阴性细菌. pLM4VF显著提高了预测准确度,为了解细菌疾病和开发新治疗方法提供了宝贵的工具.
科学领域:
- 细菌病原和病毒性因素预测
- 计算生物学和生物信息学
- 微生物学中的机器学习
背景情况:
- 细菌感染是一个主要的全球健康威胁,病毒性因素 (VFs) 驱动病原性.
- 准确的VF预测对于了解疾病机制和开发新疗法至关重要.
- 现有的机器学习 (ML) 方法与过时的特征扎,并且缺乏细菌类型 (格拉姆阳性/阴性) 的区分.
研究的目的:
- 为细菌毒性因子开发一个先进的预测框架,pLM4VF.
- 提高VF预测的准确性和可靠性,特别是对格拉姆阳性和格拉姆阴性细菌.
- 为研究人员提供可访问的工具,以在全基因组规模上预测VF.
主要方法:
- 利用ESM蛋白质语言模型来提取特定于格兰阳性和格兰阴性细菌的特征.
- 采用堆叠策略来整合单独的模型以提高预测性能.
- 对独立测试数据集进行了广泛的基准测试,与最先进的方法进行比较.
主要成果:
- 与现有方法相比,pLM4VF表现出优异的性能,精度提高了0.088-0.320的格兰阳性细菌和0.063-0.307的格兰阴性细菌.
- 通过细胞毒性和急性毒性试验的生物验证证实了该框架的可靠性.
- 开发了一个在线工具,为具有有限ML专业知识的研究人员提供全基因组VF预测.
结论:
- pLM4VF为阐明致病机制提供了重要的支持.
- 该框架有助于开发新的抗菌疗法和疫苗.
- pLM4VF有助于预防和管理细菌性疾病.
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