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AMPCliff:对抗微生物中的活性悬崖的定量定义和基准测试

Kewei Li¹, Yuqian Wu², Yinheng Li³

¹College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China.

Journal of advanced research

|May 3, 2025

概括

抗微生物 (AMP) 中的活性悬崖 (AC) 是常见的,有一个新的框架,AMPCliff,定义和对其预测进行基准测试. 虽然模型显示出希望,但整合动态信息是提高AMP活动悬崖预测准确度的关键.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
生物信息学是一种生物信息学.

背景情况:

活性悬崖 (AC) 代表了类似分子之间生化活性的显著差异,影响了药物开发.
虽然小分子中的AC已得到充分研究,但使用正规氨基酸的抗菌 (AMP) 中的AC仍未得到充分研究.

研究的目的:

介绍AMPCliff,这是对抗微生物 (AMPs) 中活性悬崖的定量定义和基准测试框架.
评估各种机器学习和深度学习模型在预测AMP活动悬崖方面的性能.

主要方法:

从GRAMPA数据库中建立了对抗黄金葡萄球菌的配对AMP的基准数据集.
评估了九种机器学习,四种深度学习,四种掩面语言和四种生成语言模型用于AMP AC预测.
使用最小抑制度 (MIC) 和 0.9.9 的正常化BLOSUM62相似度值定义了AC.

主要成果:

在AMP中确定的活动悬崖的显著流行.
预先训练的蛋白质语言模型ESM2在评估模型中表现优越.
在ESM2实现了0.4669的斯皮尔曼相关系数,用于预测-log (MIC) 值,这表明有改进的余地.

结论:

目前的深度学习模型在准确捕获AMP属性方面存在局限性.
整合原子级动态信息对于提高AMP活动和行动机制的预测至关重要.

关键词:

在 AMPC 里面,有很大的风险.活动悬崖 (AC)抗微生物 (AMP) 是一种抗微生物.一个基准的基准.最低抑制度 (MIC) 是指最小的抑制度.

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