pLM4ACE:一种基于蛋白质语言模型的预测器,用于抗高血压查
Zhenjiao Du1, Xingjian Ding2, William Hsu2
1Department of Grain Science and Industry, Kansas State University, Manhattan, KS 66506, USA.
Food chemistry
|August 21, 2023
概括
这项研究开发了一种使用进化规模建模 (ESM-2) 嵌入的蛋白质语言模型,以识别抑制血管素-I转化酶 (ACE) 的. 通过ESM-2方法显著提高了ACE抑制剂的预测准确性.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- ангиотензин-I转化酶 (ACE) 是氨酸- ангиотензин系统的一个关键调节剂.
- ACE是用于管理高血压的验证药物标.
- 开发有效的ACE抑制剂对于心血管健康至关重要.
研究的目的:
- 开发和验证一种蛋白质语言模型 (pLM) 用于选 ACE 抑制.
- 为了利用进化规模建模 (ESM-2) 嵌入来提高活性预测.
- 将ESM-2嵌入的性能与传统嵌入方法进行比较.
主要方法:
- 使用ESM-2嵌入式用于蛋白质语言模型.
- 在实验数据上训练和评估了65种不同的分类器.
- 将ESM-2与12种传统的嵌技术和5种机器学习模型进行比较.
- 使用平衡精度 (BACC),马修斯相关系数 (MCC) 和曲线下面积 (AUC) 评估模型性能.
主要成果:
- 后勤回归与ESM-2嵌入相结合,实现了最高的性能 (BACC: 0.883 ± 0.017,MCC: 0.77 ± 0.032,AUC: 0.96 ± 0.009).
- 在12种传统的嵌入方法中,ESM-2嵌入显示出更高的性能.
- 多层感知子和支向量机器模型也显示出与ESM-2嵌入的强大的兼容性.
结论:
- 嵌入ESM-2提供了一种强大的工具,用于增强预测ACE抑制活性.
- 开发的模型为发现新型ACE抑制剂提供了有希望的方法.
- 一个公开可访问的网络服务器可用于利用性能最高的模型.
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