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HOTGpred:通过使用基于综合预训练蛋白语言模型的特征和多阶段特征选择方法,提高人类O-链接的三氨酸糖化预测

Nhat Truong Pham¹, Ying Zhang², Rajan Rakkiyappan³

¹Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.

Computers in biology and medicine

|July 19, 2024

概括

预测O链接的三氨酸糖化位点 (OTGs) 对于理解蛋白质功能至关重要. 新的生物信息学工具HOTGpred使用高级功能和机器学习准确识别OTG,其性能优于现有方法.

科学领域:

生物化学和分子生物学
生物信息学和计算生物学

背景情况:

与O相关的糖化是人类蛋白质中关键的翻译后修饰 (PTM),调节细胞通路.
鉴定O-链接的氨酸糖化位点 (OTGs) 是具有挑战性的,因为缺乏特定的序列特征和不稳定的结构.
目前用于OTG预测的生物信息工具在特征选择和有效性方面存在局限性.

研究的目的:

开发一个准确和有效的计算工具,用于预测人类O-链接的三氨酸糖化位点 (OTGs).
通过使用先进的特征工程和选择策略来解决当前OTG预测方法的局限性.

主要方法:

采用了多阶段的特征选择过程,从基于蛋白语言模型 (PLM) 的嵌入和常规描述器评估了25个特征集.
集成了高性能嵌入式,并采用了顺序前置搜索来确定最佳功能集.
开发了一个基于极端梯度增强 (XGBT) 的模型,命名为HOTGpred,结合了最佳功能.

主要成果:

在培训数据集中,HOTGpred的准确度达到92.03%,在平衡的独立数据集中达到88.25%.
在平衡和不平衡的独立数据集上,HOTGpred模型显著超过了当前最先进的方法.
沙普利添加式扩张 (SHAP) 和消去分析确定了对HOTGpred表现有贡献的关键特征.

结论:

HOTGpred显示了对人类O-链接的三氨酸糖化位的卓越预测能力.
开发的Web服务器为研究糖化酶的糖生物学家提供了宝贵的资源.
该研究强调了高级特征选择在提高PTM预测准确性方面的重要性.

关键词:

生物信息学是一种生物信息学.极端的梯度增强了极端的梯度.与O相关的三氨酸糖化.后翻译修改后的修改.预训练的蛋白质语言基于模型的特征.两个步骤的功能选择选择.

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