DeepTGIN:一种使用变压器和图形异态网络进行蛋白质-联体结合亲缘关系预测的新型混合多式方式方法
Guishen Wang1,2, Hangchen Zhang1, Mengting Shao3
1College of Computer Science and Engineering, Changchun University of Technology, North Yunda Street No. 3000, Changchun, 130012, Jilin, China.
DeepTGIN是一种新的深度学习模型,通过整合序列和图形特征,准确地预测蛋白质-连接体结合亲和力. 这种混合方法通过提高对分子相互作用的理解来增强药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 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:
- 预测蛋白质 - 配体结合亲和力对于药物发现和理解分子相互作用至关重要.
- 基于序列和基于图形的模型已经显示出希望,但整合不同的数据类型仍然是一个挑战.
研究的目的:
- 介绍DeepTGIN,一种新的混合多式联络深度学习模型,用于预测蛋白质-联络体结合亲和力.
- 从蛋白质和连接体中有效地学习序列和图形特征.
主要方法:
- 开发了DeepTGIN,集成了对序列特征 (蛋白质/口袋) 的变压器编码器和对联体图特征的图形异态网络.
- 对PDBbind 2016和2013核心集的评估性能与最先进的模型相比.
主要成果:
- 在多个指标 (R,RMSE,MAE,SD,CI) 中,DeepTGIN显著优于现有模型.
- 除研究证实了连接体特征和编码器模块对模型性能的贡献.
结论:
- DeepTGIN提供了一种强大的新方法来预测蛋白质 - 连接体结合亲和力.
- 该模型能够整合多式联运特征,解决了当前方法的局限性,推动了药物发现研究.
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