GraphGPSM:使用图形神经网络的蛋白质结构的全球评分模型
Guangxing He1, Jun Liu1, Dong Liu1
1College of Information Engineering, Zhejiang University of Technology.
Briefings in bioinformatics
|June 15, 2023
概括
我们开发了GraphGPSM,一个等价图神经网络模型,以提高蛋白质结构预测的准确性. 图形GPSM显著优于现有的方法,特别是在挑战多域和孤儿蛋白质方面,推进了蛋白质建模领域.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 结构生物学中的机器学习
背景情况:
- 蛋白质结构预测的准确性仍然是一个挑战,特别是对于多域和孤儿蛋白质.
- 现有的评分模型,包括统一字段和蛋白质特异性功能,在准确指导蛋白质结构建模和排名方面存在局限性.
- 迫切需要深度学习方法来开发更准确,更有效的蛋白质评分模型.
研究的目的:
- 提出和评估GraphGPSM,一种基于等价图神经网络 (EGNN) 的新型全球蛋白质结构评分模型.
- 使用深度学习指导蛋白质结构折叠和排序,以提高准确性.
- 解决当前评分模型在预测复杂蛋白质结构方面的局限性.
主要方法:
- 开发了一种EGNN架构 (GraphGPSM),其中包含一个信息传递机制,用于在图节点和边缘之间交换信息.
- 集成的残留级超快形状识别,高斯辐射基函数用于拓编码,罗塞塔能量术语,二面角和间残留几何.
- 使用多层感知子来输出蛋白质模型的全球得分.
主要成果:
- 图GPSM得分与CASP13,CASP14和CAMEO数据集上的TM得分有很强的相关性.
- 它的表现优于已有的评分模型,如REF2015,ModFOLD8,ProQ3D和DeepAcc.Net等.
- 在484个测试蛋白质的建模准确度中显著改善.
- 与AlphaFold2.2.相比,孤儿和多域蛋白质的TM平均得分更高.
- 在CASP15.15的全球准确性估计中展示了竞争性表现.
结论:
- GraphGPSM提供了一种高度准确和高效的蛋白质结构全球评分方法.
- 该模型显著提高了蛋白质结构建模的准确性,特别是对于具有挑战性的蛋白质类型.
- GraphGPSM代表了基于深度学习的蛋白质结构预测和评分的实质性进展.
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