软件导刊2024，Vol.23Issue(1)：69-74,6.DOI:10.11907/rjdk.231042
基于动态图卷积与迁移学习的蛋白质质量评估
Protein Model Quality Assessment Based on Dynamic Graph Convolution and Transfer Learning
冯子健 1黄伟鸿 1姜博文1
作者信息
- 1. 浙江理工大学 信息科学与工程学院,浙江 杭州 310018
- 折叠
摘要
Abstract
Protein model quality assessment refers to the scoring of protein models predicted by computational methods,so as to select an ex-cellent model that is closer to the native structure.Graph structures can intuitively represent protein models,so graph convolutional neural net-works(GCNs)have been widely used in quality assessment in recent years.However,the fixed adjacency relationship of graph nodes limits the ability of GCN to mine node features deeply.Based on this,a dynamic graph convolution quality assessment method DGCQA is proposed to predict the global quality score of the protein model.This method dynamically obtains the neighborhood according to the feature distance of the node,and combines the multi-scale convolution module to extract the residue pair features to enhance the expressive ability of the network.In addition,based on the idea of transfer learning,the protein pre-training model ESM-1b encoding feature is introduced,which improves the performance of DGCQA on multiple indicators.The final experiments show that DGCQA is highly competitive in comparison with 12 quality as-sessment methods based on the CASP13 dataset.关键词
蛋白质模型质量评估/动态图卷积/迁移学习/ESM-1bKey words
protein model quality assessment/dynamic graph convolution/transfer learning/ESM-1b分类
计算机与自动化引用本文复制引用
冯子健,黄伟鸿,姜博文..基于动态图卷积与迁移学习的蛋白质质量评估[J].软件导刊,2024,23(1):69-74,6.
