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Briefings in Bioinformatics 论文解析
现状问题:These methods are mostly based on neighbored features in sequence, and thus limitd to capture spatial information
模型:GraphPPIS
PPi位点预测转化为一个图节点分类任务(graph node classification task)
关键技术:初始残差(initial residual)和身份映射技术(identity mapping techniques)
想法:把蛋白质视为无向图,将蛋白质位点预测视为节点分类问题,整合evolutionary and structural information to construct node features and calculated pairwise amino acid distances to construct the adjacency matrix. 然后,利用初始残差和身份映射实现一个深度进化框架,从高阶氨基酸领域捕获信息。
三个数据集:
Dset_186,Dset_72,Dset_164
To ensure that the training and test set obey similar distributions in terms of interacting percentages(确保里面的PPI占比相同?), 将他们融合
最终:335 protein chains(Train_335), 60 chain(Test_60)
Test_315 验证模型泛化性
UBtest_31 To evaluate the robustness of GraphPPIS and the impact of conformational changes on method performance. corresponding unbound structures
问题1:怎么计算pairwise amino acid distance
Node features:
two groups of amino acid features: evolutionary information(PSSM and HMM), and structural properties(DSSP)
Evolutionary information:
PSSM:position-specific scoring matrix
HMM:hidden Markov models
Structural properties:
DSSP 包括了三个结构信息
Adjacency matrix 获取方式
第一步:根据蛋白质的PDB,获得每个氨基酸的原子坐标,然后计算了所有残基队之间的欧式距离,形成了一个距离图
第二步: 将小于或等于所选cutoff 为 1,大于cutoff 为0,将该蛋白质距离图转换为邻接矩阵,custoff:14A
different strategy: to process protein distance maps into continuous matrices in which values are in the range of 0 to 1. 用公式进行标准化,如果小于或者等于cutoff
GCN with initial residual and identify mapping
本文标签: InteractionSiteproteinStructureAware
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