可达表面积
估计员
计算机科学
皮尔逊积矩相关系数
度量(数据仓库)
Atom(片上系统)
相关性
深度学习
功能(生物学)
残留物(化学)
蛋白质结构预测
算法
数据挖掘
人工智能
作者
Jianzhao Gao,Shuangjia Zheng,Mengting Yao,Peikun Wu
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2021-08-27
卷期号:38 (1): 94-98
被引量:2
标识
DOI:10.1093/bioinformatics/btab616
摘要
Abstract Motivation The solvent accessible surface is an essential structural property measure related to the protein structure and protein function. Relative solvent accessible area (RSA) is a standard measure to describe the degree of residue exposure in the protein surface or inside of protein. However, this computation will fail when the residues information is missing. Results In this article, we proposed a novel method for estimation RSA using the Cα atom distance matrix with the deep learning method (EAGERER). The new method, EAGERER, achieves Pearson correlation coefficients of 0.921–0.928 on two independent test datasets. We empirically demonstrate that EAGERER can yield better Pearson correlation coefficients than existing RSA estimators, such as coordination number, half sphere exposure and SphereCon. To the best of our knowledge, EAGERER represents the first method to estimate the solvent accessible area using limited information with a deep learning model. It could be useful to the protein structure and protein function prediction. Availabilityand implementation The method is free available at https://github.com/cliffgao/EAGERER. Supplementary information Supplementary data are available at Bioinformatics online.
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