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Structure-aware Protein Solubility Prediction From Sequence Through Graph Convolutional Network And Predicted Contact Map

View ORCID ProfileJianwen Chen, View ORCID ProfileShuangjia Zheng, View ORCID ProfileHuiying Zhao, View ORCID ProfileYuedong Yang
doi: https://doi.org/10.1101/2020.06.24.169011
Jianwen Chen
1School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China
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Shuangjia Zheng
1School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China
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Huiying Zhao
2Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510000, China
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Yuedong Yang
1School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China
2Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510000, China
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  • For correspondence: yangyd25@mail.sysu.edu.cn
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Abstract

Motivation Protein solubility is significant in producing new soluble proteins that can reduce the cost of biocatalysts or therapeutic agents. Therefore, a computational model is highly desired to accurately predict protein solubility from the amino acid sequence. Many methods have been developed, but they are mostly based on the one-dimensional embedding of amino acids that is limited to catch spatially structural information.

Results In this study, we have developed a new structure-aware method to predict protein solubility by attentive graph convolutional network (GCN), where the protein topology attribute graph was constructed through predicted contact maps from the sequence. GraphSol was shown to substantially out-perform other sequence-based methods. The model was proven to be stable by consistent R2 of 0.48 in both the cross-validation and independent test of the eSOL dataset. To our best knowledge, this is the first study to utilize the GCN for sequence-based predictions. More importantly, this architecture could be extended to other protein prediction tasks.

Availability The package is available at http://biomed.nscc-gz.cn

Contact yangyd25{at}mail.sysu.edu.cn

Supplementary information Supplementary data are available at Bioinformatics online.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 25, 2020.
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Structure-aware Protein Solubility Prediction From Sequence Through Graph Convolutional Network And Predicted Contact Map
Jianwen Chen, Shuangjia Zheng, Huiying Zhao, Yuedong Yang
bioRxiv 2020.06.24.169011; doi: https://doi.org/10.1101/2020.06.24.169011
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Structure-aware Protein Solubility Prediction From Sequence Through Graph Convolutional Network And Predicted Contact Map
Jianwen Chen, Shuangjia Zheng, Huiying Zhao, Yuedong Yang
bioRxiv 2020.06.24.169011; doi: https://doi.org/10.1101/2020.06.24.169011

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