Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map.

ABSTRACT: 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. In this study, we have developed a new structure-aware method GraphSol to predict protein solubility by attentive graph convolutional network (GCN), where the protein topology attribute graph was constructed through predicted contact maps only from the sequence. GraphSol was shown to substantially outperform other sequence-based methods. The model was proven to be stable by co

SUBMITTER: Chen J 

PROVIDER: S-EPMC7869490 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature