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 consistent {text{R}}^{2} 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 protein solubility predictions. More importantly, this architecture could be easily extended to other protein prediction tasks requiring a raw protein sequence.
Highlights
Over the past 20 years, recombinant protein had played a vital role in biotechnology and medicine, including novel therapeutic protein drugs and antibodies [1]
A similar constructed structure helps us evaluate the predicted performance when compared to the possible experimental structure. Inspired by these new development tools, we proposed a novel structure-aware method GraphSol for protein solubility prediction from the sequence by combining predicted contact maps and graph neural networks
Given a protein sequence that consists of L amino acids, the whole protein could be expressed as a topological attributed graph G(F, E), with F for the feature set of all residues and E for the residual contacts according to predicted protein contact map
Summary
Over the past 20 years, recombinant protein had played a vital role in biotechnology and medicine, including novel therapeutic protein drugs and antibodies [1]. To enhance the performance of recombinant proteins, many experimental technologies have been developed, e.g. directed evolution, immobilization, designing better promoters, optimizing codon usage, and changing culture conditions including media and temperature [5, 6]. Such empirical optimizations are laborintensive and time-consuming. Given an exact experimental condition (i.e. temperature, expression host, etc.), the solubility is determined mainly by its primary structure that is decided by the sequence [3] To this end, two types of computational approaches have been proposed to predict the protein solubility: physical-based and machine/deep learning-based methods
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