ArticleJournal of cheminformatics2021
Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Soluble protein analog selection engine (SPASE): An automated AI-powered server to improve protein engineering workflows.Protein science : a publication of the Protein Society · 2026Article
- Dynamics-informed multigraph neural networks for protein thermostability prediction and residue-level interpretation.iScience · 2026Article
- Integrating multimodal features with deep learning for protein solubility prediction.Journal of cheminformatics · 2026Article
- Computational glycosyltransferases masked deoxynivalenol toxicity and halted FHB spread in wheat grains.Journal of advanced research · 2026Article
- Universal feature selection for simultaneous interpretability of multitask datasets.Journal of cheminformatics · 2026Article
- ProGraphTrans: multimodal dynamic collaborative framework for protein representation learning.BMC biology · 2025Article
- SeekTP: identifying multifunctional therapeutic peptides via graph attention and deep property integration.Briefings in bioinformatics · 2025Article
- Gated-GPS: enhancing protein-protein interaction site prediction with scalable learning and imbalance-aware optimization.Briefings in bioinformatics · 2025Article
- Decoding Solubility Signatures from Amyloid Monomer Energy Landscapes.Journal of chemical theory and computation · 2025Article
- ProCeSa: Contrast-Enhanced Structure-Aware Network for Thermostability Prediction with Protein Language Models.Journal of chemical information and modeling · 2025Article
- ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks.ACS omega · 2025Article
- An Online Server for Geometry-Aware Protein Function Annotations Through Predicted Structure.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A review on computational models for predicting protein solubility.Journal of microbiology (Seoul, Korea) · 2025Review
- GRACE: Generative Redesign in Artificial Computational Enzymology.ACS synthetic biology · 2024Article
- Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.Interdisciplinary sciences, computational life sciences · 2024Article
- Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures.Nature communications · 2024Article
- ProSol-multi: Protein solubility prediction via amino acids multi-level correlation and discriminative distribution.Heliyon · 2024Article
- PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.Journal of cheminformatics · 2024Article
- GPSFun: geometry-aware protein sequence function predictions with language models.Nucleic acids research · 2024Article
- HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.Communications biology · 2024Article
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Authors and funding
4 authors.
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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 [Formula: see text] 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.
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