ArticleBMC biology2023
Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE.
Article in BMC biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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41 citing papers in PubMed.
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- Integrating multimodal features with deep learning for protein solubility prediction.Journal of cheminformatics · 2026Article
- In Silico Design and Computational Characterization of Novel Chimeric Multiepitope Antigens for Mpox Serosurveillance: An Immunoinformatics Approach.Health science reports · 2026Article
- In-silico design of a multi-epitope vaccine candidate against Strongyloides stercoralis.BMC infectious diseases · 2026Article
- Strategies for enhancing protein solubility: methods, applications, and prospects.NPJ science of food · 2026Review
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Structure-Guided Engineering of High-Affinity Antibodies Against Zika Virus Using Deep Learning and Molecular Dynamics.Chemistry & biodiversity · 2026Article
- Biological sequence analysis: Advances, medical applications, and challenges.Fundamental research · 2026Review
- Phase Diagrams as a DOE Tool in Biomolecular Crystallization.Advances in biochemical engineering/biotechnology · 2026Review
- Deep learning on protein language model embeddings unlocks accurate prediction of protein solubility.Frontiers in microbiology · 2026Article
- Strategy for the Construction of SARS-CoV-2 S and N Recombinant Proteins and Their Immunogenicity Evaluation.Biotech (Basel (Switzerland)) · 2025Article
- AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2025Article
- Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies.Structural dynamics (Melville, N.Y.) · 2025Article
- One Health Approach to the Computational Design of a Lipoprotein-Based Multi-Epitope Vaccine Against Human and Livestock Tuberculosis.International journal of molecular sciences · 2025Article
- ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks.ACS omega · 2025Article
- A review on computational models for predicting protein solubility.Journal of microbiology (Seoul, Korea) · 2025Review
- In silico design and assessment of a multi-epitope peptide vaccine against multidrug-resistantIn silico pharmacology · 2025Article
- Insight into Protein Engineering: FromCurrent pharmaceutical design · 2025Review
- Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference.PloS one · 2025Article
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2 authors.
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Abstract
backgroundProtein solubility is a precondition for efficient heterologous protein expression at the basis of most industrial applications and for functional interpretation in basic research. However, recurrent formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form. Despite numerous solubility prediction models having been developed over time, their performance remains unsatisfactory in the context of the current strong increase in available protein sequences. Hence, it is imperative to develop novel and highly accurate predictors that enable the prioritization of highly soluble proteins to reduce the cost of actual experimental work.
resultsIn this study, we developed a novel tool, DeepSoluE, which predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools. Furthermore, we explored specific features that have a dominant impact on the model performance as well as their interaction effects.
conclusionsDeepSoluE is suitable for the prediction of protein solubility in E. coli; it serves as a bioinformatics tool for prescreening of potentially soluble targets to reduce the cost of wet-experimental studies. The publicly available webserver is freely accessible at http://lab.malab.cn/~wangchao/softs/DeepSoluE/ .
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