ArticleBriefings in bioinformatics2024
PLM_Sol: predicting protein solubility by benchmarking multiple protein language models with the updated Escherichia coli protein solubility dataset.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 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
- Engineering of a Carbonic Anhydrase fromInternational journal of molecular sciences · 2026Article
- Integrating multimodal features with deep learning for protein solubility prediction.Journal of cheminformatics · 2026Article
- A Miniaturized, High-Throughput Aqueous Solvent-Centric Method for Protein Solubility Screening.Biochemistry · 2026Article
- Unified genomic and chemical representations enable bidirectional biosynthetic gene cluster and natural product retrieval.Scientific reports · 2026Article
- RP3Net: a deep learning model for predicting recombinant protein production in Escherichia coli.Bioinformatics (Oxford, England) · 2026Article
- Large-scale statistical dissection of sequence-derived biochemical features distinguishing soluble and insoluble proteins.PloS one · 2026Article
- DeepVIC: modular prediction and classification of bacterial virulence factors using protein language model embeddings.Bioinformatics advances · 2026Article
- MiLaSol: modeling protein solubility by mixing up multiple protein language models.Bioinformatics advances · 2026Article
- Deep learning on protein language model embeddings unlocks accurate prediction of protein solubility.Frontiers in microbiology · 2026Article
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- A review on computational models for predicting protein solubility.Journal of microbiology (Seoul, Korea) · 2025Review
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8 authors.
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Abstract
Protein solubility plays a crucial role in various biotechnological, industrial, and biomedical applications. With the reduction in sequencing and gene synthesis costs, the adoption of high-throughput experimental screening coupled with tailored bioinformatic prediction has witnessed a rapidly growing trend for the development of novel functional enzymes of interest (EOI). High protein solubility rates are essential in this process and accurate prediction of solubility is a challenging task. As deep learning technology continues to evolve, attention-based protein language models (PLMs) can extract intrinsic information from protein sequences to a greater extent. Leveraging these models along with the increasing availability of protein solubility data inferred from structural database like the Protein Data Bank holds great potential to enhance the prediction of protein solubility. In this study, we curated an Updated Escherichia coli protein Solubility DataSet (UESolDS) and employed a combination of multiple PLMs and classification layers to predict protein solubility. The resulting best-performing model, named Protein Language Model-based protein Solubility prediction model (PLM_Sol), demonstrated significant improvements over previous reported models, achieving a notable 6.4% increase in accuracy, 9.0% increase in F1_score, and 11.1% increase in Matthews correlation coefficient score on the independent test set. Moreover, additional evaluation utilizing our in-house synthesized protein resource as test data, encompassing diverse types of enzymes, also showcased the good performance of PLM_Sol. Overall, PLM_Sol exhibited consistent and promising performance across both independent test set and experimental set, thereby making it well suited for facilitating large-scale EOI studies. PLM_Sol is available as a standalone program and as an easy-to-use model at https://zenodo.org/doi/10.5281/zenodo.10675340.
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