ArticleMolecular bioSystems2013
Discrimination of soluble and aggregation-prone proteins based on sequence information.
Article in Molecular bioSystems, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Proteins for Polyphenol Encapsulation, Functional Food Production, and Breast Cancer Incidence Reduction: Impact of pH Conditions, BSA Particle Size, and Curcumin Concentration.The protein journal · 2026Review
- A review on computational models for predicting protein solubility.Journal of microbiology (Seoul, Korea) · 2025Review
- A Novel Strategy to Identify Endolysins with Lytic Activity against Methicillin-ResistantInternational journal of molecular sciences · 2023Article
- Enhancement of the solubility of recombinant proteins by fusion with a short-disordered peptide.Journal of microbiology (Seoul, Korea) · 2022Article
- Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map.Journal of cheminformatics · 2021Article
- Codon usage clusters correlation: towards protein solubility prediction in heterologous expression systems in E. coli.Scientific reports · 2018Article
- Classification model of amino acid sequences prone to aggregation of therapeutic proteins.In silico pharmacology · 2016Article
- A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli.BMC bioinformatics · 2014Article
- How do eubacterial organisms manage aggregation-prone proteome?F1000Research · 2014Article
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Authors and funding
2 authors.
Funding
Abstract
Understanding the factors governing protein solubility is a key to grasp the mechanisms of protein solubility and may provide insight into protein aggregation and misfolding related diseases such as Alzheimer's disease. In this work, we attempt to identify factors important to protein solubility using feature selection. Firstly, we calculate 1438 features including physicochemical properties and statistics for each protein. Random Forest algorithm is used to select the most informative and the minimal subset of features based on their predictive performance. A predictive model is built based on 17 selected features. Compared with previous models, our model achieves better performance with a sensitivity of 0.82, specificity 0.85, ACC 0.84, AUC 0.91 and MCC 0.67. Furthermore, a model using a redundancy-reduced dataset (sequence identity <= 30%) achieves the same performance as the model without redundancy reduction. Our results provide not only a reliable model for predicting protein solubility but also a list of features important to protein solubility. The predictive model is implemented as a freely available web application at .
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Registered trials
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