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Prediction of protein solubility in Escherichia coli using logistic regression
Abstract
In this article we present a new and more accurate model for the prediction of the solubility of proteins overexpressed in the bacterium Escherichia coli . The model uses the statistical technique of logistic regression. To build this model, 32 parameters that could potentially correlate well with solubility were used. In addition, the protein database was expanded compared to those used previously. We tested several different implementations of logistic regression with varied results. The best implementation, which is the one we report, exhibits excellent overall prediction accuracies: 94% for the model and 87% by cross‐validation. For comparison, we also tested discriminant analysis using the same parameters, and we obtained a less accurate prediction (69% cross‐validation accuracy for the stepwise forward plus interactions model). Biotechnol. Bioeng. 2010; 105: 374–383. © 2009 Wiley Periodicals, Inc.
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“supporting previous findings that cysteine residues contribute to poor solubility in the E.coli expression system (Diaz et al., 2010; Wilkinson and Harrison, 1991).”
From Solubility-Weighted Index: fast and accurate prediction of protein solubility (2020) · cites this paper as Diaz et al., 2010
In the citing paper
“This idea aligns with our understanding that protein solubility and folding are closely linked, and folding occurs cotranslationally, a complex process that is driven various intrinsic and extrinsic factors (Chiti et al., 2003; Davis et al., 1999; Diaz et al., 2010; Natan et al., 2018; Tartaglia et al., 2004; Wilkinson and Harrison, 1991).”
From Solubility-Weighted Index: fast and accurate prediction of protein solubility (2020) · cites this paper as Diaz et al., 2010
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Metadata
- Authors
- Diaz, A. A., Tomba, E., Lennarson, R., Richard, R., Bagajewicz, M. J., & Harrison, R. G.
- Affiliations
- University of Oklahoma (US)
- Corresponding author
- Harrison, Roger G.
- Venue
- Biotechnology and Bioengineering
- Publisher
- Wiley
- Published
- 2009-10-20
- Volume
- 105
- Issue
- 2
- Pages
- 374-383
- ISSN
- 0006-3592 (ISSN-L)
- 1097-0290
- OpenAlex
- W2116818641
- Cited by
- 90
- References
- 37
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 6 · 2023: 6 · 2022: 6 · 2021: 5 · 2020: 6 · 2019: 9 · 2018: 11 · 2017: 7 · 2016: 6 · 2015: 4 · 2014: 10 · 2013: 3 · 2012: 2
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biotechnology and Bioengineering (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein solubility prediction, Escherichia coli, recombinant protein overexpression, logistic regression, discriminant analysis, solubility parameter, protein databases, cross-validation accuracy
- MeSH terms
- Inclusion Bodies, Escherichia coli, Recombinant Proteins, Solubility, Logistic Models, Databases, Protein
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