The DOI matches its Crossref record; 61 of the 62 references it cites were found in a registry or an index.
The CamSol Method of Rational Design of Protein Mutants with Enhanced Solubility
Abstract
Protein solubility is often an essential requirement in biotechnological and biomedical applications. Great advances in understanding the principles that determine this specific property of proteins have been made during the past decade, in particular concerning the physicochemical characteristics of their constituent amino acids. By exploiting these advances, we present the CamSol method for the rational design of protein variants with enhanced solubility. The method works by performing a rapid computational screening of tens of thousand of mutations to identify those with the greatest impact on the solubility of the target protein while maintaining its native state and biological activity. The application to a single-domain antibody that targets the Alzheimer's Aβ peptide demonstrates that the method predicts with great accuracy solubility changes upon mutation, thus offering a cost-effective strategy to help the production of soluble proteins for academic and industrial purposes.
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“To predict the solubility and aggregation propensity of protein, several prediction tools have been developed, such as the Camsol [25], and SOLart [26], with SOLart currently holding state-of-the-art performance.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [25]
In the citing paper
“Good solubility is crucial [202, 203] to avoid aggregation that can potentially lead to loss of activity, degradation of antibodies or immunogenicity, as discussed previously.”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [202]
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Cites
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
Comparative Protein Modelling by Satisfaction of Spatial Restraints
Šali, A., & Blundell, T. L. (1993) · Journal of Molecular Biology · doi:10.1006/jmbi.1993.1626 · Cited by 12,977
Protein folding and misfolding
Dobson, C. M. (2003) · Nature · doi:10.1038/nature02261 · Cited by 4,791
Molecular chaperones in protein folding and proteostasis
Hartl, F. U., Bracher, A., & Hayer-Hartl, M. (2011) · Nature · doi:10.1038/nature10317 · Cited by 3,381
Adapting Proteostasis for Disease Intervention
Balch, W. E., Morimoto, R. I., Dillin, A., & Kelly, J. W. (2008) · Science · doi:10.1126/science.1141448 · Cited by 2,495
Protein Therapeutics: A Summary and Pharmacological Classification
Leader, B., Baca, Q. J., & Golan, D. E. (2008) · Nature Reviews Drug Discovery · doi:10.1038/nrd2399 · Cited by 2,161
Aggresomes, inclusion bodies and protein aggregation
Kopito, R. R. (2000) · Trends in Cell Biology · doi:10.1016/s0962-8924(00)01852-3 · Cited by 1,942
Modeller: Generation and Refinement of Homology-Based Protein Structure Models
Fiser, A., & Šali, A. (2003) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(03)74020-8 · Cited by 1,708
Experimentally determined hydrophobicity scale for proteins at membrane interfaces
Wimley, W. C., & White, S. H. (1996) · Nature Structural & Molecular Biology · doi:10.1038/nsb1096-842 · Cited by 1,691
Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins
Fernández‐Escamilla, A. M., Rousseau, F., Schymkowitz, J. W. H., & Serrano, L. G. (2004) · Nature Biotechnology · doi:10.1038/nbt1012 · Cited by 1,667
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Widespread Proteome Remodeling and Aggregation in Aging C. elegans
Walther, D. M., Kasturi, P., Zheng, M., Pinkert, S., Vecchi, G., Ciryam, P., Morimoto, R. I., Dobson, C. M., Vendruscolo, M., Mann, M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.03.032 · Cited by 569
“…are predicted to have significantly lower aggregation-propensity Z scores, are more charged, display more structural disorder (coil average) (Sormanni et al., 2015b), and are less hydrophobic compared to the proteins Cell 161, 919–932, May 7, 2015 ª2015 Elsevier Inc. 925 926 Cell 161,…”
BackgroundSelf-citationProGen2: Exploring the boundaries of protein language models
Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., & Madani, A. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.002 · Cited by 480
Posttranslational Modifications Mediate the Structural Diversity of Tauopathy Strains
Arakhamia, T., Lee, C. E., Carlomagno, Y., Kumar, M., Duong, D. M., Wesseling, H., Kundinger, S. R., Wang, K., Williams, D., Deture, M. A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.027 · Cited by 411
Current insights into protein solubility: A review of its importance for alternative proteins
Grossmann, L., & McClements, D. J. (2023) · Food Hydrocolloids · doi:10.1016/j.foodhyd.2022.108416 · Cited by 367
Protein Design: From the Aspect of Water Solubility and Stability
Qing, R., Hao, S., Smorodina, E., Jin, D., Zalevsky, A. O., & Zhang, S. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00757 · Cited by 334
Metadata
- Authors
- Sormanni, P., Aprile, F. A., & Vendruscolo, M.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Vendruscolo, Michele
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 427
- Issue
- 2
- Pages
- 478-490
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W2103083136
- Cited by
- 494
- References
- 62
- Citations by year
- 2026: 52 · 2025: 77 · 2024: 60 · 2023: 42 · 2022: 62 · 2021: 59 · 2020: 44 · 2019: 33 · 2018: 28 · 2017: 20 · 2016: 9 · 2015: 7
- FWCI
- 4.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Molecular Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein solubility, rational protein design, site-directed mutagenesis, nanobodies, amyloid-beta, Alzheimer's disease, soluble protein production, native state stability
- MeSH terms
- Amino Acids, Circular Dichroism, Cloning, Molecular, Computer Simulation, Escherichia coli, Models, Molecular, Protein Conformation, Recombinant Proteins, Solubility, Protein Engineering, Molecular Structure, Mutant Proteins, Chemical Phenomena
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