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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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BackgroundVendruscolo, M., & Fuxreiter, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.07.414409 · Cited by 5
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InfluentialSelf-citationMTPSol: Multimodal Twin Protein Solubility Prediction Architecture Based on Pretrained Models
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“Consistent with this, we calculated the CamSol solubility score (Sormanni et al., 2015), predicting a small decrease in solubility for the optimized variants likely due to the removal of charges by some mutations, such as R20T and K48Q (Supplementary Fig.”
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“…of CamSol, known as CamSol structurally corrected, applies a PDB file as input, provides structural correction to the intrinsic solubility, and calculates the protein solubility based on the amino acid proximity in the structure and regarding their solvent exposure (Sormanni et al., 2015).”
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“To assess aggregation propensity, we considered the CamSol solubility score, which is based on a phenomenological amino acid aggregation scale [46].”
MethodDeep Learning-Based Prediction and Suppression of Protein Aggregation- Prone RegionsPreprint
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“Aggregation predictors or physicochemical scales (Tango 12 , Amypred 23 , Camsol 13 , PLAAC 14 , Aggrescan 15 ) were used to calculate a score for each sequence.”
McKay, C. E., Deans, M., Connor, J. P., Saunders, J. C., Lloyd, C., Radford, S. E., & Brockwell, D. J. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2516165122 · Cited by 4
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Self-citationAggrescanAI: Prediction of Aggregation-Prone Regions Using Contextualized Embeddings
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“Among the tested prediction methods, only 4 (SOuLMuSiC, PON-Sol2 34 , CamSol 32 , SODA 33 ) directly predict solubility changes upon mutations, with the former three providing a continuous score and the latter being a binary classifier.”
InfluentialKuri, P. R., & Goswami, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-69896-1 · Cited by 4
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Nipah Virus Infection Generates Ordered Structures in Cellulo
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Surface interaction patches link non-specific binding and phase separation of antibodiesPreprint
Ausserwӧger, H., Krainer, G., Welsh, T. J., Šneideris, T., Schneider, M. M., Invernizzi, G., Herling, T. W., Lorenzen, N., & Knowles, T. P. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.07.483238 · Cited by 4
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“▲ CAUTION CamSol can run in graphics mode, which is not advised in the case of multiple sequence runs.”
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InfluentialChowdhury, V. R., Röntgen, A., Greenig, M., Méndez, Y., Spiegel, S., Nowinska, M., Ramon, A., Sormanni, P., Chan, A., & Vendruscolo, M. (2025) · Applied Research · doi:10.1002/appl.70012 · Cited by 2
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“6 47,48 (Supplementary Fig.”
Brezinova, M., Brotzakis, Z. F., Horne, R. I., Chowdhury, V. R., Gregory, R. C., Bian, Y., Díaz, A. G., Gentile, F., & Vendruscolo, M. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00159-5 · Cited by 3
“Next, we added a solubility fi lter using the structurally-corrected version of CamSol (Sormanni et al, 2015), a computational method to identify regions of low solubility on the surfaces of protein structures (Fig.”
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“Camsol refers to the Camsol method [24] and Gravy refers to the biopython hydrophobicity score function. its performance, for example by doubling the number of training steps from 8000 to 16000.”
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“…al ., 2006), Waltz (Maurer-Stroh et al ., 2010) (both predicting the likelihood of β -sheet aggregation in parts of the sequence), and CamSol (predicting solubility in the sequence in arbitrary units) (Sormanni et al. , 2015, 2017) were used to predict the solubility and likelihood of aggregation.”
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Massively parallel quantification of mutational impact on IAPP amyloid formation
Badia, M., Batlle, C., & Bolognesi, B. (2026) · Nature Communications · doi:10.1038/s41467-026-70611-z · Cited by 3
DualGPT-AB: a dual-stage generative optimization framework for therapeutic antibody design
Xie, D., Chen, S., Zeng, X., Lu, D., Jiao, S., Xiao, S., Liu, J., Hao, J., Dai, H., & Peng, J. (2026) · Nature Computational Science · doi:10.1038/s43588-026-00976-0 · Cited by 2
From evolution to rational design: AI-driven engineering of safe and highly efficient food enzymes
Wu, X., Yuan, Y., Wang, F., Lu, F., & Liu, F. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108901 · Cited by 2
Thorsteinson, N., Comeau, S. R., & Kumar, S. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2609-2_11 · Cited by 2
Emperador, A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25116021 · Cited by 2
“…(accessed on 23 May 2024)), Solubis [14] (https://solubis.switchlab.org/ ( accessed on 23 May 2024)), or Camsol [15] (https://www-cohsoftware.ch.cam.ac.uk (accessed on 23 May 2024)) use bioinformatics-based methods instead of MD-based methods (for a collection…”
MethodNielsen, G. H., Schmitz, Z. D., & Hackel, B. J. (2024) · Protein Engineering Design and Selection · doi:10.1093/protein/gzae010 · Cited by 2
Radhakumar, D. S., Thiyagarajan, S., Rajaram, K., & Parsanathan, R. (2024) · Medicinal Chemistry · doi:10.2174/0115734064312418240614104220 · Cited by 2
Pretti, E., & Shell, M. S. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c07248 · Cited by 2
Amyloid-forming property of the N-terminal 1−70 residues of human apolipoprotein A-IV
Namba, N., Danjo, T., Kitagawa, Y., Naito, Y., Ohgita, T., Shimanouchi, T., & Saito, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-97992-3 · Cited by 2
A New Highly Specific and Soluble Protease for Precise Removal of N‑Terminal Purification Tags
Shams, R. S., & Regan, L. (2025) · ACS Omega · doi:10.1021/acsomega.5c01764 · Cited by 2
Modifying recombinant purple acid phosphatase using computational design
Venkatramani, A., Ali, M., Predeina, O., Molloy, J., Sormanni, P., & Hall, E. A. H. (2025) · European Biophysics Journal · doi:10.1007/s00249-025-01779-3 · Cited by 2
“Previous studies have demonstrated that a combination of mutations are often necessary to achieve substantial improvements in protein stability and solubility (Sormanni et al. 2015; Bhadouria et al. 2017; Bhadouria and Giri 2022).”
Self-citationBiophysical Aspects of Alzheimer’s Disease: Implications for Pharmaceutical Sciences
Arosio, P. (2017) · Pharmaceutical Research · doi:10.1007/s11095-017-2266-4 · Cited by 2
Ibrahim, A., Reljić, R., Drake, P. M. W., & K‐C., J. (2020) · Protein Expression and Purification · doi:10.1016/j.pep.2020.105691 · Cited by 2
Characterization and computational simulation of human Syx, a RhoGEF implicated in glioblastoma
Boyd, R. J., Olson, T. L., Zook, J. D., Stein, D., Aceves, M., Lin, W., Craciunescu, F. M., Hansen, D. T., Anastasiadis, P. Z., Singharoy, A., et al. (2022) · The FASEB Journal · doi:10.1096/fj.202101808rr · Cited by 1
“46 The resulting prediction was encoded into the B-factor of the protein and visualized with PyMol.”
Szulc, N., Gąsior‐Głogowska, M., Żyłka, P., Szefczyk, M., Wojciechowski, J. W., Żak, A. M., Dyrka, W., Kaczorowska, A., Burdukiewicz, M., Tarek, M., et al. (2024) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2024.124094 · Cited by 2
Nowicka, U., Chrościcki, P., Stroobants, K., Sladowska, M., Turek, M., Uszczyńska-Ratajczak, B., Kundra, R., Goral, T., Perni, M., Dobson, C. M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.02.442342 · Cited by 2
“Protein solubility was analyzed using CamSol (Sormanni et al., 2015) software.”
MethodSelf-citationGlobal Prediction of Candidate R-Loop Binding and R-Loop Regulatory ProteinsPreprint
Fournier, L.-A., Kumar, A., Smith, T., Su, E., Moksa, M., Hirst, M., & Stirling, P. C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.09.454968 · Cited by 2
“We then measured solubility scores using the CamSol solubility prediction method that utilizes protein sequences to assign solubility scores (32).”
MethodKorona, D., Dirnberger, B., Giachello, C. N., Queiroz, R. M. L., Minde, D., Deery, M. J., Johnson, G., Müller, K. H., Firth, L. C., Earley, F. G., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.13.456240 · Cited by 2
“Focusing on the membrane receptors solubilized by SMA, we analysed the amino acid sequence properties of identified proteins and calculated an overall solubility score (Sormanni et al., 2015; Sormanni et al., 2017).”
MethodSequence-Based Prediction of Ph-Dependent Protein Solubility Using CamsolPreprint
Oeller, M., Kang, R. J. D., Bell, R., Ausserwӧger, H., Sormanni, P., & Vendruscolo, M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4189397 · Cited by 2
Self-citationSUMO – In Silico Sequence Assessment Using Multiple Optimization ParametersPreprint
Evers, A., Malhotra, S., Bolick, W.-G., Najafian, A., Borisovska, M., Warszawski, S., Fomekong‐Nanfack, Y., Kühn, D., Rippmann, F., Crespo, A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.19.517175 · Cited by 2
Martín, M., & Bolognesi, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.15.557952 · Cited by 2
Zalba, N., Ursua-Medrano, P., & Bustince, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.03.597105 · Cited by 2
“The calculation of the overall solubility, performed as described in (26), on the WT form and the two mutant variants (Table S3) demonstrates how solubility of both mutants has also increased compared to that of the WT form.”
MethodOverath, M. D., Thumtecho, S., Rivera‐de‐Torre, E., Bénard-Valle, M., Wolff, D. S., Grahadi, R., Björnsson, K. H., Rygaard, A. M., Hofmann, N., Ledergerber, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.19.700436 · Cited by 2
“The CamSol method [36] was used via the online server (https://www-629 cohsoftware.ch.cam.ac.uk/index.php/camsolcombination) to propose solubility-improving mutations for the 19 αBgtx poly-specific V H Hs with high-confidence structures.”
InfluentialATM kinase phosphorylates Ser15 of p53 in a pH-dependent manner
Pektaş, S. (2024) · Trakya University Journal of Natural Sciences · doi:10.23902/trkjnat.1499251 · Cited by 1
Cofactor-Independent Amino Acid Epimerases with Catalytic Serines Instead of Cysteines
Lamer, T., van Belkum, M. J., Chen, P., Perov, I., Heard, B. L., Wijewardane, A., Lemieux, M. J., & Vederas, J. C. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169375 · Cited by 1
Kotel'nikov, D. D., Tatarinova, K. S., & Zhdanov, D. D. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26199262 · Cited by 1
“The solubility of the nanobodies was assessed by the CamSol Structurally Corrected tool (https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolstrucorr (accessed on 22 July 2025)) [71,72] utilizing NB2-derived structures cleaned on the same server with pH = 7 and PatchRadius = 10 Å for all designs.”
Arif, S., & Aslam, F. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111144 · Cited by 1
Raupach, J., Housria, Y., Hein, S., Schnierle, B. S., Durmaz, H., Zahn, T. R., & Hildt, E. (2025) · Vaccine · doi:10.1016/j.vaccine.2025.127987 · Cited by 1
Machine Learning Approaches for Interpretable Antibody Property Prediction Using Structural Data
Michalewicz, K., Barahona, M., & Bravi, B. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5408-8_7 · Cited by 1
“More recently, several ML models have incorporated both amino acid sequence and structural information for antibody property prediction [17, 18], including binding affinity and specificity [19, 20].”
Wang, S., Boyens‐Thiele, L., Bicanin, M., Svensson, B., Buell, A. K., & Møller, M. S. (2025) · Sustainable Food Proteins · doi:10.1002/sfp2.70040 · Cited by 1
Rational Design of Nanobodies Targeting LINGO‐1
Röntgen, A., Chowdhury, V. R., Ramon, A., Greenig, M., Xu, X., Sormanni, P., & Vendruscolo, M. (2026) · Applied Research · doi:10.1002/appl.70071 · Cited by 1
Self-citation
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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