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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]
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“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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“This is the reason why computer- (32) or experiment- (33) based designs usually target residues within or close to CDRs; however, these changes might compromise significantly the antibody affinity.”
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InfluentialRational design of antibodies targeting specific epitopes within intrinsically disordered proteins
Sormanni, P., Aprile, F. A., & Vendruscolo, M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422401112 · Cited by 149
“The effect of the triple Glu insertion was previously assessed (40), where it was shown to act as a powerful gatekeeper that significantly increased the solubility of the VH domain.”
MethodInfluentialSelf-citationTrade-offs between enzyme fitness and solubility illuminated by deep mutational scanning
Klesmith, J. R., Bacik, J. P., Wrenbeck, E. E., Michalczyk, R., & Whitehead, T. A. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1614437114 · Cited by 149
“Computational approaches have been used to evaluate and design thermodynamic stability (1) and aggregation propensity (2, 3).”
MethodAprile, F. A., Sormanni, P., Perni, M., Arosio, P., Linse, S. S., Knowles, T. P. J., Dobson, C. M., & Vendruscolo, M. (2017) · Science Advances · doi:10.1126/sciadv.1700488 · Cited by 144
Self-citationNarayanan, H., Dingfelder, F., Butté, A., Lorenzen, N., Sokolov, M., & Arosio, P. (2021) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2020.12.004 · Cited by 151
Vendruscolo, M., & Fuxreiter, M. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167201 · Cited by 143
Self-citationThird generation antibody discovery methods: in silico rational design
Sormanni, P., Aprile, F. A., & Vendruscolo, M. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00523k · Cited by 143
Self-citationRapid and accurate in silico solubility screening of a monoclonal antibody library
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“OptSolMut and CamSol require a known 3-”
Rebeaud, M. E., Mallik, S., Goloubinoff, P., & Tawfik, D. S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2020885118 · Cited by 119
“This prediction is restricted by the fact that some of the predicted segments actually reside in the hydrophobic cores of stably folded proteins and hence do not comprise aggregation hotspots (Sormanni et al., 2015).”
BackgroundNetSolP: predicting protein solubility in Escherichia coli using language models
Thumuluri, V., Martiny, H.-M., Armenteros, J. J. A., Salomon, J., Nielsen, H., & Johansen, A. R. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab801 · Cited by 118
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Self-citationSolubility-Weighted Index: fast and accurate prediction of protein solubility
Bhandari, B. K., Gardner, P. P., & Lim, C. S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa578 · Cited by 97
“2017 ) , CamSol v2.1 (Sormanni, Aprile, and Vendruscolo 2015; Sormanni et al.”
Kulenkampff, K., Perez, A.-M. W., Sormanni, P., Habchi, J., & Vendruscolo, M. (2021) · Nature Reviews Chemistry · doi:10.1038/s41570-021-00254-9 · Cited by 113
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Computational tools help improve protein stability but with a solubility tradeoff
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“Prediction of mutant solubility was performed using the web servers for Aggrescan3D (99) (biocomp.chem.uw.edu.pl/A3D/), 3 CamSol (100), PASTA (101) (protein.bio.unipd.it/pasta2/), 3 TANGO (102) (tango.crg.es/), 3 ZipperDB (103) (services. mbi.ucla.edu/zipperdb/intro), 3 and Zyggregator (104).”
MethodUlamec, S. M., Brockwell, D. J., & Radford, S. E. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.611285 · Cited by 106
“regions with low solubility predicted by CamSol (below –1 is aggregation promoting highlighted in blue) (Sormanni et al., 2015) and the β-aggregation potential of”
BackgroundComputational design of enzymes for biotechnological applications
Planas-Iglesias, J., Marques, S. M., Pinto, G., Musil, M., Štourač, J., Damborský, J., & Bednář, D. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107696 · Cited by 107
Advances in computational structure-based antibody design
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In vitro and in silico assessment of the developability of a designed monoclonal antibody library
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“Specifically, we calculated the intrinsic solubility profile (Figure 1) of HzATNP from its amino acid sequence using the CamSol intrinsic method.(39) Then, we carried out structural corrections to the intrinsic solubility profile using a homology model of HzATNP.”
BackgroundMethodInfluentialSelf-citationSequence grammar underlying the unfolding and phase separation of globular proteins
Ruff, K. M., Choi, Y. H., Cox, D., Ormsby, A. R., Myung, Y., Ascher, D. B., Radford, S. E., Pappu, R. V., & Hatters, D. M. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.06.024 · Cited by 101
“To test this hypothesis, we used the CamSol method (Sormanni et al., 2015) to calculate relative solubilities normalized to that of WT barnase.”
Computational methods to predict protein aggregation
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Wang, C., & Zou, Q. (2023) · BMC Biology · doi:10.1186/s12915-023-01510-8 · Cited by 94
“As listed in Table S1, five of the 13 models, rWH, CCSOL [23], ESPRESSO [24], CamSol [25], and Protein-Sol [26], were built on datasets extracted from specifically published research, and the five datasets were not considered for model training in our study because of their insufficient…”
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“124 Similarly, a statistically significant correlation (R ~ 0.71 to 0.93) between CamSol predictions and solubility measurements was also reported for mutational variants of a troublesome mAb.”
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“In addition, molecular dynamics (MD) simulations at the atomistic level allow designing proteins with enhanced solubility [25], and can provide insights into protein-solvent interactions, as well as in osmolyteinduced protein refolding or denaturation [26–28].”
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Wilman, W., Wróbel, S., Bielska, W., Deszyński, P., Dudzic, P., Jaszczyszyn, I., Kaniewski, J., Młokosiewicz, J., Rouyan, A., Satława, T., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac267 · Cited by 86
“The authors calculated the Fvcsp [87], Camsol score [88] and NetMHC2PAN [89] to filter the set of predicted binders.”
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Sankar, K., Trainor, K., Blazer, L. L., Adams, J., Sidhu, S. S., Day, T., Meiering, E. M., & Maier, J. K. X. (2022) · Molecular Informatics · doi:10.1002/minf.202100240 · Cited by 72
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“In Table 2, SODA performance using only sequence information is compared with the published solubility predictors CamSol (21), SOLpro (17) and Proso II (20).”
BackgroundMethodInfluentialSantos, J., Pujols, J., Pallarès, I., Iglesias, V., & Ventura, S. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.05.026 · Cited by 74
“Another structure-based approach has been developed by Sormanni and coworkers [97] The CamSol method applies the physicochemical principles implemented in the sequence-based predictor Zyggregator and performs structural corrections similar to those of SAP or A3D to compute the solubility of native…”
MethodTiller, K. E., Li, L., Kumar, S., Julian, M. C., Garde, S., & Tessier, P. M. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.m117.783837 · Cited by 71
“were determined using the CamSol intrinsic method (32).”
MethodZaman, M., & Andreasen, M. (2020) · eLife · doi:10.7554/elife.59776 · Cited by 74
“Compared to the other PSM peptides PSMa4 is the one with both the lowest solubility score and the lowest calculated aggregation propensity when computing these using the CamSol algorithm (Sormanni et al., 2017; Sormanni et al., 2015; Figure 4—figure supplements 2 and Table 5).”
MethodComputational analysis of the amino acid interactions that promote or decrease protein solubility
Hou, Q., Bourgeas, R., Pucci, F., & Rooman, M. J. (2018) · Scientific Reports · doi:10.1038/s41598-018-32988-w · Cited by 68
“Indeed, it saves costly, time-consuming, wet lab experiments that are needed to reduce unwanted aggregate formation and increase solubility 14,23,24 .”
BackgroundStructural and functional characterization of a putative de novo gene in Drosophila
Lange, A., Patel, P. H., Heames, B., Damry, A. M., Saenger, T., Jackson, C. J., Findlay, G. D., & Bornberg‐Bauer, E. (2021) · Nature Communications · doi:10.1038/s41467-021-21667-6 · Cited by 67
“Aggregation propensity was predicted 430 using TANGO [80] and solubility was predicted using [81].”
MethodPeptides as Potential Therapeutics for Alzheimer’s Disease
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“CamSOL incorporates the sliding window method to develop soluble antibodies targeting Aβ peptides [313].”
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Self-citationDistinct Features of Stress Granule Proteins Predict Localization in Membraneless Organelles
Kuechler, E. R., Budzyńska, P. M., Bernardini, J. P., Gsponer, J. A., & Mayor, T. (2020) · Journal of Molecular Biology · doi:10.1016/j.jmb.2020.02.020 · Cited by 69
Automated optimisation of solubility and conformational stability of antibodies and proteins
Rosace, A., Bennett, A., Oeller, M., Mortensen, M. M., Sakhnini, L. I., Lorenzen, N., Poulsen, C., & Sormanni, P. (2023) · Nature Communications · doi:10.1038/s41467-023-37668-6 · Cited by 69
Self-citationPON-Sol: prediction of effects of amino acid substitutions on protein solubility
Yang, Y., Niroula, A., Shen, B., & Vihinen, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw066 · Cited by 65
Seuma, M., Lehner, B., & Bolognesi, B. (2022) · Nature Communications · doi:10.1038/s41467-022-34742-3 · Cited by 63
Seuma, M., Faure, A. J., Badia, M., Lehner, B., & Bolognesi, B. (2021) · eLife · doi:10.7554/elife.63364 · Cited by 60
“We then calculated the log value for each variant relative to the WT score (single and double aa mutants for Tango, Zyggregator, CamSol and single aa mutants for Waltz).”
BackgroundMethodMarchand, T. L., de Rosa, M., Salvi, N., Sala, B. M., Andreas, L. B., Barbet‐Massin, E., Sormanni, P., Barbiroli, A. G., Porcari, R., Mota, C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04078-y · Cited by 61
Self-citationProtein Solubility Predictions Using the CamSol Method in the Study of Protein HomeostasisPreprint
Sormanni, P., & Vendruscolo, M. (2019) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a033845 · Cited by 59
“ulate a wide range of structurally heterogeneous states, including small and large oligomers, and it is thus challenging to distinguish between soluble and insoluble assemblies (Sormanni et al. 2015).”
BackgroundMethodInfluentialSelf-citationMakowski, E. K., Wu, L., Gupta, P., & Tessier, P. M. (2021) · mAbs · doi:10.1080/19420862.2021.1895540 · Cited by 63
“tionally screen potential antibody (and other protein) variants and select mutations predicted to enhance solubility while maintaining binding affinity and folding stability.(110) The algorithm calculates an intrinsic solubility profile from the protein sequence using a linear combination of physicochemical descriptor…”
MethodSingle residue modulators of amyloid formation in the N-terminal P1-region of α-synuclein
Ulamec, S. M., Maya‐Martinez, R., Byrd, E. J., Dewison, K. M., Xu, Y., Willis, L. F., Sobott, F., Heath, G. R., van Oosten‐Hawle, P., Buchman, V. L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32687-1 · Cited by 56
Sequence-based prediction of pH-dependent protein solubility using CamSol
Oeller, M., Kang, R. J. D., Bell, R., Ausserwӧger, H., Sormanni, P., & Vendruscolo, M. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad004 · Cited by 58
“For a detailed explanation of the CamSol approach, the reader is referred to the original CamSol paper [19].”
MethodInfluentialSelf-citationDiscovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning
Horne, R. I., Andrzejewska, E. A., Alam, P., Brotzakis, Z. F., Srivastava, A., Aubert, A., Nowinska, M., Gregory, R. C., Staats, R., Possenti, A., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01580-x · Cited by 57
“3a), and its mid to low solubility according to CamSol 40 (Supplementary Fig.”
Self-citationEngineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design
Kuroda, D., & Tsumoto, K. (2020) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2020.01.011 · Cited by 59
Insight into the protein solubility driving forces with neural attention
Raimondi, D., Orlando, G., Fariselli, P., & Moreau, Y. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007722 · Cited by 56
“To evaluate the ability of SKADE to identify mutations increasing or decreasing protein solubility, we used the CamSol [14] dataset, which contains 56 experimentally validated mutations.”
BackgroundMethodInfluentialDewaker, V., Morya, V. K., Kim, Y. H., Park, S. T., Kim, H. S., & Koh, Y. H. (2025) · Biomarker Research · doi:10.1186/s40364-025-00764-4 · Cited by 62
Camilloni, C., Sala, B. M., Sormanni, P., Porcari, R., Corazza, A., de Rosa, M., Zanini, S., Barbiroli, A. G., Esposito, G., Bolognesi, M., et al. (2016) · Scientific Reports · doi:10.1038/srep25559 · Cited by 60
Self-citationCombining Structural Aggregation Propensity and Stability Predictions To Redesign Protein Solubility
Gil-García, M., Bañó‐Polo, M., Varejão, N., Jamroz, M., Kuriata, A., Díaz-Caballero, M., Lascorz, J., Morel, B., Navarro, S., Reverter, D., et al. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00341 · Cited by 56
In situ analysis of osmolyte mechanisms of proteome thermal stabilization
Pepelnjak, M., Velten, B., Näpflin, N. C., von Rosen, T., Palmiero, U. C., Ko, J. H., Maynard, H. D., Arosio, P., Weber-Ban, E. U., de Souza, N., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01568-7 · Cited by 58
“Solubility (CamSol) was calculated using CamSol software 88 .”
Mutant UBQLN2 promotes toxicity by modulating intrinsic self-assembly
Sharkey, L. M., Safren, N., Pithadia, A. S., Gerson, J. E., Dulchavsky, M. E., Fischer, S., Patel, R., Lantis, G., Ashraf, N., Kim, J. H., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810522115 · Cited by 53
Amyloidogenic proteins in the SARS-CoV and SARS-CoV-2 proteomes
Bhardwaj, T., Gadhave, K., Kapuganti, S. K., Kumar, P., Brotzakis, Z. F., Saumya, K. U., Nayak, N., Kumar, A., Joshi, R., Mukherjee, B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36234-4 · Cited by 52
Self-citationFragment-based computational design of antibodies targeting structured epitopes
Aguilar Rangel, M., Bedwell, A., Costanzi, E., Taylor, R. J., Russo, R., Bernardes, G. J. L., Ricagno, S., Frydman, J., Vendruscolo, M., & Sormanni, P. (2022) · Science Advances · doi:10.1126/sciadv.abp9540 · Cited by 55
“Therefore, all possible CDR motif candidates generated for the input epitope are ranked according to the total num ber of favorable interactions, the number of interactions that could not be optimized, and a solubility score calculated with the CamSol method (15).”
BackgroundMethodInfluentialSelf-citationDalkas, G. A., & Rooman, M. J. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1528-9 · Cited by 52
“The solubility per residue was calculated using the sequence-based version of the CamSol [63] program (F12).”
MethodN-glycosylation as a eukaryotic protective mechanism against protein aggregation
Duran-Romaña, R., Houben, B., De Vleeschouwer, M., Louros, N., Wilson, M. P., Matthijs, G., Schymkowitz, J., & Rousseau, F. (2024) · Science Advances · doi:10.1126/sciadv.adk8173 · Cited by 52
Bottleneck in secretion of α-amylase in Bacillus subtilis
Yan, S., & Wu, G. (2017) · Microbial Cell Factories · doi:10.1186/s12934-017-0738-1 · Cited by 53
“Also, unfolded α-amylase is less soluble than folded one because we find α-amylase precursor (accession number H9BPX5) poorly soluble when using CamSol [98] to compute its solubility (Fig.”
MethodKhan, M. A. A., Ami, J. Q., Faisal, K., Chowdhury, R., Ghosh, P., Hossain, F., Wahed, A. A. E., & Mondal, D. (2020) · Parasites & Vectors · doi:10.1186/s13071-020-04064-8 · Cited by 50
Mason, D. M., Friedensohn, S., Weber, C. R., Jordi, C., Wagner, B., Meng, S. M., Gaínza, P., Correia, B. E., & Reddy, S. T. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/617860 · Cited by 50
Bell, R., Thrush, R. J., Castellana-Cruz, M., Oeller, M., Staats, R., Nene, A., Flagmeier, P., Xu, C. K., Satapathy, S., Galvagnion, C., et al. (2022) · Biochemistry · doi:10.1021/acs.biochem.2c00104 · Cited by 50
Self-citationComparison of hydrophobicity scales for predicting biophysical properties of antibodies
Waibl, F., Fernández‐Quintero, M. L., Wedl, F. S., Kettenberger, H., Georges, G. J., & Liedl, K. R. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.960194 · Cited by 46
“Sequence-based methods, such as CamSol (Sormanni et al., 2015) and many others (Conchillo-Sole et al.”
MethodBell, R., & Vendruscolo, M. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.661117 · Cited by 52
Self-citationWu, J., Schultz, J. S., Weldon, C. L., Sule, S. V., Chai, Q., Geng, S. B., Dickinson, C. D., & Tessier, P. M. (2015) · Protein Engineering Design and Selection · doi:10.1093/protein/gzv045 · Cited by 48
“…Tanha et al., 2006; Chennamsetty et al., 2009; Conley et al., 2011; Perchiacca et al., 2011, 2012, 2014; Yadav et al., 2011; Yoshino et al., 2011; Bethea et al., 2012; Dudgeon et al., 2012; Kim et al., 2012; Chaudhri et al., 2013; Kim et al., 2014; Nichols et al., 2015; Sormanni et al., 2015).”
Desmin forms toxic, seeding-competent amyloid aggregates that persist in muscle fibers
Kedia, N., Arhzaouy, K., Pittman, S. K., Sun, Y., Batchelor, M., Weihl, C. C., & Bieschke, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1908263116 · Cited by 48
“Several algorithms have been developed to predict amyloidogenicity based on hydrophobicity, amino acid composition, and sequence (20, 23).”
BackgroundMethodAdriaenssens, E., Tedesco, B., Mediani, L., Asselbergh, B., Crippa, V., Antoniani, F., Carra, S., Poletti, A., & Timmerman, V. (2020) · Scientific Reports · doi:10.1038/s41598-020-65664-z · Cited by 47
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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