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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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“After the fitting of these parameters, the performance of the method was initially tested with a qualitative validation on solubility measurements from the literature [172] (55/56 correctly predicted solubility changes when using CamSol version 2 [128]), and in many other quantitative benchmarks [7, 128, 172, 217].”
BackgroundMethodInfluentialSelf-citationAggreProt: a web server for predicting and engineering aggregation prone regions in proteins
Planas-Iglesias, J., Borko, S., Swiatkowski, J., Elias, M., Havlásek, M., Salamon, O., Grakova, E., Kunka, A., Martinovič, T., Damborský, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae420 · Cited by 46
“…are typically fast, and rely either on experimentally derived scales of aggregation propensity, like in AGGRESCAN ( 16 ) or CamSol intrinsic ( 17 ), or features based on the protein amino acid composition (frequency distribution, physico-chemical properties, β -sheet propensity,…”
BackgroundUsing protein engineering to understand and modulate aggregation
Ebo, J. S., Guthertz, N., Radford, S. E., & Brockwell, D. J. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.01.005 · Cited by 44
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Thorsteinson, N., Gunn, J. R., Kelly, K., Long, W., & Labute, P. (2021) · mAbs · doi:10.1080/19420862.2021.1981805 · Cited by 42
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A pH-dependent switch promotes β-synuclein fibril formation via glutamate residues
Moriarty, G. M., Olson, M. P., Atieh, T. B., Janowska, M. K., Khare, S. D., & Baum, J. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.m117.780528 · Cited by 43
Blanco, M. A. (2022) · mAbs · doi:10.1080/19420862.2022.2044744 · Cited by 43
Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics
Pang, K. T., Yang, Y., Zhang, W., Ho, Y. S., Sormanni, P., Michaels, T. C. T., Walsh, I., & Chia, S. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108192 · Cited by 42
Self-citationStress‐specific aggregation of proteins in the amyloid bodies
Marijan, D., Tse, R., Elliott, K., Chandhok, S., Luo, M., Lacroix, E., & Audas, T. E. (2019) · FEBS Letters · doi:10.1002/1873-3468.13597 · Cited by 39
Inherent Biophysical Properties Modulate the Toxicity of Soluble Amyloidogenic Light Chains
Maritan, M., Romeo, M., Oberti, L., Sormanni, P., Tasaki, M., Russo, R., Ambrosetti, A., Motta, P., Rognoni, P., Mazzini, G., et al. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.12.015 · Cited by 41
“As an additional control, we ran predictions of intrinsic solubility using CamSol[47-49].”
MethodSelf-citationCoskuner‐Weber, O., & Uversky, V. N. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00926 · Cited by 40
Mechanism of phage sensing and restriction by toxin-antitoxin-chaperone systems
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An in vivo platform to select and evolve aggregation-resistant proteins
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Self-citationSequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids
Oeller, M., Kang, R. J. D., Bolt, H. L., dos Santos, A. L. G., Weinmann, A. L., Nikitidis, A., Zlatoidský, P., Su, W., Czechtizky, W., Maria, L. D., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42940-w · Cited by 40
“To address this problem, we have presented CamSol-PTM, a software that predicts the intrinsic solubility in aqueous solution at room temperature of peptides and proteins containing non-canonical amino acids based on the physicochemical properties of their amino acid sequences 75 – 77 .”
InfluentialSelf-citationAssessing amyloid fibrils and amorphous aggregates: A review
Basha, S., Mukunda, D. C., Pai, A. R., & Mahato, K. K. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.143725 · Cited by 40
TISIGNER.com: web services for improving recombinant protein production
Bhandari, B. K., Lim, C. S., & Gardner, P. P. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab175 · Cited by 38
AlphaDesign: a de novo protein design framework based on AlphaFold
(2025) · doi:10.1038/s44320-025-00119-z · Cited by 36
“To assess the impact of ADM redesign on de novo design quality, we evaluated both raw and redesigned outputs of AlphaDesign in terms of pLDDT, scRMSD computed using AF and ESMfold, interface predicted aligned error (ipAE) (Evans et al, 2021) and predicted solubility (Sormanni et al, 2015).”
InfluentialPredicting protein stability and solubility changes upon mutations: data perspective
Mazurenko, S. (2020) · ChemCatChem · doi:10.1002/cctc.202000933 · Cited by 34
An accelerated surface-mediated stress assay of antibody instability for developability studies
Kopp, M. R. G., Pérez, A.-M. W., Zucca, M. V., Palmiero, U. C., Friedrichsen, B., Lorenzen, N., & Arosio, P. (2020) · mAbs · doi:10.1080/19420862.2020.1815995 · Cited by 33
Vihinen, M. (2020) · ADMET & DMPK · doi:10.5599/admet.831 · Cited by 34
“The discussed tools included Aggrescan3D, CamSol, Spatial Aggregation Propensity (SAP) and SolubiS.”
BackgroundMassive experimental quantification allows interpretable deep learning of protein aggregation
Thompson, M., Martín, M., Olmo, T. S., Rajesh, C., Koo, P. K., Bolognesi, B., & Lehner, B. (2025) · Science Advances · doi:10.1126/sciadv.adt5111 · Cited by 37
“To understand where the methods underperformed, we examined the scores from the highest performing methods [CamSol ( 13 ) and TANGO ( 12 )] and found that non-aggregating sequences with a high-predicted aggregation score had higher hydrophobicity (two-sided t test P value 2 × 10 − 16 ) than all…”
Dubreuil, B., Matalon, O., & Levy, E. D. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.08.008 · Cited by 34
Martin, W. R., & Cheng, F. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1894986 · Cited by 33
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Yang, Y., Zeng, L., & Vihinen, M. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22158027 · Cited by 32
“CamSol uses a residue-specific solubility profile.”
MethodDe novo design of peptides that coassemble into β sheet–based nanofibrils
Xiao, X., Wang, Y., Seroski, D. T., Wong, K. M., Liu, R., Paravastu, A. K., Hudalla, G. A., & Hall, C. K. (2021) · Science Advances · doi:10.1126/sciadv.abf7668 · Cited by 31
“Although there are some algorithms that attempt to answer the latter question by correlating the amyloidogenic tendencies of the individual amino acids ( 8 – 11 ), there is no efficient computational or experimental approach to discover which sequences form which structures.”
A new humanized antibody is effective against pathogenic fungi in vitro
Mambro, T. D., Vanzolini, T., Bruscolini, P., Perez-Gaviro, S., Marra, E., Roscilli, G., Bianchi, M., Fraternale, A., Schiavano, G. F., Canonico, B., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-98659-5 · Cited by 32
Advances in the Prediction of Protein Aggregation Propensity
Pallarès, I., & Ventura, S. (2019) · Current Medicinal Chemistry · doi:10.2174/0929867324666170705121754 · Cited by 33
Aggrescan3D standalone package for structure-based prediction of protein aggregation properties
Kuriata, A., Iglesias, V., Kurciński, M., Ventura, S., & Kmiecik, S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz143 · Cited by 31
Conformational Ensembles of Antibodies Determine Their Hydrophobicity
Waibl, F., Fernández‐Quintero, M. L., Kamenik, A. S., Kraml, J., Hofer, F., Kettenberger, H., Georges, G. J., & Liedl, K. R. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.11.010 · Cited by 32
“This might be one reason for the continued popularity of sequence-based hydrophobicity prediction methods (13,102,103) because it implies that structurebased methods, which should, in principle, outperform their sequence-based counterparts, are significantly limited by the quality of their input structures.”
BackgroundAdvanced computational approaches to understand protein aggregation
Ghosh, D., Biswas, A., & Radhakrishna, M. (2024) · Biophysics Reviews · doi:10.1063/5.0180691 · Cited by 32
Aggidis, A., Devitt, G., Zhang, Y., Chatterjee, S., Townsend, D. J., Fullwood, N. J., Ruiz, E., Tarutani, A., Hasegawa, M., Cooper, A. S., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14246 · Cited by 31
“52 Camsol predicted the intrinsic sol-ubility profile of experimental peptides 53,54 by evaluating amino acid hydrophobicity, aggregation propensity, charge, and secondary structure.”
Arslan, M., & Karadağ, D. (2019) · TURKISH JOURNAL OF BIOLOGY · doi:10.3906/biy-1809-28 · Cited by 30
“, 2014), and Camsol Instrinsic (Sormanni et al., 2015), which determine the aggregation propensity of an antibody based on its sequence.”
BackgroundMethodInfluentialChanges in hydrophobicity mainly promotes the aggregation tendency of ALS associated SOD1 mutants
Tompa, D. R., & Saraboji, K. (2019) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2019.09.181 · Cited by 30
SoluProtMut DB : A manually curated database of protein solubility changes upon mutations
Velecký, J., Hamsikova, M., Štourač, J., Musil, M., Damborský, J., Bednář, D., & Mazurenko, S. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.009 · Cited by 29
“The cornerstones of SoluProtMut are four mutational solubility datasets, published between 2010 and 2017, which we merged together: OptSolMut [7], CamSol [8], A3D [9], and PON-Sol [10].”
BackgroundMethodSoler, M. A., de Marco, A., & Fortuna, S. (2016) · Scientific Reports · doi:10.1038/srep34869 · Cited by 30
Matsui, D., Nakano, S., Dadashipour, M., & Asano, Y. (2017) · Scientific Reports · doi:10.1038/s41598-017-09749-2 · Cited by 30
Xu, X., Xu, T., Zhou, J., Liao, X., Zhang, R., Wang, Y., Zhang, L., & Gao, X. (2023) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.03.004 · Cited by 32
“Successful sequences were obtained and further filtered with CamSol (version 2.2) solubility scores [24] greater than or equal to 0.42, which was the score for Herceptin.”
MethodInfluentialMei, K., Ghazaryan, A., Teoh, E. Z., Summers, H. D., Li, Y., Ballesteros, B., Piasecka, J., Walters, A. A., Hider, R. C., Mailänder, V., et al. (2018) · Advanced Materials · doi:10.1002/adma.201802732 · Cited by 29
Brotzakis, Z. F., Löhr, T., Truong, S. D., Hoff, S., Bonomi, M., & Vendruscolo, M. (2023) · Biochemistry · doi:10.1021/acs.biochem.3c00010 · Cited by 28
Self-citationModulation of α-synuclein in vitro aggregation kinetics by its alternative splice isoforms
Röntgen, A., Toprakcioglu, Z., Tomkins, J. E., & Vendruscolo, M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313465121 · Cited by 32
“We initially used the CamSol method (31, 32) to predict the effect of exon deletions on the solubility and aggregation of α Syn.”
Self-citationComputational re-design of protein structures to improve solubility
Navarro, S., & Ventura, S. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1637413 · Cited by 29
“A3D calculates the structural aggregation propensity score of each residue by computing its intrinsic aggregation propensity, which is afterwards corrected by its exposure to solvent and the impact exerted by the amino acids in the close vicinity, similarly to CamSol [66].”
BackgroundMethodVillegas, J. A., & Levy, E. D. (2022) · Protein Science · doi:10.1002/pro.4361 · Cited by 29
Recent advances in automated protein design and its future challenges
Setiawan, D., Brender, J., & Zhang, Y. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1465922 · Cited by 26
PROTSOLM: Protein Solubility Prediction with Multi-modal Features
Tan, Y., Zheng, J., Liang, H., & Zhong, B. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822310 · Cited by 27
“The first is supervised machine learning or deep learning models, including DeepSoluE [34], ccSOL omics [10], SoluProt [11], SKADE [41], Camsol [26], NetSolP [12], and DSResSOL [17].”
Jean, L., Brimijoin, S., & Vaux, D. J. (2019) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.006230 · Cited by 27
Albaqami, F. F., Altharawi, A., Althurwi, H. N., Alharthy, K. M., Qasim, M., Muhseen, Z. T., & Qamar, M. T. U. (2023) · BioMed Research International · doi:10.1155/2023/5560605 · Cited by 28
“The antigenic and immunogenic epitopes were assessed for solubility and toxicity using CamSol Intrinsic and ToxinPred servers, respectively [28].”
MethodInsights into amyloid-like aggregation of H2 region of the C-terminal domain of nucleophosmin
Russo, A., Diaferia, C., Manna, S. L., Giannini, C., Sibillano, T., Accardo, A., Morelli, G., Novellino, E., & Marasco, D. (2016) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2016.11.006 · Cited by 28
Understanding and predicting protein misfolding and aggregation: Insights from proteomics
Pallarès, I., & Ventura, S. (2016) · PROTEOMICS · doi:10.1002/pmic.201500529 · Cited by 26
Methionine oxidized apolipoprotein A‐I at the crossroads of HDL biogenesis and amyloid formation
Witkowski, A., Chan, G. K. L., Boatz, J. C., Li, N. J., Inoue, A. P., Wong, J. C., van der Wel, P. C. A., & Cavigiolio, G. (2018) · The FASEB Journal · doi:10.1096/fj.201701127r · Cited by 26
Korona, D., Dirnberger, B., Giachello, C. N., Queiroz, R. M. L., Popovic, R., Müller, K. H., Minde, D., Deery, M. J., Johnson, G., Firth, L. C., et al. (2022) · eLife · doi:10.7554/elife.74322 · Cited by 25
Approaches for evaluation of novel CPP-based cargo delivery systems
Porosk, L., & Langel, Ü. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1056467 · Cited by 25
“For secondary structure, additionally to previously mentioned calculators PPC 3.1, there are other predictors also available, such as S2Dv2, or PEP-FOLD 3.5.”
BackgroundInfluentialArras, P., Yoo, H. B., Pekar, L., Clarke, T., Friedrich, L., Schröter, C. J., Schanz, J., Tonillo, J., Siegmund, V., Doerner, A., et al. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1249247 · Cited by 25
“, 2022), replacement of chemically labile and posttranslational modification (PTM) motifs and ideally considering further developability-related aspects (Lauer et al., 2012; Sormanni et al., 2015; Raybould et al., 2019; Ahmed et al., 2021; Khetan et al., 2022; Negron et al., 2022; Evers et al., 2023a; Fernández-Quinte…”
BackgroundIkenoue, T., Aprile, F. A., Sormanni, P., Ruggeri, F. S., Perni, M., Heller, G. T., Haas, C. P., Middel, C., Limbocker, R., Mannini, B., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69626-3 · Cited by 24
Self-citationUsing extensional flow to reveal diverse aggregation landscapes for three IgG1 molecules
Willis, L. F., Kumar, A., Dobson, J., Bond, N. J., Lowe, D., Turner, R., Radford, S. E., Kapur, N., & Brockwell, D. J. (2018) · Biotechnology and Bioengineering · doi:10.1002/bit.26543 · Cited by 24
“The CDR1 of STT is therefore predicted to be markedly more soluble (greater positive CamSol score) than the same region inWFL (Sormanni et al., 2015).”
BackgroundIglesias, V., Santos, J., Santos-Suárez, J., Pintado‐Grima, C., & Ventura, S. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.718301 · Cited by 23
“The set of 69 natural and synthetic prion-like domains described by Ross and coworkers in Saccharomyces cerevisiae was analyzed using AGGRESCAN (Conchillo-Sole et al., 2007) and CamSol Intrinsic (Sormanni et al., 2015) algorithms to evaluate protein aggregation propensities.”
MethodOverath, M. D., Rygaard, A. M., Jacobsen, C. P., Brasas, V., Morell, O., Sormanni, P., & Jenkins, T. P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.14.670059 · Cited by 36
Self-citationWang, J., Chen, S., Yuan, Q., Chen, J., Li, D., Wang, L., & Yang, Y. (2023) · Journal of Computational Chemistry · doi:10.1002/jcc.27249 · Cited by 22
Cryptic amyloidogenic regions in intrinsically disordered proteins: Function and disease association
Santos, J., Pallarès, I., Iglesias, V., & Ventura, S. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.07.019 · Cited by 22
“Protein solubilities were predicted using the CamSol method [54].”
BackgroundMethodNucleation in Protein Aggregation in Biotherapeutic Development: A look into the Heart of the Event
Das, T. K., Chou, D. K., Jiskoot, W., & Arosio, P. (2022) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2022.01.017 · Cited by 22
Amyloid-like Fibrils from an α-Helical Transmembrane Protein
Stroobants, K., Kumita, J. R., Harris, N. J., Chirgadze, D. Y., Dobson, C. M., Booth, P. J., & Vendruscolo, M. (2017) · Biochemistry · doi:10.1021/acs.biochem.7b00157 · Cited by 23
“LacY has a high intrinsic propensity for aggregation In a preliminary assessment, we used the CamSol method(46) to calculate the sequence-dependent solubility of LacY (Figure 1), which is inversely related to the aggregation propensity score (see Material and Methods).”
MethodInfluentialSelf-citationTaricska, N., Horváth, D., Menyhárd, D. K., Ákontz‐Kiss, H., Noji, M., So, M., Goto, Y., Fujiwara, T., & Perczel, A. (2019) · Chemistry - A European Journal · doi:10.1002/chem.201903826 · Cited by 21
Polyphenol‐solubility alters amyloid fibril formation of α‐synuclein
So, M., Kimura, Y., Yamaguchi, K., Sugiki, T., Fujiwara, T., Aguirre, C., Ikenaka, K., Mochizuki, H., Kawata, Y., & Goto, Y. (2021) · Protein Science · doi:10.1002/pro.4130 · Cited by 21
Kinetic Modulation of Amyloid-β (1–42) Aggregation and Toxicity by Structure-Based Rational Design
Im, D., Heo, C. E., Son, M. K., Park, C. R., Kim, H. I., & Choi, J. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.1c10173 · Cited by 21
Development of QSAR models for in silico screening of antibody solubility
Han, X., Shih, J., Lin, Y., Chai, Q., & Cramer, S. M. (2022) · mAbs · doi:10.1080/19420862.2022.2062807 · Cited by 21
Shift of the insoluble content of the proteome in the aging mouse brain
Molzahn, C. M., Kuechler, E. R., Zemlyankina, I., Nierves, L. A., Ali, T., Cole, G., Wang, J., Albu, R. F., Zhu, M., Cashman, N. R., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2310057120 · Cited by 22
“…prediction in TANGO, the grand average hydrophobicity score (GRAVY), RNA - binding likelihood calculated by RBPPRED, protein secondary structure using SCRATCH, molecular recognition features from the MoRF ChiBi System, and protein solubility scores calculated with CamSol (31, 36, 39, 42, 63–69).”
Rawat, S., Valpadashi, A., Jha, M., Sreedurgalakshmi, K., Ghosh, A., Ganguly, D., & Raychaudhuri, S. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.01.022 · Cited by 20
Pashley, C. L., Hewitt, E. W., & Radford, S. E. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.01.009 · Cited by 22
“The critical concentration (Cs) is the protein concentration at which phase separation occurs, thus any protein molecules above this concentration will, eventually, become insoluble [11,25].”
BackgroundInfluentialVendrell, R. C., Ajagekar, A., Bergman, M. T., Hall, C. K., & You, F. (2024) · Science Advances · doi:10.1126/sciadv.adq8492 · Cited by 20
Zika virus capsid anchor forms cytotoxic amyloid-like fibrils
Saumya, K. U., Gadhave, K., Kumar, A., & Giri, R. (2021) · Virology · doi:10.1016/j.virol.2021.04.010 · Cited by 20
Król, S. K., Österlund, N., Vosough, F., Jarvet, J., Wärmländer, S. K. T. S., Barth, A., Ilag, L. L., Magzoub, M. M., Gräslund, A., & Mörman, C. (2021) · iScience · doi:10.1016/j.isci.2021.102852 · Cited by 20
Challenges in describing the conformation and dynamics of proteins with ambiguous behavior
Roca‐Martínez, J., Lázár, T., Gavaldá-García, J., Bickel, D., Pancsa, R., Dixit, B., Tzavella, K., Ramasamy, P., Sanchez-Fornaris, M., Grau, I., et al. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.959956 · Cited by 20
“In addition, other sequence-based predictors provide useful information, such as backbone dynamics (DynaMine) (Cilia et al., 2013, 2014), fuzziness (FuzPred) (Horvath et al., 2020; Miskei et al., 2020), secondary structure [PSIPRED4 (Jones, 1999), SPOT-1D (Singh et al., 2021)], solvent accessibility [SABLE (Adamczak,…”
Stenvang, M., Schafer, N. P., Malmos, K. G., Pérez, A.-M. W., Niembro-Vivanco, O., Sormanni, P., Basaiawmoit, R. V., Christiansen, G., Andreasen, M., & Otzen, D. E. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.03.001 · Cited by 20
Self-citationShahfar, H., Du, Q., Parupudi, A., Shan, L., Esfandiary, R., & Roberts, C. J. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c03669 · Cited by 20
Antibody apparent solubility prediction from sequence by transfer learning
Feng, J., Jiang, M., Shih, J., & Chai, Q. (2022) · iScience · doi:10.1016/j.isci.2022.105173 · Cited by 23
“, 2017)) or from structures (structure-based predictors (Chan et al., 2013; Han et al., 2022; Sormanni et al., 2015)).”
BackgroundWee, J., Chen, J., Xia, K., & Wei, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.107918 · Cited by 20
“Over the years, a variety of computational methods have been developed to explore the ef-3 fects of mutations on protein solubility, including but not limited to CamSol [3], OptSolMut [4], PON-Sol [5], SODA [6], Solubis [7], and others as summarized in a recent review [8].”
MethodSteinkühler, J., Peruzzi, J. A., Krüger, A., Villaseñor, C. G., Jacobs, M. L., Jewett, M. C., & Kamat, N. P. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00357 · Cited by 19
Sahin, C., Kjær, L., Christensen, M. S., Pedersen, J. N., Christiansen, G., Pérez, A.-M. W., Møller, I. M., Enghild, J. J., Pedersen, J. S., Larsen, K., et al. (2018) · Biochemistry · doi:10.1021/acs.biochem.8b00627 · Cited by 19
Sluchanko, N. N., Tugaeva, K. V., Faletrov, Y. V., & Levitsky, D. I. (2015) · Protein Expression and Purification · doi:10.1016/j.pep.2015.11.002 · Cited by 17
Pohl, C., Zalar, M., Bialy, I. E., Indrakumar, S., Peters, G. H., Friess, W., Golovanov, A., Streicher, W., Noergaard, A., & Harris, P. (2020) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.0c00406 · Cited by 13
Xiao, X., Sarma, S., Menegatti, S., Crook, N., Magness, S. T., & Hall, C. K. (2021) · ACS Chemical Biology · doi:10.1021/acschembio.1c00743 · Cited by 15
Random Peptides Rich in Small and Disorder-Promoting Amino Acids Are Less Likely to Be Harmful
Kosinski, L., Aviles, N., Gómez, K., & Joanna Masel, A. (2022) · Genome Biology and Evolution · doi:10.1093/gbe/evac085 · Cited by 18
Nuclear protein quality control in yeast: The latest INQuiries
Kumar, A., Mathew, V., & Stirling, P. C. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102199 · Cited by 23
CAPRIN1P512L causes aberrant protein aggregation and associates with early-onset ataxia
Vedove, A. D., Natarajan, J., Zanni, G., Eckenweiler, M., Muiños-Bühl, A., Storbeck, M., Guillén‐Boixet, J., Barresi, S., Pizzi, S., Hölker, I., et al. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04544-3 · Cited by 18
Simplifying complex antibody engineering using machine learning
Makowski, E. K., Chen, H.-T., & Tessier, P. M. (2023) · Cell Systems · doi:10.1016/j.cels.2023.04.009 · Cited by 19
Zalewski, M., Iglesias, V., Bárcenas, O., Ventura, S., & Kmiecik, S. (2024) · Protein Science · doi:10.1002/pro.5180 · Cited by 18
“CamSol (Sormanni et al., 2015), on the other hand, predicts protein solubility by taking into account factors such as hydrophobicity, charge, and structural context, and it provides users with protein variants engineered for increased solubility.”
BackgroundMethodInfluentialTransient interactions between the fuzzy coat and the cross-β core of brain-derived Aβ42 filaments
Milanesi, M., Brotzakis, Z. F., & Vendruscolo, M. (2025) · Science Advances · doi:10.1126/sciadv.adr7008 · Cited by 18
Self-citationInteraction of selected biomolecules and metabolites with amyloidogenic proteins
Kundu, D., Umesh, & Dubey, V. K. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1760138 · Cited by 18
“, 2014) and Camsol Intrinsic Property Software developed by Vendruscolo Lab, Oxford University (Sormanni et al., 2015).”
MethodInfluentialPhosphorylation-induced changes in the energetic frustration in human Tank binding kinase 1
Husain, S., Kumar, V., & Hassan, M. I. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.04.016 · Cited by 17
The effect of mutation on an aggregation-prone protein: An in vivo, in vitro, and in silico analysis
Guthertz, N., van der Kant, R., Martinez, R. M., Xu, Y., Trinh, C. H., Iorga, B. I., Rousseau, F., Schymkowitz, J. W. H., Brockwell, D. J., & Radford, S. E. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2200468119 · Cited by 17
“corrected CamSol (16) (values > +1 indicate soluble and < 1 indicate insoluble regions [dotted black lines]), and (H) sequence-based Tango (18) (values >5% correspond to an aggregation-prone sequence).”
BackgroundMethodInfluentialKulenkampff, K., Emin, D., Staats, R., Zhang, Y. P., Sakhnini, L. I., Kouli, A., Rimon, O., Lobanova, E., Williams‐Gray, C. H., Aprile, F. A., et al. (2022) · Chemical Science · doi:10.1039/d2sc00066k · Cited by 16
“25,26 The solubility for the binding partners is evaluated by employing a sequence-based solubility calculation with the CamSol method 32 and DesAbs scoring a high solubility score are favored.”
Self-citationLe, L., Lee, J., Im, D., Park, S., Hwang, K.-D., Lee, J., Jiang, Y., Lee, Y.-S., Suh, Y. H., Kim, H. I., et al. (2023) · Advancement of science · doi:10.1002/advs.202302035 · Cited by 13
Dewison, K. M., Rowlinson, B., Machin, J. M., Crossley, J. A., Thacker, D., Wilkinson, M., Ulamec, S. M., Khan, G. N., Ranson, N. A., van Oosten‐Hawle, P., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2315006121 · Cited by 16
“1 B ) (24, 25), we show that deletion of residues 2 to 7 slows the rate of lipid - independent amyloid formation of α Syn in vitro and inhibits its recruitment to α SynWT amyloid fibril seeds.”
BackgroundBanach, M., Konieczny, L., & Roterman, I. (2019) · Molecules · doi:10.3390/molecules24234395 · Cited by 17
“In particular, mutations that reduce solubility seem to promote the formation of fibrillar forms [68].”
BackgroundPandya, A., Zhang, C., Barata, T. S., Brocchini, S., Howard, M. J., Zloh, M., & Dalby, P. A. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.4c00332 · Cited by 16
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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