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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
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
Flagged first, then by citations
1 of the 62 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 401–494 of 494
Bailey, J., Spina, S. C., Hu, A., Phan, N., Getman, R. B., & Kimmel, B. R. (2026) · ACS Engineering Au · doi:10.1021/acsengineeringau.5c00099 · Cited by 3
NbBayesLM: bayesian prediction of nanobody thermostability using protein language model
Shishir, F. S., Rudro, R., Sarker, B., Zhong, C., & Shomaji, S. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1832968 · Cited by 1
“The unlabeled nanobody corpus was computationally labeled using the CamSol (Sormanni et al., 2015) solubility prediction tool to generate Tm estimates.”
Lima, A. R., Dias, G. G., Davis, S. J., Menezes, G. D. L., Lima, A. F., Costa, M. D. S., Wolf, L. D., Kashiwagi, F. M., Morais, M. A. B., Mikael Bodén, A., et al. (2026) · ACS Synthetic Biology · doi:10.1021/acssynbio.6c00325 · Cited by 1
Artificial intelligence advancements in monoclonal antibody development technology
Ammar, M., Samsonov, M. Y., Gurylina, E., & Bayzigitov, D. R. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1802038 · Cited by 1
Ferdousy, T. J., Miraz, M. M. H., Arian, T. A., Ullah, M. A., Risat, M. T. H., Akter, M. A., Kundu, S., Sarkar, B. K., Sarkar, A. P., Khalipha, A. B. R., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0354891 · Cited by 1
“Usually, in a peptide or protein chain, disulfide bonds are formed between two cystine residues by oxidation of the sulfhydryl (-SH) groups [83].”
InfluentialAPCyc: Property-Informed Design of Cyclic Peptides via Automated Cyclization
Zhao, Y., Qin, L., & Chen, J. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818908 · Cited by 1
Pardo, S. S., Ochoa-Díaz, A. F., & Ortíz, J. E. P. (2021) · Infectio · doi:10.22354/in.v25i4.956 · Cited by 1
Javid, M., Shahverdi, A. R., Ghasemi, A., Moosavi‐Movahedi, A. A., Ebrahim‐Habibi, A., & Sepehrizadeh, Z. (2024) · The Protein Journal · doi:10.1007/s10930-024-10195-z · Cited by 1
“Further structural analysis was performed to visualize the conserved and soluble spots on the surface of the predicted 3D structures using the ConSurf [64] and CamSol [65] servers, respectively.”
Kavousipour, S., Barazesh, M., & Mohammadi, S. (2025) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-025-03429-4 · Cited by 2
“…a linear equation to the input properties [21] CAMSOL A predictor of protein solubility and potential sites to be modified for improvement through sequence and structure-based algorithm [73] SOLart A predictor of protein solubility and aggregation through structure-based algorithm [74] [61].”
Rodríguez-Salazar, C. A., & Recalde-Reyes, D. P. (2021) · Infectio · doi:10.22354/in.v25i4.955 · Cited by 1
p53 TAD2 Domain (38–61) Forms Amyloid-like Aggregates in Isolation
Gadhave, K., Kapuganti, S. K., Mishra, P. M., & Giri, R. (2022) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.1c00860 · Cited by 1
Antibodies engineering by computational approach
Mustafa, M. I. (2022) · Multidisciplinary Reviews · doi:10.31893/multirev.2022012 · Cited by 1
Hebditch, M., & Warwicker, J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/345231 · Cited by 1
“The freely available CamSol (Sormanni et al. , 2015, 2017), and Aggrescan 3D (Zambrano et al. , 2015) servers use sequence and structural information for rational design of mutants with enhanced solubility.”
MethodDesolvation Energy Explains Partitioning of Client Proteins into CondensatesPreprint
Villegas, J. A., & Levy, E. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.16.456554 · Cited by 1
“These were Wimley-White,(Wimley and White, 1996) Kyte-Doolittle,(Kyte and Doolittle, 1982) CamSol,(Sormanni et al., 2015) Roseman,(Roseman, 1988) Janin,(Janin, 1979) and Aggrescan,(Conchillo-Solé et al., 2007).”
Radhakumar, D. S., Thiyagarajan, S., Rajaram, K., & Parsanathan, R. (2023) · Research Square · doi:10.21203/rs.3.rs-3000084/v1 · Cited by 1
Residues 2-7 of α-synuclein regulate amyloid formation via lipid-dependent and -independent pathwaysPreprint
Dewison, K. M., Rowlinson, B., Machin, J. M., Crossley, J., Thacker, D., Wilkinson, M., Ulamec, S. M., Khan, G. N., Ranson, N., van Oosten-Hawle, P., et al. (2024) · bioRxiv · doi:10.1101/2024.05.24.595537
“1B) (38, 39), we show that deletion of residues 2-7 slows the rate of lipid-independent amyloid formation of αSyn in vitro and inhibits its recruitment to αSynWT amyloid fibril seeds.”
BackgroundVendruscolo, M., Brezinova, M., Brotzakis, Z. F., Horne, R. I., Chowdhury, V. R., Gregory, R., & Gentile, F. (2024) · Research Square · doi:10.21203/rs.3.rs-4512167/v1 · Cited by 1
Self-citationJiang, Z., Laosinwattana, S., & Dalby, P. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.24.666527 · Cited by 2
Rawat, S., Valpadashi, A., Jha, M., Sreedurgalakshmi, K., Ghosh, A., Ganguly, D., & Raychaudhuri, S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/312793
Hebditch, M. (2018) · Research Explorer (The University of Manchester)
Boatz, J. C. (2019) · D-Scholarship@Pitt (University of Pittsburgh)
Regulación de la virulencia en Bordetella bronchiseptica por BvgR, una proteína EAL atípica
Gutierrez, M. D. L. P. (2021) · doi:10.35537/10915/116590 · Cited by 1
Ritter, S. C. (2019) · University of Minnesota Digital Conservancy (University of Minnesota)
Hussain, H., McKenzie, E. A., Robinson, A. M., Gingles, N. A., Marston, F. A. O., Warwicker, J., & Dickson, A. J. (2021) · Research Square · doi:10.21203/rs.3.rs-292583/v1
Nash, M. A. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.737739899.793576291
Sequence-based prediction of the solubility of peptides containing non-natural amino acidsPreprint
Oeller, M., Kang, R. J. D., Bolt, H. L., dos Santos, A. G., Weinmann, A. L., Nikitidis, A., Zlatoidský, P., Su, W., Czechtizky, W., Maria, L. D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.03.530952
Self-citationPirmoradi, S. (2022) · Infection Epidemiology and Microbiology · doi:10.52547/iem.8.4.365
Multiplexed Digital Characterisation of Misfolded Protein Oligomers via Solid-State NanoporesPreprint
Sandler, S. E., Horne, R. I., Rocchetti, S., Novak, R. W., Hsu, N., Castellana-Cruz, M., Brotzakis, Z. F., Gregory, R. C., Chia, S., Bernardes, G. J. L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.09.552642
Self-citationTransient interactions between the fuzzy coat and the cross-β core of brain-derived Aβ42 filamentsPreprint
Vendruscolo, M., Milanesi, M., & Brotzakis, Z. F. (2024) · Research Square · doi:10.21203/rs.3.rs-3847495/v1
Self-citationMechanism of phage sensing and abortion by toxin-antitoxin-chaperone systemsPreprint
Mets, T., Kurata, T., Ernits, K., Johansson, M., Craig, S. Z., Evora, G. M., Buttress, J. A., Odai, R., Wallant, K. C. ’., Nakamoto, J. A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.24.581848
Molzahn, C. M., Kuechler, E. R., Nierves, L. A., Cole, G., Wang, J., Cashman, N. R., Karsan, A., Lange, P. F., Gsponer, J. A., & Mayor, T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.28.582597
Ferdousy, T. J., Miraz, M. M. H., Arian, T. A., Ullah, M. A., Risat, M. T. H., Akter, M. A., Kundu, S., Sarkar, B. K., Sarkar, A. P., Khalipha, A. B. R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596168
Ram, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00169-x
Bauer, J., Kube, S., Gupta, P. K., & Kumar, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-62007-2_14
Han, J., Matsumoto, T., Yamada, R., & Ogino, H. (2024) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2024.151237
SOuLMuSiC: A Computational Tool for Predicting the Impact of Mutations on Protein SolubilityPreprint
Attanasio, S., Kwasigroch, J. M., Rooman, M. J., & Pucci, F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.15.633233
Enhancing the Thermostability and solubility of a single-domain catalytic antibody
Cui, Y., Zhou, X., Li, S., Chen, J., Qin, M., An, L., Wang, Y., & Yao, L. (2025) · Protein Engineering Design and Selection · doi:10.1093/protein/gzaf002
Computational investigation of antiviral peptide interactions with Mpox DNA polymerase
Tiwari, H., Ilyas, A., Rai, P. K., Upadhyay, S., & Borkotoky, S. (2025) · In Silico Pharmacology · doi:10.1007/s40203-025-00342-4
“The solubility profiles of the peptides were calculated using the CamSol (Sormanni et al. 2015) The hemolytic activity predictions were carried out with the HLPpred-Fuse (Hasan et al. 2020) ( h t t p : / / t h e g l e e l a b . o r g / H L P p r e d -”
Designed Peptides as Affinity Ligands for Extracellular-Vesicle-based Cancer Biomarker DetectionPreprint
Sarma, S., Cho, Y. K., Tapdiya, A., Park, J., Lee, H., & Hall, C. K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.27.645572
SRPM-Sol: A Structure Robust Protein Multimodal Model for Solubility Prediction
Ge, W., Tan, Y., Yu, H., Fan, G., & Li, M. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3569286
“The first category includes domain-specific supervised machine learning and deep learning models, such as CamSol [61], NetSolP [31], SKADE [62], DeepSoluE [34], and DSResSol [35].”
Karanov, A. A., Zayats, E. A., Kostromina, M. A., Abramchik, Y. A., Sharafutdinova, A. R., Surkova, M. S., Zamyatnin, A. A., & Есипов, Р. С. (2025) · Biochemistry (Moscow) · doi:10.1134/s0006297925600358
“Therefore, we analyzed the potential effects of the sequence and spatial structure of the designed mini-inteins on solubility of the proteins using CamSol 2.0 web server [33].”
InfluentialSardag, I., Düvenci, Z. Ş., & Timuçin, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.02.668245
“The crystal structure and the selected OLF conformations from MD trajectories were analyzed using aggregation propensity prediction tools, namely AggreScan3D [50–54] and CamSol [55, 56].”
Zhang, H., Horne, R. I., Brotzakis, Z. F., Harris, C. B., Lió, P., & Vendruscolo, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.16.670694
“…binding pocket was originally determined using Fpocket, 80 which identifies potential binding pockets based on volume criteria (Figure 1A), and CamSol, which provides a solubility score, 81 and then validated experimentally by showing that small molecules binding to it inhibit secondary nucleation.”
Self-citationAziz, U. B. A., Rehman, A., Mushahid, M., Rehman, F., Khan, N., Yousaf, M. H., Niaz, M. K., Akbar, M. A., Rizwan, M., & Ahmad, S. A. (2025) · Journal of World’s Poultry Science · doi:10.58803/jwps.v4i3.79
Velecký, J., Faldynová, H., Hermosilla, P., Sendlerová, N., Doerr, M., Egersdorfová, S., Bornscheuer, U. T., Damborský, J., Prokop, Z., & Mazurenko, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.26.676459
VHH engineering and utilization for blood-brain barrier transport and anti-infectious diseases
Pérez, J. F., Asano, R., Uto, Y., Tsumoto, K., & Nakakido, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00069-8
Hassan, M., Shahzadi, S., Moustafa, A. A., & Kloczkowski, A. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262211021 · Cited by 1
“…and therapeutic antibody optimization greatly benefit from CamSol’s ability to accurately predict solubility and recommend rational amino acid substitutions that increase solubility without altering the native protein fold, thanks to its dual-sequence and structure-aware methodology [127].”
Chen, Y., Jia, Q., Jia, X., Fu, Z., Sun, P., Li, B., & Ren, Z. (2025) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720025500209
Li, J., & Dong, X. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.23.689994
Zargan, S., Mesdaghinia, S., Jalili, H., Dabirmanesh, B., & Khajeh, K. (2025) · Protein Expression and Purification · doi:10.1016/j.pep.2025.106869
Wu, L., Zhang, C. H., Hu, Z., Wang, X., Liu, H., & Zhu, B. (2025) · Process Biochemistry · doi:10.1016/j.procbio.2025.12.006
Amyloid-mediated RNA uptake by sperm for embryonic delivery
Mallen, W., Zabello, N., Brenner‐Morton, S., Sidoli, S., Liang, D. F., Sall, J., Yin, J., Mathews, P., Zhu, M., Zhang, G., et al. (2026) · Molecular Cell · doi:10.64898/2025.12.10.693493
Lin, J., Zhang, Y. P., Chia, S., Klenerman, D., Sormanni, P., & Vendruscolo, M. (2025) · Chemical Science · doi:10.1039/d5sc01427a
“23,49 In total, four monobody variants with the original monobody sca ff old were generated: FETLTLR(BC)-wt(FG), GSFETLTLREEE(BC)-wt(FG), wt(BC)- FETLTLR(FG) and wt(BC)-GSFETLTLREEE(FG) (Schemes S2 – S5).”
Self-citationKhan, M. A. A., Ami, J. Q., Faisal, K., Chowdhury, R., Ghosh, P., Hossain, F., Wahed, A. A. E., & Mondal, D. (2020) · Figshare · doi:10.6084/m9.figshare.12134892
Khan, M. A. A., Ami, J. Q., Faisal, K., Chowdhury, R., Ghosh, P., Hossain, F., Wahed, A. A. E., & Mondal, D. (2020) · Figshare · doi:10.6084/m9.figshare.12134853
Generative design of antibody Fc-variants with synthetic and programmable functional profilesPreprint
Irvine, E. B., Bikias, T., Stamkopoulos, E., Frei, L., Schürmann, N., Warrender, A. K., Schmid, H., Coukos, D., Yang, H., Minot, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.10.681689
“6 ), with an orthogonal developability score from CamSol – a publicly available solubility predictor (Sormanni et al., 2015).”
InfluentialSardag, I., Düvenci, Z. Ş., & Timuçin, E. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02828
K, Y. K. Y. (2026) · Research Square · doi:10.21203/rs.3.rs-8576121/v1
Peña‐Díaz, S., Zhang, Z., Mittal, C., Jiang, Y., Ferreira, P., Hernández, M. L., Daugberg, A. O. H., Dueholm, M. S., Ramos, M. J., Pedersen, J. S., et al. (2026) · Elsevier eBooks · doi:10.1016/b978-0-323-99507-8.00052-4
Dabiri, M., Levarski, Z., Struhárňanská, E., Demko, V., Beneš, V., Turňa, J., & Stuchlík, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-38805-z
“…chosen based on RMSF, SASA, H-bond analyses, and the Ramachandran plot, they were all run through CamSol (https://www-cohsoftware.ch.cam.ac.uk/) 102 , a server that calculates an intrinsic solubility profile and solubility gain upon mutagenesis, and Protein-Sol…”
Núñez-Muñoz, L. A., Calderón-Pérez, B., Ruíz‐Medrano, R., Xoconostle‐Cázares, B., & Torre-Almaráz, R. D. L. (2026) · Molecular Biotechnology · doi:10.1007/s12033-026-01556-3
Deng, J., Huang, Q., Lv, J., Yang, Y. L., Ye, Z., Chu, Y., Wu, Y., Zhao, Q., Fang, W., & Ouyang, D. (2026) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2026.126703
Fine-Tuning α-Synuclein Phase Separation through Sequence-Optimized Peptide ModulatorsPreprint
Ikenoue, T., Konuma, T., Ikegami, T., & Suga, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.21.707152
“Theoretical scores of water solubility of linear peptides were determined using the CamSol method 39 .”
Designed peptides as affinity ligands for extracellular-vesicle-based cancer diagnosis
Sarma, S., Park, J., Cho, Y. K., Tapdiya, A., Woo, H.-K., Lee, H., & Hall, C. K. (2026) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2026.118584
Njemoga, S., Majerová, P., Piešťanský, J., Gažová, Z., Kadeřávek, P., Barrera, E., Bednáriková, Z., & Cehlár, O. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.152066 · Cited by 1
Rahimzadeh, F., Khanli, L. M., Salehpour, P., & Golabi, F. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132559 · Cited by 1
Explicit representation of germline and non-germline residues improves antibody language modelingPreprint
Kim, J., Blalock, N., Kulkarni, A., Nakamura, K., & Romero, P. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.06.723387 · Cited by 1
Protein solubility depends on centrifugation: Aiki-Sol, a per-regime predictor for E. coliPreprint
Rajagopalan, R., Meda, R. S., Shastry, S., & Mysore, V. P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.14.725067
“…[6, 5]; 32 , 000 × g additionally clears aggregated species; 100 , 000 × g pellets ribosomes and large soluble multimers in addition to aggregates [7]; and the cell-free eSol/PURE system at 21 , 600 × g [4] is likely correlated with chaperone-independent intrinsic aggregation propensity [23].”
Multi-objective antibody design and optimization using machine learning
Kuo, Y., Brown, C. N., Akin, E., Xie, Y., & Tessier, P. M. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-026-00444-4
“Finally, in a process akin to in vivo clonal selection, the predicted antibody variants were filtered for properties such as net charge and hydrophobicity index to account for viscosity and in vivo clearance, as well as for solubility 38 and immunogenicity 45 .”
InfluentialDiscriminator‐Guided Inverse Folding for Multi‐Property Protein Design
Zheng, Y., Liu, C., Liu, Z., Su, M., Tang, C., Zheng, X., Zhang, H., & Li, J. (2026) · Advanced Science · doi:10.1002/advs.75988
“Besides, their solubility is essential for the efficient production via recombinant expression systems [ 10, 17, 18 ].”
Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation
Ren, Y., Wee, J., Chen, X., Qian, G., & Wei, G. (2026) · Protein Science · doi:10.1002/pro.70700
“…in the prediction of mutation-induced solubility changes have yielded tools such as CamSol (Sormanni et al., 2015), SODA (Paladin et al., 2017), Solubis (Van Durme et al., 2016), PON-Sol (Yang et al., 2016), and PON-Sol2 (Yang et al., 2021), which utilize gradient boosting on expanded datasets.”
Microbial Synthesis of Daphnetin by Engineering 2-Oxoglutarate-Dependent Dioxygenases
Ji, S., Xie, C., Wang, Y., Sun, M., Dai, J., Du, Y., Shen, X., Wang, J., Yin, Q., Sun, X., et al. (2026) · ACS Synthetic Biology · doi:10.1021/acssynbio.6c00300
Pishgahzadeh, E., Mafakher, L., Dabirian, S., Vahidi, H., Kazemi‐Lomedasht, F., Jamshidi, H., & Mohit, E. (2026) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2026.104432
Hamada, H., Yiqi, Y., Zendo, T., & Hanai, T. (2026) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2026.109543
MiLaSol: modeling protein solubility by mixing up multiple protein language models
Lou, W., Erden, M., & Cowen, L. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag233
“Early computational methods to predict protein solubility directly from amino acid sequence relied on hand-crafted features that come from known physicochemical properties (Agostini et al. 2012, Smialowski et al. 2012, Sormanni et al. 2015, Bhandari et al. 2020).”
Computational navigation of constrained multidimensional protein fitness landscapes
Kaushik, R., Piroozmand, F., & Re, S. (2026) · FEBS Letters · doi:10.1002/1873-3468.70442 · Cited by 1
Montefusco-Pereira, C. V. (2026) · Entropic and Disordered Matter · doi:10.3390/edm1010003
“Sequence-based or structure-based scores can support early ranking, while automated formulation experiments can test how pH and excipients change the result [21,30].”
Manyumwa, C. V., Jers, C., & Mijakovic̀, I. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27167498
Szataniak, I., & Packi, K. (2026) · Antibodies · doi:10.3390/antib15050081
“The application of artificial intelligence has therefore transformed affinity maturation into a predictive optimization process in which computational models guide experimental design [83–86].”
InfluentialFeng, K., Parkinson, J., Hard, R. L., Wang, D., Zhao, Z., Yu, Y., Zhou, Z., Wang, R., Zhang, J. A., Zhang, E., et al. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.6c11220
Joint optimisation of amino acid and coding sequence for de novo designed proteinsPreprint
Ali, A. A. M., Delhibabu, R., & Nelson, E. J. R. (2026) · Research Square · doi:10.21203/rs.3.rs-10942775/v1
Li, N., Liu, X., Li, Q., Yue, C., Li, X., Huang, Y., Deng, X., Zhang, J., Liang, X., & Ye, G. (2026) · Advanced Intelligent Discovery · doi:10.1002/aidi.70159
TopoFormer : a topological-geometric neural network for accurate protein solubility prediction
Kumar, S., Harman, O., Song, S., Kim, Y., Paton, R. S., & Kim, S. (2026) · Digital Discovery · doi:10.1039/d6dd00219f
Vicente‐Pazos, M., López‐Siles, M., & McConnell, M. J. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0232
“2 server ( https://www-vendrus-colo.ch.cam.ac.uk/camsolmethod.html ) [ 109 ] for a deeper solubility analysis (per residue) and also taking into account generic aggregation propensity (pH value was set at the predicted theoretical pI).”
Overath, 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) · Science Advances · doi:10.1126/sciadv.aef5325 · Cited by 2
“We used AF3-generated complexes as starting points to guide targeted optimization of affinity and solubility with a structure-informed language model ( 38 ) and the CamSol algorithm ( 39 ).”
InfluentialMao, S., Meng, S., & Xiang, Y. (2026) · iScience · doi:10.1016/j.isci.2026.117693
Niazi, S. K., Magoola, M., & Mariam, Z. (2026) · npj Drug Discovery. · doi:10.1038/s44386-026-00084-z
“Integrating these methods with structural dynamics simulations offers deeper insights into the thermodynamic (enthalpy and entropy contributions) and kinetic (on-rates and off-rates) factors governing antibody-antigen interactions, enabling more advanced optimization strategies 71 .”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
