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Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations
Abstract
We have developed a computer algorithm, FOLDEF (for FOLD-X energy function), to provide a fast and quantitative estimation of the importance of the interactions contributing to the stability of proteins and protein complexes. The predictive power of FOLDEF was tested on a very large set of point mutants (1088 mutants) spanning most of the structural environments found in proteins. FOLDEF uses a full atomic description of the structure of the proteins. The different energy terms taken into account in FOLDEF have been weighted using empirical data obtained from protein engineering experiments. First, we considered a training database of 339 mutants in nine different proteins and optimised the set of parameters and weighting factors that best accounted for the changes in stability of the mutants. The predictive power of the method was then tested using a blind test mutant database of 667 mutants, as well as a database of 82 protein-protein complex mutants. The global correlation obtained for 95 % of the entire mutant database (1030 mutants) is 0.83 with a standard deviation of 0.81 kcal mol(-1) and a slope of 0.76. The present energy function uses a minimum of computational resources and can therefore easily be used in protein design algorithms, and in the field of protein structure and folding pathways prediction where one requires a fast and accurate energy function. FOLDEF is available via a web-interface at http://fold-x.embl-heidelberg.de
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“They usually take a form of linear functions, often with only energy‐based terms, and fail to exploit evolution information, which can limit their ability to capture mutation effects on binding affinity.”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [6]
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“FoldX [46] is a purely physics-based method that uses empirical energy functions to predict G b as described in the FOLDEF paper [10].”
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MethodResultInfluentialPredicting antibody affinity changes upon mutations by combining multiple predictors
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Knowledge-Based Energy Functions for Computational Studies of Proteins
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“The prediction decision on whether the mutation stabilises or destabilises the structure is based upon [16,48] where; no effect on structural stability: ΔΔG is< 1.”
BackgroundMethodInfluentialEngineering Proteins for Thermostability with iRDP Web Server
Panigrahi, P., Sule, M., Ghanate, A. D., Ramasamy, S., & Suresh, C. G. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139486 · Cited by 23
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Expanding the watch list for potential Ebola virus antibody escape mutations
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“[21, 22] As with our previous study, we initially minimize each snapshot six times in succession using the RepairPDB command to obtain convergence of the potential energy.”
MethodSurvey of Protein Engineering Strategies
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“Differences between wild-type and mutant proteins in terms of the free energy of binding can be determined by experimental mutagenesis studies (39) and also by various computational methods, albeit with a relatively lower level of confidence (20,40,41).”
BackgroundPezzullo, M., Vecchio, P. D., Mandrich, L., Nucci, R., Rossi, M., & Manco, G. (2012) · Protein Engineering Design and Selection · doi:10.1093/protein/gzs066 · Cited by 23
Xu, J., & Zhang, J. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu130 · Cited by 30
“FoldX (Guerois et al. 2002) was used to optimize the neighboring residue side chain orientation in a protein structure upon the replacement of neighboring residues.”
MethodComputational Identification of Antigen-Binding Antibody Fragments
Burkovitz, A., Leiderman, O., Sela‐Culang, I., Byk, G., & Ofran, Y. (2013) · The Journal of Immunology · doi:10.4049/jimmunol.1200757 · Cited by 21
Robust prediction of mutation-induced protein stability change by property encoding of amino acids
Kang, S., Chen, G., & Guosheng, X. I. A. O. (2008) · Protein Engineering Design and Selection · doi:10.1093/protein/gzn063 · Cited by 23
O'Brien, C. J., Blanco, M. A., Costanzo, J. A., Enterline, M., Fernández, E., Robinson, A. S., & Roberts, C. J. (2016) · Protein Engineering Design and Selection · doi:10.1093/protein/gzw010 · Cited by 25
“…2013); and applying physics- and chemistry-based approaches that rely on models of the underlying molecular interactions (Kuhlman and Baker, 2000; Guerois et al., 2002; Chiti et al., 2003; DuBay et al., 2004; Fernandez-Escamilla et al., 2004; Jordan et al., 2009; Michaelson et al., 2009; Sahin…”
MethodAnsar, M., Paracha, S. A., Serretti, A., Sarwar, M. T., Khan, J., Ranza, E., Falconnet, E., Iwaszkiewicz, J., Shah, S. F., Qaisar, A. A., et al. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy406 · Cited by 23
COVID-19: The Effect of Host Genetic Variations on Host–Virus Interactions
Chakravarty, S. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00637 · Cited by 23
Rahman, A., Herfst, C. A., Jalali, B. M., Shames, S. R., Chau, L. A., Bueno, C., Madrenas, J., Sundberg, E. J., & McCormick, J. K. (2006) · The Journal of Immunology · doi:10.4049/jimmunol.177.12.8595 · Cited by 23
Biswas, R., Gao, S., Cultraro, C. M., Maity, T. K., Venugopalan, A. K., Abdullaev, Z., Shaytan, A. К., Carter, C. A., Thomas, A., Rajan, A., et al. (2016) · Molecular Case Studies · doi:10.1101/mcs.a001263 · Cited by 22
“FoldX4 program (Guerois et al. 2002) was used to repair the initial structure, generate the structure of the point mutant, and estimate changes in free energy upon mutation (ΔΔG).”
Wang, 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
Stability Effects of Protein Mutations: The Role of Long-Range Contacts
Bigman, L. S., & Levy, Y. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b07379 · Cited by 22
A strategy for proline and glycine mutations to proteins with alchemical free energy calculations
Hayes, R. L., & Brooks, C. L. (2021) · Journal of Computational Chemistry · doi:10.1002/jcc.26525 · Cited by 23
Zheng, F., Liu, Y., Yang, Y. J., Wen, Y., & Li, M. (2023) · Protein Science · doi:10.1002/pro.4861 · Cited by 23
“The reason for this difference lies in the fact that the reverse mutations for the S594 dataset were generated using FoldX, which primarily focuses on optimizing the surrounding side chains near the mutation site during the creation of mutant structures.”
BackgroundMethodInfluentialNantasenamat, C., Prachayasittikul, V., & Bülow, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062996 · Cited by 22
Enzymatic Properties and Mutational Studies of Chalcone Synthase from Physcomitrella patens
Rahman, R. N. Z. R. A., Zakaria, I. I., Salleh, A. B., & Basri, M. (2012) · International Journal of Molecular Sciences · doi:10.3390/ijms13089673 · Cited by 22
Crucial importance of the water-entropy effect in predicting hot spots in protein–protein complexes
Oshima, H., Yasuda, S., Yoshidome, T., Ikeguchi, M., & Kinoshita, M. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21597c · Cited by 21
Targeting AML through DR4 with a novel variant of rhTRAIL
Szegezdi, É., Reis, C. R., van der Sloot, A. M., Natoni, A., O’Reilly, A., Reeve, J. L., Cool, R. H., O'Dwyer, M. E., Knapper, S., Serrano, L., et al. (2010) · Journal of Cellular and Molecular Medicine · doi:10.1111/j.1582-4934.2010.01211.x · Cited by 22
Self-citationHuang, J., Jones, B. J., & Kazlauskas, R. J. (2015) · Biochemistry · doi:10.1021/acs.biochem.5b00333 · Cited by 22
BetaSerpentine: a bioinformatics tool for reconstruction of amyloid structures
Bondarev, S. A., Bondareva, O., Zhouravleva, G. A., & Kajava, A. V. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx629 · Cited by 24
Structural effects of point mutations in proteins
Shanthirabalan, S., Chomilier, J., & Carpentier, M. (2018) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25499 · Cited by 24
Wei, W., Behloul, N., Baha, S., Liu, Z., Aslam, M. S., & Meng, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-20137-2 · Cited by 22
Metadata
- Authors
- Guerois, R., Nielsen, J. E., & Serrano, L.
- Affiliations
- Howard Hughes Medical Institute (US)
- European Molecular Biology Laboratory (DE)
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2002-07
- Volume
- 320
- Issue
- 2
- Pages
- 369-387
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W2092387745
- Cited by
- 1,728
- References
- 95
- Citations by year
- 2026: 68 · 2025: 85 · 2024: 61 · 2023: 78 · 2022: 74 · 2021: 90 · 2020: 98 · 2019: 82 · 2018: 90 · 2017: 84 · 2016: 94 · 2015: 93 · 2014: 90 · 2013: 102 · 2012: 87
- FWCI
- 12.09
- 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 › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein stability prediction, point mutations, protein-protein complex, parameter optimization, protein design, protein folding pathways
- MeSH terms
- Algorithms, Hydrogen Bonding, Models, Genetic, Mutation, Protein Conformation, Proteins, Software, Thermodynamics, Water, Models, Statistical, Point Mutation, Protein Structure, Secondary, Protein Folding, Databases as Topic
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