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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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“Structure-based predictors of protein stability mainly attribute to empirical potentials that integrate physical and statistical structure-related energy components (Guerois et al., 2002), and ML techniques (Dehouck et al., 2009; Laimer et al., 2015).”
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Xiong, D., Lee, D., Li, L., Zhao, Q., & Yu, H. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.11.012 · Cited by 40
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De novo DNM1 mutations in two cases of epileptic encephalopathy
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“20,21 For each mutant, we report the average ΔΔ G for the folding of mutant versus wild-type of 20 BuildModel calculations.”
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“8 kcal/mol in absolute terms [39], in FoldX predicted values; see S2 Fig).”
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Chong, L. T., Pitera, J. W., Swope, W. C., & Pande, V. S. (2008) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2008.12.006 · Cited by 37
Epistasis between promoter activity and coding mutations shapes gene evolvability
Cisneros, A. F., Gagnon-Arsenault, I., Dubé, A. K., Després, P. C., Kumar, P., Lafontaine, K., Pelletier, J. N., & Landry, C. R. (2023) · Science Advances · doi:10.1126/sciadv.add9109 · Cited by 33
Gong, J., Jiang, L., Chen, Y., Zhang, Y., Li, X., Ma, Z., Fu, Z., He, F., Sun, P., Ren, Z., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad646 · Cited by 37
“Some of them, like Rosetta (Chen et al. 2020) and FoldX (Guerois et al. 2002), calculate protein thermodynamic stability changes ( DD G ) by simulating the protein folding process.”
BackgroundMethodKanchan, K., Ergülen, E., Király, R., Simon‐Vecsei, Z., Fuxreiter, M., & Fésüs, L. (2013) · Biochemical Journal · doi:10.1042/bj20130696 · Cited by 36
Taberner, F. J., López-Córdoba, A., Fernandez-Ballester, G. J., Korchev, Y. E., & Ferrer-Montiel, A. V. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m114.577478 · Cited by 36
“Such parameters as the atomic contact map, the accessibility of the atoms and residues, the backbone dihedral angles, the hydrogen bonds, and the electrostatic networks of the protein were assessed with FoldX.”
MethodKucukkal, T. G., Yang, Y., Chapman, S. C., Cao, W., & Alexov, E. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15069670 · Cited by 40
“Other resources that also use similar physics-based energy approach to calculate the stability changes upon mutations are the methods and webservers; Eris [124], FOLDEF [136], EGAD [137] and FoldX [33,34].”
MethodSahoo, A., Khare, S., Devanarayanan, S., Jain, P. C., & Varadarajan, R. (2015) · eLife · doi:10.7554/elife.09532 · Cited by 36
“A similar analysis was done using FoldX ( Guerois et al., 2002 ) ( Figure 8—figure supplement 1 ).”
BackgroundMethodInfluentialEvolutionary Conservation of a GPCR-Independent Mechanism of Trimeric G Protein Activation
Coleman, B., Marivin, A., Parag‐Sharma, K., DiGiacomo, V., Kim, S., Pepper, J. S., Casler, J. C., Nguyen, L. T., Koelle, M. R., & Garcia‐Marcos, M. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv336 · Cited by 36
Strokach, A. V., Corbi‐Verge, C., & Kim, P. M. (2019) · Human Mutation · doi:10.1002/humu.23852 · Cited by 39
“The correlations between predicted and experimental values for Provean (G6 ‐ 3), FoldX (G6 ‐ 2), ELASPIC (G6 ‐ 1), Amber TI, Rosetta ’ s ddg_monomer protocol and Rosetta ’ s cartesian_ddg protocol are presented in Figure 1, while the residuals from the lines of best fit are displayed in Figure 2.”
InfluentialFornés, O., Garcı́a-Garcı́a, J., Bonet, J., & Oliva, B. (2014) · Advances in protein chemistry and structural biology · doi:10.1016/b978-0-12-800168-4.00004-4 · Cited by 35
A Genome-wide Ras-Effector Interaction Network
Kiel, C., Foglierini, M., Kuemmerer, N., Beltrão, P., & Serrano, L. G. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.05.015 · Cited by 36
Self-citationLyu, C., Yao, L., Zhu, Q., Mei, J., Cao, Y., Hu, S. C., Zhao, W., Huang, J., Mei, L., Yao, S., et al. (2021) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-021-11328-5 · Cited by 36
Multidimensional epistasis and fitness landscapes in enzyme evolution
Zhang, W., Dourado, D. F. A. R., Fernandes, P. A., Ramos, M. J., & Mannervik, B. (2012) · Biochemical Journal · doi:10.1042/bj20120136 · Cited by 35
Polydorides, S., & Simonson, T. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23450 · Cited by 35
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“The FoldX software program (24) was used to estimate the unfolding free energy and to model the unfolding state.”
MethodThe Mutational Landscape of the Oncogenic MZF1 SCAN Domain in Cancer
Nygaard, M. M., Terkelsen, T., Olsen, A. V., Sora, V., Viloria, J. S., Rizza, F., Bergstrand-Poulsen, S., Marco, M. D., Vistesen, M. V., Tiberti, M., et al. (2016) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2016.00078 · Cited by 31
“The resulting complexes are then ranked by binding energy scores (i.e., Docking energy) calculated using the CHARMM force field (MacKerell et al., 1998).”
BackgroundMethodInfluentialSun, F., Wang, T., Jiang, J., Wang, Y., Ma, Z., Li, Z., Han, Y., Pan, M., Cai, J., Wang, M., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.17228 · Cited by 35
“From the results (Figure 2A), we chose to mutate the residues with more positive dStability values (L41, R51 in VL and I48, R67 in VH), which are, as a result, more unstable than other residues [28, 29].”
BackgroundInterpreting the molecular mechanisms of disease variants in human transmembrane proteins
Tiemann, J. K. S., Zschach, H., Lindorff-Larsen, K., & Stein, A. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2022.12.031 · Cited by 35
Enhancement of Antitumor Properties of rhTRAIL by Affinity Increase toward Its Death Receptors
Reis, C. R., van der Sloot, A. M., Szegezdi, É., Natoni, A., Tur, V., Cool, R. H., Afshin Samali, A., Serrano, L., & Quax, W. J. (2009) · Biochemistry · doi:10.1021/bi801927x · Cited by 35
Self-citationMoal, I. H., & Fernández‐Recio, J. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400295z · Cited by 35
Busch, M. S. A., Lopes, A., Amara, N., Bathelt, C. M., & Simonson, T. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-148 · Cited by 35
A holistic in silico approach to predict functional sites in protein structures
Segura, J., Jones, P. F., & Fernández‐Fuentes, N. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts269 · Cited by 35
Assessment of protein side-chain conformation prediction methods in different residue environments
Peterson, L. X., Kang, X., & Kihara, D. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24552 · Cited by 35
“36– 38 In this work, we benchmarked eight available side-chain conformation prediction programs.”
MethodAn object‐oriented library for computational protein design
Chowdry, A. B., Reynolds, K. A., Hanes, M. S., Voorhies, M., Pokala, N., & Handel, T. M. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20727 · Cited by 35
Rational and mechanistic approaches for improving biocatalyst performance
Phintha, A., & Chaiyen, P. (2022) · Chem Catalysis · doi:10.1016/j.checat.2022.09.026 · Cited by 38
Sun, J., Zhu, T., Cui, Y., & Wu, B. (2025) · The Innovation · doi:10.1016/j.xinn.2024.100750 · Cited by 45
“11 Several studies have successfully leveraged well-established energy functions in models such as Rosetta, 12 FoldX, 13 and ABACUS2 14 todesignthermostableenzymes.”
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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