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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 5.0 (AnalyseComplex command) was used to inspect the possible differences in binding affinity between monomers in the PAH tetramer.”
BackgroundInfluentialChi, Y., Stodola, T. J., de Assuncao, T. M., Leverence, E. N., Smith, B. C., Volkman, B. F., Mathison, A., Lomberk, G. A., Zimmermann, M. T., & Urrutia, R. A. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.04.028 · Cited by 15
“We assessed the stability of the mutated protein by the variant-induced changes in folding energy ( DD G fold ) using FoldX [21] and the pH-dependent mutation energy protocol [22] implemented in the Discovery Studio suite (Dassault Systèmes BIOVIA).”
MethodMolecular Information Theory Meets Protein Folding
Sanchez, I. E., Galpern, E. A., Garibaldi, M. M., & Ferreiro, D. U. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04532 · Cited by 15
The evolution of the HIV-1 protease folding stability
Ferreiro, D., Khalil, R., Gallego, M. J., Osório, N. S., & Arenas, M. (2022) · Virus Evolution · doi:10.1093/ve/veac115 · Cited by 14
“For the specific analysis of PR stability with every observed DRM (or combination of DRMs; Supplementary Table S4), we obtained a PR structural model for every studied DRM (or combination of DRMs) from the WT structure using FoldX (Guerois, Nielsen, and Serrano 2002).”
MethodInfluentialUtsuno, Y., Hamada, K., Hamanaka, K., Miyoshi, K., Tsuchimoto, K., Sunada, S., Itai, T., Sakamoto, M., Tsuchida, N., Uchiyama, Y., et al. (2023) · Journal of Human Genetics · doi:10.1038/s10038-023-01206-5 · Cited by 14
“In addition, free energy changes for the above variants were analyzed using FoldX [20, 21].”
Zheng, F., Jiang, X., Wen, Y., Yang, Y., & Li, M. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.12.018 · Cited by 14
“We performed a comprehensive comparison between our models and eight other state-of-the-art methods used to calculate the binding energy, including PRODIGY [44,45], PPI-Affinity [38], PPA_Pred2 [16], Minpredictor [42], ISLAND [43], FoldX [75], Rosetta [76], and MMPBSA [8,77].”
MethodSharma, P., Pandey, R., Kumari, K., Pandey, G., Jackson, C. J., Russell, R. J., Oakeshott, J. G., & Lal, R. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025128 · Cited by 14
Noguera, M. E., Vazquez, D. S., Ferrer‐Sueta, G., Agudelo, W. A., Howard, E., Rasia, R. M., Manta, B., Cousido-Siah, A., Mitschler, A., PODJARNY, A. D., et al. (2017) · Scientific Reports · doi:10.1038/srep42343 · Cited by 14
Kutlu, A., Venturini, A., Burduroglu, H. C., & Sezerman, O. U. (2018) · Journal of Molecular Modeling · doi:10.1007/s00894-018-3753-1 · Cited by 14
The calmodulin‐binding tetraleucine motif of KCNE4 is responsible for association with Kv1.3
Solé, L., Roig, S. R., Sastre, D., Vallejo-Gracia, A., Serrano-Albarrás, A., Ferrer-Montiel, A. V., Fernandez-Ballester, G. J., Tamkun, M. M., & Felipe, A. (2019) · The FASEB Journal · doi:10.1096/fj.201801164rr · Cited by 14
“The energy of the model and the docking complexes was tested using FoldX (30, 31) on the Centre for Genomic Regulation site (http://foldx.crg.es).”
MethodTu, T., Wang, Z., Luo, Y., Li, Y., Su, X., Wang, Y., Zhang, J., Rouvinen, J., Yao, B., Hakulinen, N., et al. (2021) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.0c06941 · Cited by 14
Nordquist, E. B., English, C. A., Clérico, E. M., Sherman, W., Gierasch, L. M., & Chen, J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009567 · Cited by 14
Chauhan, P. K., & Sowdhamini, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-07553-1 · Cited by 14
Robust and Versatile Host Protein for the Design and Evaluation of Artificial Metal Centers
Fischer, J., Renn, D., Quitterer, F., Radhakrishnan, A., Liu, M., Makki, A. A., Ghorpade, S. A., Rueping, M., Arold, S. T., Groll, M., et al. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b02896 · Cited by 14
Fang, Y., Chao, K., He, J., Wang, Z., & Chen, Z. (2023) · 3 Biotech · doi:10.1007/s13205-023-03557-4 · Cited by 14
“Aiming to increase ICCG thermostability to enable rapid PET depolymerization by whole-cell biocatalysis at high temperatures, we applied three algorithms, FoldX (force field-based energy function) (Guerois et al.”
MethodInfluentialLavanya, P., Ramaiah, S., Singh, H., Bahadur, R., & Anbarasu, A. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.07.028 · Cited by 14
“O interaction to free energy, we used FoldX energy function [22–24] to calculate the free energy of all the PBPs analyzed.”
MethodCarlsson, J., Soussi, T., & Persson, B. (2009) · FEBS Journal · doi:10.1111/j.1742-4658.2009.07124.x · Cited by 14
Cluster conservation as a novel tool for studying protein–protein interactions evolution
Rahat, O., Yitzhaky, A., & Schreiber, G. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21749 · Cited by 14
Bemporad, F., & Ramazzotti, M. (2016) · International review of cytology · doi:10.1016/bs.ircmb.2016.08.008 · Cited by 14
Evaluating the effect of mutations and ligand binding on transthyretin homotetramer dynamics
Saldaño, T. E., Zanotti, G., Parisi, G. D., & Fernández-Alberti, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181019 · Cited by 14
“Key position residues have been analyzed using using FoldX algorithm[53],[54] that predicts differences in the free energy changes ΔΔG = ΔGmutant-ΔGwild-type between the unfolding free energy of a mutant (= ΔGmutant) and wild type (ΔGwild-type) protein.”
MethodEyrisch, S., Medina‐Franco, J. L., & Helms, V. (2011) · Journal of Molecular Modeling · doi:10.1007/s00894-011-1217-y · Cited by 14
Meet Me Halfway: When Genomics Meets Structural Bioinformatics
Gong, S., Worth, C. L., Cheng, T. M. K., & Blundell, T. L. (2011) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-011-9259-1 · Cited by 15
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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