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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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MethodProtein Function Analysis through Machine Learning
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“Energy calculations( DDG) The effect of in–silico mutation of each CDR residue to ALA is calculated using FoldX.(36) FoldX’s calculations have been previously shown to be correlated to experimentally measured results of 1030 mutants (R D 0.”
BackgroundResultInfluentialAssessment of Solvated Interaction Energy Function for Ranking Antibody–Antigen Binding Affinities
Sulea, T. I., Vivcharuk, V., Corbeil, C. R., Deprez, C., & Purisima, E. O. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00043 · Cited by 29
A Rational Engineering Strategy for Designing Protein A-Binding Camelid Single-Domain Antibodies
Henry, K. A., Sulea, T. I., van Faassen, H., Hussack, G., Purisima, E. O., MacKenzie, C. R., & Arbabi‐Ghahroudi, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0163113 · Cited by 31
“We used three protocols (SIE-SCWRL [43–45], FoldX [46,47] and Rosetta [48,49]) for modeling the structures and evaluating the energies of single-point substitutions of the other 17 naturally-occurring amino acids (Cys and Pro excluded) at each of these 14 positions relative to the wild-type sequence.”
MethodThermodynamics of β-amyloid fibril formation
Tiana, G., Simona, F., Broglia, R. A., & Colombo, G. (2004) · The Journal of Chemical Physics · doi:10.1063/1.1689293 · Cited by 29
Combinatorial approaches to protein stability and structure
Magliery, T. J., & Regan, L. (2004) · European Journal of Biochemistry · doi:10.1111/j.1432-1033.2004.04075.x · Cited by 29
Quantification of helix–helix binding affinities in micelles and lipid bilayers
Lomize, A. L., Pogozheva, I. D., & Mosberg, H. I. (2004) · Protein Science · doi:10.1110/ps.04850804 · Cited by 29
Free energy perturbation calculations of mutation effects on SARS-CoV-2 RBD::ACE2 binding affinity
Sergeeva, A. P., Katsamba, P. S., Liao, J., Sampson, J. M., Bahna, F., Mannepalli, S., Morano, N. C., Shapiro, L., Friesner, R. A., & Honig, B. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168187 · Cited by 27
Molecular Binding Mechanism of TtgR Repressor to Antibiotics and Antimicrobials
Fernández‐Escamilla, A. M., Fernandez-Ballester, G. J., Morel, B., Casares, S., & Ramos, J. L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138469 · Cited by 28
“Mutagenesis and stability energy measurements were performed using FoldX [16, 17] on the CRG site: http:// foldx.crg.es.”
A humanized anti-DLL4 antibody promotes dysfunctional angiogenesis and inhibits breast tumor growth
Jia, X., Wang, W., Xu, Z., Wang, S., Wang, T., Wang, M., & Wu, M. (2016) · Scientific Reports · doi:10.1038/srep27985 · Cited by 29
Kumar, V., Islam, A., Hassan, M. I., & Ahmad, F. (2016) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2016.06.011 · Cited by 29
A long-acting PAI-1 inhibitor reduces thrombus formation
Peng, S., Xue, G., Gong, L., Fang, C., Chen, J., Yuan, C., Chen, Z., Yao, L., Furie, B., & Huang, M. (2017) · Thrombosis and Haemostasis · doi:10.1160/th16-11-0891 · Cited by 28
“The FoldX program (30) was utilised to estimate mutational effects on thermostability of PAI-1:PAItrap.”
MethodFlexible regions govern promiscuous binding of IL ‐24 to receptors IL ‐20R1 and IL ‐22R1
Zahradník, J., Kolářová, L., Peleg, Y., Kolenko, P., Svidenská, S., Charnavets, T., Unger, T., Sussman, J. L., & Schneider, B. (2019) · FEBS Journal · doi:10.1111/febs.14945 · Cited by 26
Computational 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
“CABS-flex is a high-resolution coarse-grained molecular modeling method that has been shown to consistently reproduce the dynamic fluctuations of proteins in the near-native state [106,107].”
MethodNutschel, C., Fulton, A., Zimmermann, O., Schwaneberg, U., Jaeger, K., & Gohlke, H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00954 · Cited by 30
Kumar, M., & Papaleo, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-71527-4 · Cited by 26
Predicting protein stability changes upon mutation using a simple orientational potential
Hernández, I. M., Dehouck, Y., Bastolla, U., López‐Blanco, J. R., & Chacón, P. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad011 · Cited by 29
González, Y., Pons, T., Gil, J., Besada, V., del Rivero, M. A., Tanaka, A. S., Araújo, M. S., & Chávez, M. D. L. A. (2007) · Biological Chemistry · doi:10.1515/bc.2007.129 · Cited by 28
Prediction and redesign of protein–protein interactions
Lua, R. C., Marciano, D. C., Katsonis, P., Adikesavan, A. K., Wilkins, A. D., & Lichtarge, O. (2014) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2014.05.004 · Cited by 29
“…either all-atom molecular dynamics simulations (Moreira et al., 2007; Gilson et al., 1997; Simonson et al., 2002; Swanson et al., 2004) or with simpler models (Guerois et al., 2002; Kortemme and Baker, 2002) and structural analysis (Nussinov and Wolfson, 1991; Tsai et al., 1996; Hu et al., 2000).”
Method
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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