The DOI matches its Crossref record; 94 of the 95 references it cites were found in a registry or an index.
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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“Despite the use of stability data for parameterization, FoldX has been previously applied to predict changes in binding affinity, 59,68 and we observed good performance relative to other scoring functions in a large-scale test of this application.”
BackgroundMethodA neural-network-based method for predicting protein stability changes upon single point mutations
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“Force-field methods such as FoldX provide only modest accuracy in predicting the effects of specific mutations (64, 65), in part because they approximate multibody interactions by sums of pairwise interactions.”
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“While there are several predictors of ΔΔ G upon single site variation, to the best of our knowledge, only Maestro [30] and FoldX [4] allow the predictions of ΔΔ G upon multiple site variants.”
BackgroundQuantification of biases in predictions of protein stability changes upon mutations
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“Experimentally measured ∆∆Gs generally fall within -5 kcal/mol to +5 kcal/mol (13, 46); 381 thus, ThermoNet’s predictions are consistent with the range of observed values.”
BackgroundMethodInfluentialJoachimiak, Ł. A., Kortemme, T., Stoddard, B. L., & Baker, D. A. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2006.05.022 · Cited by 142
Brender, J. R., & Zhang, Y. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004494 · Cited by 140
“However, unlike physics based potentials, the profile scores can be used to score thousands of mutations across a protein-protein interface very quickly (approximately 20 msec per mutation as opposed to an average of 115 seconds, for instance, for building and scoring a full atom complex by FoldX) as once the profiles…”
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“Recently, several computational protein design algorithms have been developed to predict the impact of mutations upon stability to global unfolding, including Rosetta (14), FoldX (15), and SDM (16).”
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Lehmann, K., Seemann, P., Sılan, F., Goecke, T. O., Irgang, S., Kjaer, K. W., Kjaergaard, S., Mahoney, M. J., Morlot, S., Reißner, C., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519697 · Cited by 129
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“For FoldX (37), the predicted absolute sta-”
BackgroundInfluentialHolzinger, D., Jorch, S. K., de Jager, W., Lohse, P., Röhrig, U. F., Gattorno, M., Omenetti, A., Chiesa, S., Schena, F., Austermann, J., et al. (2015) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2015.04.016 · Cited by 129
“The FoldX program was used to perform an in silico alanine scan by mutating each residue to an alanine and estimating the resulting change in folding energy.(30) Molecular visualization was done with CHIMERA.”
MethodCOMPUTER-BASED DESIGN OF NOVEL PROTEIN STRUCTURES
Butterfoss, G. L., & Kuhlman, B. A. (2005) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.35.040405.102046 · Cited by 128
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“To see if the comparison with structure-based energy functions (Rosetta [5], FoldX [4], DFIRE [7], and PoPMuSiC [3]) was affected by structures determined with nuclear magnetic resonance (NMR), we also compiled the S157 (a subset of S236 comprising 157 mutations in 16 proteins) and S405 (a subset of S543 comprising 405…”
BackgroundMethodInfluentialVariation Interpretation Predictors: Principles, Types, Performance, and Choice
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“The energy function-based methods can be grouped into physical potential function, statistical potential function, and empirical potential function methods [Guerois et al., 2002; Capriotti et al., 2004; Parthiban et al., 2006].”
MethodSaitsu, H., Fukai, R., Ben‐Zeev, B., Sakai, Y., Mimaki, M., Okamoto, N., Suzuki, Y., Monden, Y., Saito, H., Tziperman, B., et al. (2015) · European Journal of Human Genetics · doi:10.1038/ejhg.2015.92 · Cited by 124
Koudeláková, T., Chaloupková, R., Brezovský, J., Prokop, Z., Šebestová, E., Hesseler, M., Khabiri, M., Plevaka, M., Kulik, D., Smatanová, I. K., et al. (2013) · Angewandte Chemie International Edition · doi:10.1002/anie.201206708 · Cited by 124
Wu, H., Chen, Q., Zhang, W., & Mu, W. (2021) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2021.1970508 · Cited by 122
Giollo, M., Martín, A. J. M., Walsh, I., Ferrari, C., & Tosatto, S. C. E. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-s4-s7 · Cited by 121
“All terms are linearly combined after fitting to experimental data [12].”
BackgroundOn the evaluation and optimization of protein X‐ray structures for pKa calculations
Nielsen, J. E., & McCammon, J. A. (2003) · Protein Science · doi:10.1110/ps.0229903 · Cited by 121
Self-citationBordner, A. J., & Abagyan, R. A. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20185 · Cited by 120
Identification of hot-spot residues in protein-protein interactions by computational docking
Grosdidier, S., & Fernández‐Recio, J. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-447 · Cited by 123
Miyake, N., Tsukaguchi, H., Koshimizu, E., Shono, A., Matsunaga, S., Shiina, M., Mimura, Y., Imamura, S., Hirose, T., Okudela, K., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.08.013 · Cited by 119
EvoEF2: accurate and fast energy function for computational protein design
Huang, X., Pearce, R., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz740 · Cited by 122
Enhancing the thermostability of Rhizopus chinensis lipase by rational design and MD simulations
Wang, R. Y., Wang, S., Xu, Y., & Yu, X. (2020) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2020.05.243 · Cited by 119
MutaBind estimates and interprets the effects of sequence variants on protein–protein interactions
Li, M., Simonetti, F. L., Goncearenco, A., & Panchenko, A. R. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw374 · Cited by 118
“Although it is not parameterized to predict changes in binding energy, it is very powerful in predicting changes of unfolding free energy (14).”
BackgroundMethodInfluentialTargeting protein–protein interactions, a wide open field for drug design
Bakail, M., & Ochsenbein, F. (2016) · Comptes Rendus. Chimie · doi:10.1016/j.crci.2015.12.004 · Cited by 117
Structural consensus among antibodies defines the antigen binding site
Kunik, V., Peters, B., & Ofran, Y. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002388 · Cited by 117
Cancer Missense Mutations Alter Binding Properties of Proteins and Their Interaction Networks
Nishi, H., Tyagi, M. G., Teng, S., Shoemaker, B. A., Hashimoto, K., Alexov, E. G., Wuchty, S., & Panchenko, A. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066273 · Cited by 117
The scoring of poses in protein-protein docking: current capabilities and future directions
Moal, I. H., Torchala, M., Bates, P. A., & Fernández‐Recio, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-286 · Cited by 119
“The 3D structures of a proteinprotein complex can be used to estimate the effect of mutations [16-21], and thus for protein design [22-29] and determining the functional consequences of mutations associated with diseases (for instance [30]).”
BackgroundChen, C.-W., Lin, M.-H., Liao, C., Chang, H., & Chu, Y.-W. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.02.021 · Cited by 116
Functional Advantages of Conserved Intrinsic Disorder in RNA-Binding Proteins
Váradi, M., Zsolyomi, F., Guharoy, M., & Tompa, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139731 · Cited by 114
“To test this, we calculated the conformational energies of each protein chain in our PDB dataset using the energy scoring function of FoldX [20](Fig 5A).”
MethodInfluentialFrenz, B., Lewis, S. M., King, I., Park, H., DiMaio, F., & Song, Y. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.558247 · Cited by 114
Li, G., Panday, S. K., & Alexov, E. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22020606 · Cited by 114
Macromolecular recognition in the Protein Data Bank
Janin, J., Rodier, F., Chakrabarti, P., & Bahadur, R. P. (2006) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s090744490603575x · Cited by 114
HotSprint: database of computational hot spots in protein interfaces
Güney, E., Tunçbağ, N., Keskin, Ö., & Gürsoy, A. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm813 · Cited by 115
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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