Cited by 1,728Protein Structure and DynamicsRNA and protein synthesis mechanismsEnzyme Structure and Function

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Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations

Guerois, Raphael, Nielsen, Jens Erik, Serrano, Luis·2002·Journal of Molecular Biology

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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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The interpretation of protein structures: Estimation of static accessibility
Lee, B., & Richards, F. M. (1971) · Journal of Molecular Biology · doi:10.1016/0022-2836(71)90324-x · Cited by 5,884
Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models
Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W., et al. (2000) · Accounts of Chemical Research · doi:10.1021/ar000033j · Cited by 4,978
WHAT IF: A molecular modeling and drug design program
Vriend, G. (1990) · Journal of Molecular Graphics · doi:10.1016/0263-7855(90)80070-v · Cited by 3,476
Proteins: Structures and Molecular Properties
Creighton, T. E. (1986) · Medical Entomology and Zoology · Cited by 2,645
Why are ?natively unfolded? proteins unstructured under physiologic conditions?
Uversky, V. N., Gillespie, J. R., & Fink, A. L. (2000) · Proteins Structure Function and Bioinformatics · doi:10.1002/1097-0134(20001115)41:3 · Cited by 2,097
The Solubility of Amino Acids and Two Glycine Peptides in Aqueous Ethanol and Dioxane Solutions
Nozaki, Y., & Tanford, C. (1971) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(19)77210-x · Cited by 1,430
Proteins. Structures and molecular properties
(1984) · Analytical Biochemistry · doi:10.1016/0003-2697(84)90583-9 · Cited by 1,308
A simplified representation of protein conformations for rapid simulation of protein folding
Levitt, M. (1976) · Journal of Molecular Biology · doi:10.1016/0022-2836(76)90004-8 · Cited by 1,123
Biased Probability Monte Carlo Conformational Searches and Electrostatic Calculations for Peptides and Proteins
Abagyan, R. A., & Totrov, M. M. (1994) · Journal of Molecular Biology · doi:10.1006/jmbi.1994.1052 · Cited by 982
The Structure of the Transition State for Folding of Chymotrypsin Inhibitor 2 Analysed by Protein Engineering Methods: Evidence for a Nucleation-condensation Mechanism for Protein Folding
Itzhaki, L. S., Otzen, D. E., & Fersht, A. R. (1995) · Journal of Molecular Biology · doi:10.1006/jmbi.1995.0616 · Cited by 750

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Method
RNA as a key factor in driving or preventing self-assembly of the TAR DNA-binding protein 43
Zacco, E., Graña‐Montes, R., Martin, S. R., de Groot, N. S., Alfano, C., Tartaglia, G. G., & Pastore, A. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.01.028 · Cited by 89
“We used different approaches topredict the effects ofmutations on RNA binding using catRAPID, protein stability through FoldX and protein aggregation with Aggrescan3D. catRAPID uses secondary structure predictions and a potential for the van der Waals and hydrogen bonding contributions.”
MethodInfluential
Computational Library Design for Increasing Haloalkane Dehalogenase Stability
Floor, R. J., Wijma, H. J., Colpa, D. I., Ramos‐Silva, A., Jekel, P. A., Szymański, W., Feringa, B. L., Marrink, ‪. J., & Janssen, D. B. (2014) · ChemBioChem · doi:10.1002/cbic.201402128 · Cited by 89
Potential role of phenotypic mutations in the evolution of protein expression and stability
Goldsmith, M., & Tawfik, D. S. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0809506106 · Cited by 81
Using a structural and logics systems approach to infer bHLH–DNA binding specificity determinants
Masi, F. D., Grove, C. A., Vedenko, A., Alibés, A., Gisselbrecht, S. S., Serrano, L., Bulyk, M. L., & Walhout, A. J. M. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr070 · Cited by 88
Self-citation
Mitochondrial Complex III Deficiency Caused by a HomozygousUQCRC2Mutation Presenting with Neonatal-Onset Recurrent Metabolic Decompensation
Miyake, N., Yano, S., Sakai, C., Hatakeyama, H., Matsushima, Y., Shiina, M., Watanabe, Y., Bartley, J. A., Abdenur, J. E., Wang, R., et al. (2012) · Human Mutation · doi:10.1002/humu.22257 · Cited by 87
Energy functions for protein design I: Efficient and accurate continuum electrostatics and solvation
Pokala, N., & Handel, T. M. (2004) · Protein Science · doi:10.1110/ps.03486104 · Cited by 88
HOXB1 Founder Mutation in Humans Recapitulates the Phenotype of Hoxb1 Mice
Webb, B. D., Shaaban, S., Gaspar, H., Cunha, L. F., Schubert, C. R., Hao, K., Robson, C. D., Chan, W. M., Andrews, C. V., MacKinnon, S. E., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.05.018 · Cited by 88
Structure-based engineering of pH-dependent antibody binding for selective targeting of solid-tumor microenvironment
Sulea, T. I., Rohani, N., Baardsnes, J., Corbeil, C. R., Deprez, C., Donates, Y. C., Robert, A., Schrag, J. D., Parat, M., Duchesne, M., et al. (2019) · mAbs · doi:10.1080/19420862.2019.1682866 · Cited by 87
AUTO-MUTE: web-based tools for predicting stability changes in proteins due to single amino acid replacements
Masso, M., & Vaisman, I. I. (2010) · Protein Engineering Design and Selection · doi:10.1093/protein/gzq042 · Cited by 86
Analyzing Protein Interaction Networks Using Structural Information
Kiel, C., Beltrão, P., & Serrano, L. G. (2008) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.77.062706.133317 · Cited by 85
Self-citation
pH‐selective mutagenesis of protein–protein interfaces: In silico design of therapeutic antibodies with prolonged half‐life
Spassov, V. Z., & Yan, L. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24230 · Cited by 87
Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces
Engin, H. B., Kreisberg, J. F., & Carter, H. K. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152929 · Cited by 84
“As recommended in the FoldX tool’s manual, we discarded the free energy changes smaller than 0.5.”
MethodInfluential
Accurate Stabilities of Laccase Mutants Predicted with a Modified FoldX Protocol
Christensen, N. J., & Kepp, K. P. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300398z · Cited by 81
EvoDesign: Designing Protein–Protein Binding Interactions Using Evolutionary Interface Profiles in Conjunction with an Optimized Physical Energy Function
Pearce, R., Huang, X., Setiawan, D., & Zhang, Y. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.02.028 · Cited by 83
“…were optimized by minimizing the difference between experimental and predicted values for mutation-induced protein monomer stability change The experimental data consisted of 3,989 non-redundant mutation samples from 210 monomeric proteins taken from the FoldX and STRUM datasets [25, 26].”
Method
A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design
Conchúir, S. Ó., Barlow, K. A., Pache, R. A., Ollikainen, N., Kundert, K., O’Meara, M. J., Smith, C. A., & Kortemme, T. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130433 · Cited by 81
“[15]; a set of 2156 mutants collected by Potapov et al.”
Background
iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations
Geng, C., Vangone, A., Folkers, G. E., Xue, L. C., & Bonvin, A. M. J. J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25630 · Cited by 82
“ple energy terms with weights optimized on a set of experimental ΔΔG values.(6) • ZeMu, which can model conformational changes upon mutation”
De novo hotspot variants in CYFIP2 cause early‐onset epileptic encephalopathy
Nakashima, M., Kato, M., Aoto, K., Shiina, M., Belal, H., Mukaida, S., Kumada, S., Sato, A., Zerem, A., Lerman‐Sagie, T., et al. (2018) · Annals of Neurology · doi:10.1002/ana.25208 · Cited by 80
“This prediction was supported by the FoldX calculation of free energy change for each mutation (Fig 4E).(34) Tyr151 in WAVE1 is a highly conserved residue located in the meander regions of WAVE1 and is important for WRC activation.”
Method
mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions
Rodrigues, C. H. M., Pires, D. E. V., & Ascher, D. B. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab273 · Cited by 83
“Overall, our method has proven to be the most robust when compared with FoldX and Discovery Studio, achieving an AUC and MCC of 0.72 and 0.53, respectively, when evaluated on mutations where | G binding | 1 kcal / mol.”
BackgroundInfluential
Evolving a Polymerase for Hydrophobic Base Analogues
Loakes, D., Gallego, J., Pinheiro, V. B., Kool, E. T., & Holliger, P. (2009) · Journal of the American Chemical Society · doi:10.1021/ja9039696 · Cited by 82
Computational and Theoretical Methods for Protein Folding
Compiani, M., & Capriotti, E. (2013) · Biochemistry · doi:10.1021/bi4001529 · Cited by 83
Sequence and Structure Signatures of Cancer Mutation Hotspots in Protein Kinases
Dixit, A., Yi, L., Gowthaman, R., Torkamani, A., Schork, N. J., & Verkhivker, G. M. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007485 · Cited by 83
A Computational Library Design Protocol for Rapid Improvement of Protein Stability: FRESCO
Wijma, H. J., Fürst, M. J. L. J., & Janssen, D. B. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7366-8_5 · Cited by 80
Pathogenicity and functional impact of non-frameshifting insertion/deletion variation in the human genome
Pagel, K. A., Antaki, D., Lian, A., Mort, M. E., Cooper, D. N., Sebat, J. L., Iakoucheva, L. M., Mooney, S. D., & Predrag Radivojac, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007112 · Cited by 82
Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2
Chen, C., Boorla, V. S., Banerjee, D., Chowdhury, R., Cavener, V. S., Nissly, R. H., Gontu, A., Boyle, N. R., Vandegrift, K. J., Nair, M. S., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106480118 · Cited by 81
“Some of them use energies directly from molecular mechanics−based empirical force fields such as FoldX (52) and Rosetta (53, 54) or energies from molecular mechanics– generalized Born surface area (MM-GBSA) analysis of ensembles obtained fromMD simulations (55).”
Method
Predicting changes in protein thermostability brought about by single- or multi-site mutations
Tian, J., Wu, N., Chu, X., & Fan, Y. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-370 · Cited by 80
Investigating the Effects of Mutations on Protein Aggregation in the Cell
Calloni, G., Zoffoli, S., Stefani, M., Dobson, C. M., & Chiti, F. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m412951200 · Cited by 81
PCRPi: Presaging Critical Residues in Protein interfaces, a new computational tool to chart hot spots in protein interfaces
Assi, S. A., Tanaka, T. U., Rabbitts, T. H., & Fernández‐Fuentes, N. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp1158 · Cited by 83
Potential Functions for Hydrogen Bonds in Protein Structure Prediction and Design
Morozov, A. V., & Kortemme, T. (2005) · Advances in protein chemistry · doi:10.1016/s0065-3233(05)72001-5 · Cited by 79
Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation
Berliner, N., Teyra, J., Çolak, R., Lopez, S. G., & Kim, P. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107353 · Cited by 77
“The second factor is that ProMaya relies on a single DDG prediction value from Prethermut, whereas we provide all independent FoldX energy terms as features in combination with molecular and sequence features.”
BackgroundMethodInfluential
Conservative and compensatory evolution in oxidative phosphorylation complexes of angiosperms with highly divergent rates of mitochondrial genome evolution
Havird, J. C., Whitehill, N. S., Snow, C. D., & Sloan, D. B. (2015) · Evolution · doi:10.1111/evo.12808 · Cited by 79
The PINK1 p.I368N mutation affects protein stability and ubiquitin kinase activity
Ando, M., Fiesel, F. C., Hudec, R., Caulfield, T. R., Ogaki, K., Górka-Skoczylas, P., Koziorowski, D., Friedman, A., Chen, L., Dawson, V. L., et al. (2017) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0174-z · Cited by 80
“Prediction of binding energy changes in proteins using FoldX algorithm For each proposed mutation locus, we equilibrated the corresponding flexibility zone in the wild type complex and evaluated the free energy of unfolding (ΔGwt) with the KB potential FoldX [48].”
MethodInfluential
Structure-Function Analyses Point to a Polynucleotide-Accommodating Groove Essential for APOBEC3A Restriction Activities
Bulliard, Y., Narvaiza, I., Bertero, A., Peddi, S., Röhrig, U. F., Ortiz, M., Zoete, V., Castro-Díaz, N., Turelli, P., Telenti, A., et al. (2010) · Journal of Virology · doi:10.1128/jvi.01651-10 · Cited by 78
Computer‐assisted engineering of hyperstable fibroblast growth factor 2
Dvořák, P., Bednář, D., Vaňáček, P., Bálek, L., Eiselleová, L., Štěpánková, V., Šebestová, E., Bosakova, M. K., Konečná, Ž., Mazurenko, S., et al. (2017) · Biotechnology and Bioengineering · doi:10.1002/bit.26531 · Cited by 77

Authors: Guerois, R., Nielsen, J. E., & Serrano, L.
Affiliations: Howard Hughes Medical Institute (US)
European Molecular Biology Laboratory (DE)
ORCID: Nielsen, Jens Erik
Serrano, Luis
Venue: Journal of Molecular Biology
Publisher: Elsevier BV
Published: 2002-07
Volume: 320
Issue: 2
Pages: 369-387
ISSN: 0022-2836 (ISSN-L)
DOI: 10.1016/s0022-2836(02)00442-4
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations

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