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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BackgroundMethod
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“FOLDEF [23] uses atomic descriptors of protein structures and various energy terms weighted based on empirical data as obtained from experimental data.”
Method
Molecular dynamics simulation of the interactions between EHD1 EH domain and multiple peptides
Yu, H., Wang, M.-J., Xuan, N.-X., Shang, Z.-C., & Wu, J. (2015) · Journal of Zhejiang University SCIENCE B · doi:10.1631/jzus.b1500106 · Cited by 19
“As the entropy contribution cannot be decomposed in the energy decomposition analysis with AMBER and the hydrogen bond energy cannot be evaluated separately by MM/GBSA, alanine scanning with FoldX was conducted as a supplement of quantitative analysis.”
MethodInfluential
X-ray crystallographic validation of structure predictions used in computational design for protein stabilization
Floor, R. J., Wijma, H. J., Jekel, P. A., van Scheltinga, A. C. T., Dijkstra, B. W., & Janssen, D. B. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24791 · Cited by 19
Phosphorylation Regulates the Bound Structure of an Intrinsically Disordered Protein: The p53-TAZ2 Case
Ithuralde, R. E., & Turjanski, A. G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0144284 · Cited by 19
Combining ancestral sequence reconstruction with protein design to identify an interface hotspot in a key metabolic enzyme complex
Holinski, A., Heyn, K., Merkl, R., & Sterner, R. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25225 · Cited by 19
Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols
Li, M., Goncearenco, A., & Panchenko, A. R. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6747-6_17 · Cited by 19
Functional characterization of the first missense variant in CEP78 , a founder allele associated with cone‐rod dystrophy, hearing loss, and reduced male fertility
Ascari, G., Peelman, F., Farinelli, P., Rosseel, T., Lambrechts, N., Wunderlich, K. A., Wagner, M., Nikopoulos, K., Martens, P. C., Balikova, I., et al. (2020) · Human Mutation · doi:10.1002/humu.23993 · Cited by 20
“We used FoldX to predict the effect of p.(Leu150Ser) on protein stability in the three models (Guerois et al., 2002).”
Method
High-throughput enrichment of temperature-sensitive argininosuccinate synthetase for two-stage citrulline production in E. coli
Schramm, T., Lempp, M., Beuter, D., Sierra, S. G., Glatter, T., & Link, H. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.03.004 · Cited by 19
Rosetta custom score functions accurately predict ΔΔ G of mutations at protein–protein interfaces using machine learning
Shringari, S. R., Giannakoulias, S. G., Ferrie, J. J., & Petersson, E. J. (2020) · Chemical Communications · doi:10.1039/d0cc01959c · Cited by 22
Cohesin-dockerin code in cellulosomal dual binding modes and its allosteric regulation by proline isomerization
Vera, A. M., Galera‐Prat, A., Wojciechowski, M., Różycki, B., Laurents, D. V., Carrión‐Vázquez, M., Cieplak, M., & Tinnefeld, P. (2021) · Structure · doi:10.1016/j.str.2021.01.006 · Cited by 19
Accurate calculation of mutational effects on the thermodynamics of inhibitor binding to p38α MAP kinase: a combined computational and experimental study
Zhu, S., Travis, S., & Elcock, A. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400104x · Cited by 21
Assessing Energetic Contributions to Binding from a Disordered Region in a Protein−Protein Interaction,
Cho, S., Swaminathan, C. P., Bonsor, D. A., Kerzic, M. C., Guan, R., Yang, J., Kieke, M. C., Andersen, P. S., Kranz, D. M., Mariuzza, R. A., et al. (2010) · Biochemistry · doi:10.1021/bi1008968 · Cited by 19
The CAP1/Prss8 catalytic triad is not involved in PAR2 activation and protease nexin‐1 (PN‐1) inhibition
Crisante, G., Battista, L., Iwaszkiewicz, J., Nesca, V., Mérillat, A., Sergi, C., Zoete, V., Frateschi, S., & Hümmler, E. (2014) · The FASEB Journal · doi:10.1096/fj.14-253781 · Cited by 19
“The residues contributing the most to the interaction and located in the center of the interaction interface, were selected for mutagenesis, and their impact on protein stability was assessed by computational alanine scanning with the FoldX program (45).”
Method
The spectrum of factor XI deficiency in Italy
Castaman, G., Giacomelli, S. H., Caccia, S., Riccardi, F., Rossetti, G., Dragani, A., Giuffrida, A. C., Biasoli, C., & Duga, S. (2013) · Haemophilia · doi:10.1111/hae.12257 · Cited by 19
Knowledge acquisition and development of accurate rules for predicting protein stability changes
Huang, L., Gromiha, M. M., Hwang, S., & Ho, S. (2006) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2006.06.004 · Cited by 19
Computational alanine scanning with linear scaling semiempirical quantum mechanical methods
Diller, D., Humblet, C. C., Zhang, X., & Westerhoff, L. M. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22745 · Cited by 19
NMR Structural Characterization and Computational Predictions of the Major Intermediate in Oxidative Folding of Leech Carboxypeptidase Inhibitor
Arolas, J. L., D’Silva, L., Popowicz, G. M., Avilés, F. X., Holak, T. A., & Ventura, S. (2005) · Structure · doi:10.1016/j.str.2005.05.008 · Cited by 19
Chronic non-spherocytic hemolytic anemia associated with severe neurological disease due to -glutamylcysteine synthetase deficiency in a patient of Moroccan origin
Pereira, M. M., Gelbart, T., Ristoff, E., Crain, K., Bergua, J., Lafuente, A. L., Kalko, S. G., Mateos, E., Beutler, E., & Corrons, J. V. (2007) · Haematologica · doi:10.3324/haematol.11238 · Cited by 19
Studying the Folding Process of the Acylphosphatase from Sulfolobus solfataricus. A Comparative Analysis with Other Proteins from the Same Superfamily
Bemporad, F., Capanni, C., Calamai, M., Tutino, M. L., Stefani, M., & Chiti, F. (2004) · Biochemistry · doi:10.1021/bi030238a · Cited by 19
An Alternative Domain Containing a Leucine-rich Sequence Regulates Nuclear Cytoplasmic Localization of Protein 4.1R
Luque, C. M., Pérez-Ferreiro, C. M., Pérez-González, A., Englmeier, L., Koffa, M. D., & Correas, I. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.m201521200 · Cited by 19
β-sheet Engineering of IsPETase for PET Depolymerization
Gao, S., Shi, L., Wei, H., Liu, P., Zhao, W., Gong, L., Tan, Z., Zhai, H., Liu, W., Liu, H., et al. (2024) · Engineering · doi:10.1016/j.eng.2024.10.015 · Cited by 18
Algorithms for optimizing cross-overs in DNA shuffling
He, L., Friedman, A. M., & Bailey‐Kellogg, C. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s3-s3 · Cited by 19
Discovery and optimization of a novel anti-GUCY2c x CD3 bispecific antibody for the treatment of solid tumors
Root, A. R., Guntas, G., Katragadda, M., Apgar, J. R., Narula, J., Chang, C. S., Hanscom, S. R., McKenna, M., Wade, J. M., Meade, C., et al. (2021) · mAbs · doi:10.1080/19420862.2020.1850395 · Cited by 18
Robust principal component analysis‐based prediction of protein‐protein interaction hot spots
Sitani, D., Giorgetti, A., Alfonso‐Prieto, M., & Carloni, P. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26047 · Cited by 18
Stability Prediction for Mutations in the Cytosolic Domains of Cystic Fibrosis Transmembrane Conductance Regulator
Bahia, M. S., Khazanov, N., Zhou, Q., Yang, Z. W., Wang, C. C., Hong, J. S., Rab, A., Sorscher, E. J., Brouillette, C. G., Hunt, J. F., et al. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01207 · Cited by 19
Research Progress of Pectin Methylesterase and its Inhibitors
Guo, X., Chang, S., Hu, J., Wang, Y., Zhang, D., Huang, L., Zhang, Z., Gao, J., Liu, W., & He, G. (2022) · Current Protein and Peptide Science · doi:10.2174/1389203723666220919092428 · Cited by 18
Understanding molecular mechanisms and predicting phenotypic effects of pathogenic tubulin mutations
Attard, T., Welburn, J. P. I., & Marsh, J. A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010611 · Cited by 18
“Structural predictions were then obtained from FoldX (60) for pathogenic and gnomAD variants.”
MethodInfluential
A semi-supervised boosting SVM for predicting hot spots at protein-protein Interfaces
Xu, B., Wei, X., Deng, L., Guan, J., & Zhou, S. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-s2-s6 · Cited by 18
Optimizing energy functions for protein–protein interface design
Sharabi, O., Yanover, C., Dekel, A., & Shifman, J. M. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21594 · Cited by 18
Interaction Dynamics Determine Signaling and Output Pathway Responses
Stojanovski, K., Ferrar, T. S., Benisty, H., Uschner, F., Delgado, J., Jiménez, J., Solé, C., de Nadal, E., Klipp, E., Posas, F., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.03.029 · Cited by 18
Self-citation
De novo variants in KLF7 are a potential novel cause of developmental delay/intellectual disability, neuromuscular and psychiatric symptoms
Powis, Z., Petrik, I., Cohen, J. S., Escolar, D. M., Burton, J. E., van Ravenswaaij‐Arts, C. M. A., Sival, D. A., Stegmann, A. P. A., Kleefstra, T., Pfundt, R., et al. (2017) · Clinical Genetics · doi:10.1111/cge.13198 · Cited by 18
“In silico mutagenesis of Asp 264 to Asn was carried out using FoldX.”
Method
Biallelic sequence and structural variants in RAX2 are a novel cause for autosomal recessive inherited retinal disease
de Sompele, S. V., Smith, C. E. L., Karali, M., Cortón, M., Schil, K. V., Peelman, F., Cherry, T. J., Rosseel, T., Verdin, H., Derolez, J., et al. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0345-5 · Cited by 18
Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge
Savojardo, C., Petrosino, M., Babbi, G., Bovo, S., Corbi‐Verge, C., Casadio, R., Fariselli, P., Folkman, L., Garg, A., Karimi, M., et al. (2019) · Human Mutation · doi:10.1002/humu.23843 · Cited by 20
“The performances of participants were compared with those achieved by state‐of‐the‐art methods by including in our assessment the predictions returned by FoldX and I‐Mutant2.”
MethodInfluential
Binding and functional profiling of antibody mutants guides selection of optimal candidates as antibody drug conjugates
Zwaagstra, J. C., Sulea, T. I., Baardsnes, J., Radinovic, S., Donates, Y. C., Robert, A., O'Connor‐McCourt, M. D., Tikhomirov, I. A., & Jaramillo, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0226593 · Cited by 18
“This consensus scoring is based on the average Z-score over the individual raw scores calculated with the three component energy functions, SIE [33, 34], FoldX-FOLDEF [35], and RosettaInterface [37].”
Method

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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