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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
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
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
Abagyan, R. A., & Totrov, M. M. (1994) · Journal of Molecular Biology · doi:10.1006/jmbi.1994.1052 · Cited by 982
Itzhaki, L. S., Otzen, D. E., & Fersht, A. R. (1995) · Journal of Molecular Biology · doi:10.1006/jmbi.1995.0616 · Cited by 750
Cited by
Sorted by citations
Items 301–400 of 1,001
Liu, S., Liu, C., & Deng, L. (2018) · Molecules · doi:10.3390/molecules23102535 · Cited by 78
“These methods include HEP [3], PredHS [11], iPPHOT [48], KFC2 [60], PCRPi [65,79], MINERVA [30], APIS [28], KFC [31], Robetta [80], and FOLDEF [81].”
MethodCui, H., Cao, H., Cai, H., Jaeger, K., Davari, M. D., & Schwaneberg, U. (2019) · Chemistry - A European Journal · doi:10.1002/chem.201903994 · Cited by 76
From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets
Chen, L., Morrow, J. K., Tran, H. T., Phatak, S. S., Du‐Cuny, L., & Zhang, S. (2012) · Current Pharmaceutical Design · doi:10.2174/138161212799436386 · Cited by 81
CCharPPI web server: computational characterization of protein–protein interactions from structure
Moal, I. H., Jiménez‐García, B., & Fernández‐Recio, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu594 · Cited by 78
Kiel, C., & Serrano, L. G. (2005) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.10.010 · Cited by 79
Self-citationCheng, G. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki894 · Cited by 78
Dissecting KMT2D missense mutations in Kabuki syndrome patients
Cocciadiferro, D., Augello, B., Nittis, P. D., Zhang, J., Mandriani, B., Malerba, N., Squeo, G. M., Romano, A., Piccinni, B., Verri, T., et al. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy241 · Cited by 76
Genome interpretation using in silico predictors of variant impact
Katsonis, P., Wilhelm, K. O., Williams, A. M., & Lichtarge, O. (2022) · Human Genetics · doi:10.1007/s00439-022-02457-6 · Cited by 86
“Some methods focus on specific aspects of protein function, such as folding stability of the mutated protein (Capriotti et al. 2005, 2008; Cheng et al. 2006; Dehouck et al. 2011; Fariselli et al. 2015; Guerois et al. 2002; Parthiban et al. 2006; Pires et al. 2014; Quan et al. 2016; Worth et al. 2011; Zhou and Zhou 200…”
BackgroundMethodFoldX force field revisited, an improved version
Delgado, J., Reche, R., Cianferoni, D., Orlando, G., van der Kant, R., Rousseau, F., Schymkowitz, J. W. H., & Serrano, L. G. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf064 · Cited by 84
“This number is very similar to the one previously published with a database of 1000 mutants (0.8 kcal/mol, Guerois et al. 2002).”
InfluentialSelf-citationRhoGAPs Attenuate Cell Proliferation by Direct Interaction with p53 Tetramerization Domain
Xu, J., Zhou, X., Wang, J., Li, Z., Kong, X., Qian, J., Hu, Y., & Fang, J. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.04.017 · Cited by 74
Huang, J., Xie, D., & Feng, Y. F. (2016) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2016.12.131 · Cited by 76
Kunka, A., Marques, S. M., Havlásek, M., Vasina, M., Velatova, N., Cengelova, L., Kovar, D. R., Damborský, J., Marek, M., Bednář, D., et al. (2023) · ACS Catalysis · doi:10.1021/acscatal.3c02575 · Cited by 81
Structure-based Prediction of bZIP Partnering Specificity
Grigoryan, G., & Keating, A. E. (2005) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.11.036 · Cited by 73
Human allelic variation: perspective from protein function, structure, and evolution
Jordan, D. M., Ramensky, V. E., & Sunyaev, S. R. (2010) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2010.03.006 · Cited by 77
Mechanisms for stabilisation and the maintenance of solubility in proteins from thermophiles
Greaves, R. B., & Warwicker, J. (2007) · BMC Structural Biology · doi:10.1186/1472-6807-7-18 · Cited by 75
Computational SNP Analysis: Current Approaches and Future Prospects
Kumar, A., Rajendran, V., Sethumadhavan, R., Shukla, P., Tiwari, S., & Purohit, R. (2013) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-013-9705-6 · Cited by 74
Tiberti, M., Terkelsen, T., Degn, K., Beltrame, L., Cremers, T. C., da Piedade, I., Marco, M. D., Maiani, E., & Papaleo, E. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac074 · Cited by 65
Venco, P., Bonora, M., Giorgi, C., Papaleo, E., Iuso, A., Prokisch, H., Pinton, P., & Tiranti, V. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00185 · Cited by 74
“3 kcal/mol, (Guerois et al., 2002) suggesting a loss of 12”
BackgroundImproving the Accuracy of Protein Thermostability Predictions for Single Point Mutations
Duan, J., Lupyan, D., & Wang, L. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.05.020 · Cited by 63
“Pucci et al. reported the performance of 15 predictors (19), and we chose to compare them with commonly used ones such as FoldX (78), Rosetta (79), MUPRO (80), and PoPMuSiCsym (81) using the raw data kindly provided by Pucci (Table 2; (78)).”
MethodPersistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants
Chen, J., Qiu, Y., Wang, R., & Wei, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106262 · Cited by 70
“Protein-protein BFE changes can be carried out in a variety of ways as shown in software packages FOLDX [18], SAAMBE [38], mCSM-AB [39], mCSM-PPI2 [42], BindProfX [58], etc. AI approaches take the advantage of existing data and often outperform other methods when experimental data become available.”
MethodComputational protein design with explicit consideration of surface hydrophobic patches
Jacak, R., Leaver‐Fay, A., & Kuhlman, B. A. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23241 · Cited by 72
Reis, C. R., van der Sloot, A. M., Natoni, A., Szegezdi, É., Setroikromo, R., Meijer, M., Sjollema, K. A., Stricher, F., Cool, R. H., Afshin Samali, A., et al. (2010) · Cell Death and Disease · doi:10.1038/cddis.2010.61 · Cited by 73
Self-citationBergman, J. E. H., Janssen, N., van der Sloot, A. M., de Walle, H. E. K., Schoots, J., Rendtorff, N. D., Tranebjærg, L., Hoefsloot, L. H., van Ravenswaaij‐Arts, C. M. A., & Hofstra, R. M. W. (2012) · Human Mutation · doi:10.1002/humu.22106 · Cited by 73
Parthiban, V., Gromiha, M. M., Hoppe, C., & Schomburg, D. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21115 · Cited by 73
HIV Control Is Mediated in Part by CD8 + T-Cell Targeting of Specific Epitopes
Pereyra, F., Heckerman, D. E., Carlson, J. M., Kadie, C. M., Soghoian, D. Z., Karel, D., Goldenthal, A. R., Davis, O. B., DeZiel, C. E., Lin, T.-H., et al. (2014) · Journal of Virology · doi:10.1128/jvi.01004-14 · Cited by 72
“Briefly, the structures of p17 (Protein Data Bank [PDB] code 2GOL) (45), p24 hexamer (PDB 3H4E) (46), protease dimer (PDB 3IXO) (47), reverse transcriptase (RT) (PDB 1DLO) (48), integrase (PDB 1BIS) (49), Tat (PDB 3MI9) (50), Rev (PDB 3LPH) (51), Nef (PDB 1EFN) (52), and gp120 (PDB 3JWD) (53) were mutated to the NL4-3…”
MethodVersatile Peptide C-Terminal Functionalization via a Computationally Engineered Peptide Amidase
Wu, B., Wijma, H. J., Song, L., Rozeboom, H. J., Poloni, C., Tian, Y., Arif, M. I., Nuijens, T., Quaedflieg, P. J. L. M., Szymański, W., et al. (2016) · ACS Catalysis · doi:10.1021/acscatal.6b01062 · Cited by 73
Zhou, G., Chen, M., Ju, C. J. ‐., Wang, Z., Jiang, J., & Wang, W. W. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa015 · Cited by 70
“We compare MuPIPR with two groups of baselines for this task: (i) BeAtMusic [11], Dcomplex [33], and FoldX3 [16] are empirical linear methods; (ii) mCSM [43], BindProfX [51] and Mutabind [29] are statistical learning methods leveraging structural and/or energy features.”
MethodAllen, B. D., Nisthal, A., & Mayo, S. L. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1012985107 · Cited by 72
Deng, L., Zhang, Q. C., Chen, Z., Meng, Y., Guan, J., & Zhou, S. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku437 · Cited by 72
“FOLDEF (9) and Robetta (10) belong to this group and were developed for the fast estimation of mutational free energy changes of a protein for hot spot identification; (iii) machine-learning methods, such as neural networks (11), decision trees (12), support vector machines (13–15), Bayesian networks (16,17), minimum…”
BackgroundMethodBreaking the amyloidogenicity code: Methods to predict amyloids from amino acid sequence
Ahmed, A. B., & Kajava, A. V. (2012) · FEBS Letters · doi:10.1016/j.febslet.2012.12.006 · Cited by 73
LogitBoost classifier for discriminating thermophilic and mesophilic proteins
Zhang, G., & Fang, B. (2006) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2006.07.020 · Cited by 71
Chavarría‐Soley, G., Sticht, H., Aklillu, E., Ingelman‐Sundberg, M., Pasutto, F., Reis, A., & Rautenstrauss, B. W. (2008) · Human Mutation · doi:10.1002/humu.20786 · Cited by 71
Reconstructing the TIR Side of the Myddosome: a Paradigm for TIR-TIR Interactions
Vyncke, L., Bovijn, C., Pauwels, E., Acker, T. V., Ruyssinck, E., Burg, E., Tavernier, J. H., & Peelman, F. (2016) · Structure · doi:10.1016/j.str.2015.12.018 · Cited by 69
“The effect of the mutation on the protein stability was calculated using FoldX with both X-ray structure (PDB: 4DOM) and NMR structure (PDB: 2Z5V) of MyD88 TIR as template (Van Durme et al., 2011; Guerois et al., 2002; Ohnishi et al., 2009).”
MethodDDGun: an untrained predictor of protein stability changes upon amino acid variants
Montanucci, L., Capriotti, E., Birolo, G., Benevenuta, S., Pancotti, C., Lal, D., & Fariselli, P. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac325 · Cited by 70
A novel empirical free energy function that explains and predicts protein–protein binding affinities
Audie, J., & Scarlata, S. (2007) · Biophysical Chemistry · doi:10.1016/j.bpc.2007.05.021 · Cited by 70
Friel, C. T., Smith, D. A., Vendruscolo, M., Gsponer, J., & Radford, S. E. (2009) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1562 · Cited by 69
Protein Destabilization as a Common Factor in Diverse Inherited Disorders
Redler, R. L., Das, J., Diaz, J. R., & Dokholyan, N. V. (2015) · Journal of Molecular Evolution · doi:10.1007/s00239-015-9717-5 · Cited by 67
“Several computational tools, including FoldX (Guerois et al. 2002), PolyPhen (Ramensky et al. 2002), PANTHER (Thomas et al. 2003), SIFT (Ng and Henikoff 2003), nsSNPAnalyzer (Bao et al. 2005), PhD-SNP (Capriotti et al. 2006), Eris (Yin et al. 2007a, b), SNAP (Bromberg and Rost 2007), MutPred (Li et…”
MethodMu, Q., Cui, Y., Tian, Y., Hu, M., Tao, Y., & Wu, B. (2019) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2019.06.094 · Cited by 70
Phenotype inference in an Escherichia coli strain panel
Galardini, M., Koumoutsi, A., Herrera-Domínguez, L., Varela, J. A. C., Telzerow, A., Wagih, O., Wartel, M., Clermont, O., Denamur, É., Typas, A., et al. (2017) · eLife · doi:10.7554/elife.31035 · Cited by 74
“Structural impact of nonsynonymous substitutions has been computed using FoldX ( Guerois et al., 2002 ) version 4; both 3-”
Huang, P., Chu, S. K. S., Frizzo, H. N., Connolly, M. P., Caster, R. W., & Siegel, J. B. (2020) · ACS Omega · doi:10.1021/acsomega.9b04105 · Cited by 69
Bi, J., Chen, S., Zhao, X., Nie, Y., & Xu, Y. (2020) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-020-10764-z · Cited by 69
Directed laccase evolution for improved ionic liquid resistance
Liu, H., Zhu, L., Bocola, M., Chen, N., Spieß, A. C., & Schwaneberg, U. (2013) · Green Chemistry · doi:10.1039/c3gc36899h · Cited by 70
Kempkens, Ö., Médina, E., Fernández-Ballester, G., Ozüyaman, S., Bivic, A. L., Serrano, L., & Knust, E. (2006) · European Journal of Cell Biology · doi:10.1016/j.ejcb.2006.03.003 · Cited by 80
Self-citationBioinformatics for personal genome interpretation
Capriotti, E., Nehrt, N. L., Kann, M. G., & Bromberg, Y. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbr070 · Cited by 68
Kolarich, D., Turecek, P. L., Weber, A., Mitterer, A., Graninger, M., Matthiessen, P., Nicolaes, G. A. F., Altmann, F., & Schwarz, H. P. (2006) · Transfusion · doi:10.1111/j.1537-2995.2006.01004.x · Cited by 69
Directed evolution methods for overcoming trade‐offs between protein activity and stability
Stimple, S. D., Smith, M. D., & Tessier, P. M. (2019) · AIChE Journal · doi:10.1002/aic.16814 · Cited by 67
On the Importance of Polar Interactions for Complexes Containing Intrinsically Disordered Proteins
Wong, E. T. C., Na, D., & Gsponer, J. A. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003192 · Cited by 68
Spatial chemical conservation of hot spot interactions in protein-protein complexes
Shulman‐Peleg, A., Shatsky, M., Nussinov, R., & Wolfson, H. J. (2007) · BMC Biology · doi:10.1186/1741-7007-5-43 · Cited by 71
Prediction of hot spots in protein interfaces using a random forest model with hybrid features
Wang, L., Liu, Z., Zhang, X.-S., & Chen, L. (2012) · Protein Engineering Design and Selection · doi:10.1093/protein/gzr066 · Cited by 68
Comparison of Five Protein Engineering Strategies for Stabilizing an α/β-Hydrolase
Jones, B. J., Lim, H. Y., Huang, J., & Kazlauskas, R. J. (2017) · Biochemistry · doi:10.1021/acs.biochem.7b00571 · Cited by 69
PROTS-RF: A Robust Model for Predicting Mutation-Induced Protein Stability Changes
Li, Y., & Fang, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047247 · Cited by 67
Predicting the stability of mutant proteins by computational approaches: an overview
Marabotti, A., Scafuri, B., & Facchiano, A. M. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa074 · Cited by 67
Molecular basis of engineered meganuclease targeting of the endogenous human RAG1 locus
Muñoz, I. G., Prìeto, J., Subramanian, S., y Coloma, J. P. D. S., Redondo, P. N., Villate, M., Merino, N., Marenchino, M., D’Abramo, M., Gervasio, F. L., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq801 · Cited by 69
Self-citationXiao, Z., Bergeron, H., Große, S., Beauchemin, M., Garron, M.-L., Shaya, D., Sulea, T. I., Cygler, M., & Lau, P. C. K. (2007) · Applied and Environmental Microbiology · doi:10.1128/aem.02220-07 · Cited by 67
Estrogen destabilizes microtubules through an ion‐conductivity‐independent TRPV1 pathway
Goswami, C., Kuhn, J., Dina, O. A., Fernandez-Ballester, G. J., Levine, J. D., Ferrer-Montiel, A. V., & Hucho, T. B. (2011) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2011.07270.x · Cited by 64
Can Contact Potentials Reliably Predict Stability of Proteins?
Khatun, J., Khare, S. D., & Dokholyan, N. V. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.01.002 · Cited by 69
Analysis and network representation of hotspots in protein interfaces using minimum cut trees
Tunçbağ, N., Salman, F. S., Keskin, Ö., & Gürsoy, A. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22741 · Cited by 66
The Role of Conformational Dynamics and Allostery in the Disease Development of Human Ferritin
Kumar, A., Glembo, T. J., & Ozkan, S. B. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2015.06.060 · Cited by 61
“Using FoldX (62) to calculate the DDG value, the change in folding stability upon mutation, it would be expected that disease variants should have larger DDG values because they cause more destabilization.”
MethodCell Type–Specific Importance of Ras–c-Raf Complex Association Rate Constants for MAPK Signaling
Kiel, C., & Serrano, L. (2009) · Science Signaling · doi:10.1126/scisignal.2000397 · Cited by 66
Self-citationBoosting antibody developability through rational sequence optimization
Seeliger, D., Schulz, P., Litzenburger, T., Spitz, J., Hoerer, S., Blech, M., Enenkel, B., Studts, J., Garidel, P., & Karow‐Zwick, A. R. (2015) · mAbs · doi:10.1080/19420862.2015.1017695 · Cited by 67
Computational sidechain placement and protein mutagenesis with implicit solvent models
Lopes, A., Aleksandrov, A. V., Bathelt, C. M., Archontis, G., & Simonson, T. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21379 · Cited by 66
Dourado, D. F. A. R., Pohle, S., Carvalho, A. T. P., Dheeman, D. S., Caswell, J. M., Skvortsov, T., Miskelly, I. R., Brown, R. T., Quinn, D. J., Allen, C. C. R., et al. (2016) · ACS Catalysis · doi:10.1021/acscatal.6b02380 · Cited by 66
The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae
Galardini, M., Busby, B. P., Viéitez, C., Dunham, A. S., Typas, A., & Beltrão, P. (2019) · Molecular Systems Biology · doi:10.15252/msb.20198831 · Cited by 68
“…variants have been identified for many human diseases (Cohen et al , 2005; Flannick et al , 2014; Chen et al , 2016) and loss-of-function (LoF) mutations in different model organisms (Hamilton & Yu, 2012; Chari & Dworkin, 2013; Vu et al , 2015; Chow et al , 2016; Mullis et al , 2018).”
Magnitude of the Hydrophobic Effect at Central versus Peripheral Sites in Protein-Protein Interfaces
Li, Y., Huang, Y., Swaminathan, C. P., Smith‐Gill, S. J., & Mariuzza, R. A. (2005) · Structure · doi:10.1016/j.str.2004.12.012 · Cited by 65
Beltrão, P., Kiel, C., & Serrano, L. G. (2007) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2007.05.005 · Cited by 67
Self-citationMartin, C., Maqueo, A. O., Wijma, H. J., & Fraaije, M. W. (2018) · Biotechnology for Biofuels · doi:10.1186/s13068-018-1051-x · Cited by 68
Prediction of protein stability upon point mutations
Gromiha, M. M. (2007) · Biochemical Society Transactions · doi:10.1042/bst0351569 · Cited by 65
Simões‐Correia, J., Figueiredo, J., Lopes, R., Stricher, F., Oliveíra, C., Serrano, L. G., & Seruca, R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033783 · Cited by 65
Self-citationStructure‐energy‐based predictions and network modelling of RASopathy and cancer missense mutations
Kiel, C., & Serrano, L. G. (2014) · Molecular Systems Biology · doi:10.1002/msb.20145092 · Cited by 67
“FoldX (http://foldx.crg.es/) is a computer algorithm that allows the calculation of interaction energies contributing to the stability of proteins and protein complexes (Guerois et al , 2002; Schymko-witz et al , 2005).”
BackgroundMethodSelf-citationThe structural impact of cancer-associated missense mutations in oncogenes and tumor suppressors
Stehr, H., Jang, S.-H. J., Duarte, J. M., Wierling, C., Lehrach, H. R., Lappé, M., & Lange, B. M. H. (2011) · Molecular Cancer · doi:10.1186/1476-4598-10-54 · Cited by 65
Kønig, S. M., Rissler, V., Terkelsen, T., Lambrughi, M., & Papaleo, E. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007485 · Cited by 67
“eu) empirical force field to predict changes in stability and interaction energies [98].”
BackgroundHighly thermostable fungal cellobiohydrolase I (Cel7A) engineered using predictive methods
Komor, R. S., Romero, P. A., Xie, C. B., & Arnold, F. H. (2012) · Protein Engineering Design and Selection · doi:10.1093/protein/gzs058 · Cited by 64
Tur, V., van der Sloot, A. M., Reis, C. R., Szegezdi, É., Cool, R. H., Afshin Samali, A., Serrano, L., & Quax, W. J. (2008) · Journal of Biological Chemistry · doi:10.1074/jbc.m800457200 · Cited by 64
Self-citationLong-range cooperative binding effects in a T cell receptor variable domain
Jalali, B. M., Buonpane, R. A., Zhu, P., Herfst, C. A., Rahman, A., McCormick, J. K., Kranz, D. M., & Sundberg, E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600220103 · Cited by 64
Prediction of protein–protein interactions: unifying evolution and structure at protein interfaces
Tunçbağ, N., Gürsoy, A., & Keskin, Ö. (2011) · Physical Biology · doi:10.1088/1478-3975/8/3/035006 · Cited by 63
Worth, C. L., Bickerton, G. R., Schreyer, A. M., Forman, J., Cheng, T. M. K., Lee, S., Gong, S., Burke, D. F., & Blundell, T. L. (2007) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720007003120 · Cited by 63
Sergeev, Y. V., Caruso, R. C., Meltzer, M. R., Smaoui, N., MacDonald, I. M., & Sieving, P. A. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq006 · Cited by 64
Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads
Nguyen, H. Q., Roy, J., Harink, B., Damle, N. P., Latorraca, N. R., Baxter, B. C., Brower, K. K., Longwell, S. A., Kortemme, T., Thorn, K. S., et al. (2019) · eLife · doi:10.7554/elife.40499 · Cited by 61
The Contribution of Polar Group Burial to Protein Stability Is Strongly Context-dependent
Takano, K., Scholtz, J. M., Sacchettini, J. C., & Pace, C. N. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.m304177200 · Cited by 62
Algorithmic approaches to protein-protein interaction site prediction
Aumentado‐Armstrong, T., Istrate, B., & Murgita, R. A. (2015) · Algorithms for Molecular Biology · doi:10.1186/s13015-015-0033-9 · Cited by 63
“Electrostatic Potential APBS [146], DelPhi [147], FoldX [148,149] RAD-T [37], Bradford & Westhead [13], Sting-LDAWNA [42] RAD-T [37]”
BackgroundA General Mechanism for the Propagation of Mutational Effects in Proteins
Rajasekaran, N., Suresh, S., Gopi, S., Raman, K., & Naganathan, A. N. (2016) · Biochemistry · doi:10.1021/acs.biochem.6b00798 · Cited by 62
Williamson, K. A., Hall, H. N., Owen, L. J., Livesey, B., Hanson, I. M., Adams, G. G. W., Bodek, S., Calvas, P., Castle, B., Clarke, M. W., et al. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0685-9 · Cited by 61
In-vitro and in-vivo effects of the CYP2C9*11 polymorphism on warfarin metabolism and dose
Tai, G., Farin, F. M., Rieder, M. J., Dreisbach, A. W., Veenstra, D. L., Verlinde, C. L. M. J., & Rettie, A. E. (2005) · Pharmacogenetics and Genomics · doi:10.1097/01.fpc.0000162005.80857.98 · Cited by 62
Abildgaard, A. B., Stein, A., Nielsen, S. V., Schultz-Knudsen, K., Papaleo, E., Shrikhande, A., Hoffmann, E. R., Bernstein, I., Gerdes, A., Takahashi, M., et al. (2019) · eLife · doi:10.7554/elife.49138 · Cited by 69
“The changes in folding stability (DDG) were calculated using FoldX (Guerois et al., 2002) based on”
BackgroundMethodInfluentialMorra, G., & Colombo, G. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21963 · Cited by 61
Bulliard, Y., Turelli, P., Röhrig, U. F., Zoete, V., Mangeat, B., Michielin, O. A., & Trono, D. (2009) · Journal of Virology · doi:10.1128/jvi.01491-09 · Cited by 61
Plun‐Favreau, H., Perret, D., Diveu, C., Froger, J., Chevalier, S., Lelièvre, E., Gascan, H., & Chabbert, M. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.m303168200 · Cited by 61
Inferring Stabilizing Mutations from Protein Phylogenies: Application to Influenza Hemagglutinin
Bloom, J. D., & Glassman, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000349 · Cited by 61
Protein binding hot spots prediction from sequence only by a new ensemble learning method
Hu, S., Chen, P., Wang, B., & Li, J. (2017) · Amino Acids · doi:10.1007/s00726-017-2474-6 · Cited by 61
Exploring the charge space of protein–protein association: A proteomic study
Shaul, Y., & Schreiber, G. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20489 · Cited by 61
Diaz, D. J., Gong, C., Ouyang-Zhang, J., Loy, J. M., Wells, J., Yang, D. K., Ellington, A. D., Dimakis, A. G., & Klivans, A. R. (2024) · Nature Communications · doi:10.1038/s41467-024-49780-2 · Cited by 69
“Previous structure-based stability predictors 30 – 32, 34 , 59, 60 require two structures — either experimental or computational — to explicitly model the We highlight several design decisions of the regression head used in Stability Oracle.”
Alibés, A., Nadra, A. D., Masi, F. D., Bulyk, M. L., Serrano, L., & Stricher, F. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq683 · Cited by 60
Self-citationPadhi, S. K., Fujii, R., Legatt, G. A., Fossum, S. L., Berchtold, R., & Kazlauskas, R. J. (2010) · Chemistry & Biology · doi:10.1016/j.chembiol.2010.06.013 · Cited by 60
Computational Methods to Predict Therapeutic Protein Aggregation
Buck, P. M., Kumar, S., Wang, X., Agrawal, N. J., Trout, B. L., & Singh, S. K. (2012) · Methods in molecular biology · doi:10.1007/978-1-61779-921-1_26 · Cited by 61
Bu, Y., Cui, Y., Peng, Y., Hu, M., Tian, Y., Tao, Y., & Wu, B. (2018) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-018-8872-1 · Cited by 60
“…the crystal structure of GH11 xylanase XynCDBFV from Neocallimastix patriciarum (PDB ID 3WP4) (Cheng et al. 2014), energy calculations with FoldX (Guerois et al. 2002), Rosetta_ddg (Kellogg et al. 2011), and ABACUS (Xiong et al. 2017) were performed to identify the potentially stabilizing…”
MethodCheng, R. R., Nordesjö, O., Hayes, R. L., Levine, H., Flores, S. C., Onuchic, J. N., & Morcos, F. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw188 · Cited by 62
“ZEMu can then estimate the binding affinity between the mutant-PhoQ/PhoP, D G ZEMuTCS ð mutant Þ , and the wild type-PhoQ/PhoP, D G ZEMuTCS ð WT Þ , using the knowledge-based FoldX (Guerois et al. 2002) potential.”
MethodBoyenle, I. D., Divine, U. C., Adeyemi, R. O., Ayinde, K. S., Olaoba, O. T., Apu, C., Du, L., Lü, Q., Yin, X., & Adelusi, T. I. (2021) · Pharmacological Research · doi:10.1016/j.phrs.2021.105577 · Cited by 59
The Tryptophan Switch: Changing Ligand-binding Specificity from Type I to Type II in SH3 Domains
Fernandez-Ballester, G. J., Blanes‐Mira, C., & Serrano, L. (2003) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.10.060 · Cited by 59
Self-citationWiederstein, M., & Sippl, M. J. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.11.012 · Cited by 59
Comprehensive in silico mutagenesis highlights functionally important residues in proteins
Bromberg, Y., & Rost, B. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn268 · Cited by 59
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review