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The FoldX web server: an online force field
Open accessAbstract
FoldX is an empirical force field that was developed for the rapid evaluation of the effect of mutations on the stability, folding and dynamics of proteins and nucleic acids. The core functionality of FoldX, namely the calculation of the free energy of a macromolecule based on its high-resolution 3D structure, is now publicly available through a web server at http://foldx.embl.de/. The current release allows the calculation of the stability of a protein, calculation of the positions of the protons and the prediction of water bridges, prediction of metal binding sites and the analysis of the free energy of complex formation. Alanine scanning, the systematic truncation of side chains to alanine, is also included. In addition, some reporting functions have been added, and it is now possible to print both the atomic interaction networks that constitute the protein, print the structural and energetic details of the interactions per atom or per residue, as well as generate a general quality report of the pdb structure. This core functionality will be further extended as more FoldX applications are developed.
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“The empirical energy-based methods, exemplified by STATIUM [12], FoldX [8] and Discovery Studio [13], have accelerated the prediction but suffered from insufficient conformational sampling, especially for mutations in flexible regions.”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [8]
In the citing paper
“We observed FoldX [8] and BeAtMuSic [38] yield the lowest correlations and the highest root-mean-square error (RMSE) on these datasets, indicating the difficulty of gauging the impacts of mutations via empirical energy-based methods.”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [8]
In the citing paper
“Previous methods were limited either in terms of their throughput (21,22) or in terms of their performance (23).”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [22]
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Cites
SWISS‐MODEL and the Swiss‐Pdb Viewer: An environment for comparative protein modeling
Guex, N., & Peitsch, M. C. (1997) · Electrophoresis · doi:10.1002/elps.1150181505 · Cited by 11,312 · cited 1×
“Although the pdb-reader in FoldX is continually extended to handle more exceptions, standardizing the format of anomalous files most often solves the problem—this can easily be achieved by opening and saving the file using Swiss PDB viewer (14) or WHATIF (15).”
MethodThe 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 · cited 1×
“The occupancy of an atom i, Occ(i), is calculated using an envelope function (21) with a distance cut-off of 6 s (9).”
MethodWHAT 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 · cited 1×
“Although the pdb-reader in FoldX is continually extended to handle more exceptions, standardizing the format of anomalous files most often solves the problem—this can easily be achieved by opening and saving the file using Swiss PDB viewer (14) or WHATIF (15).”
MethodGuerois, R., Nielsen, J. E., & Serrano, L. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00442-4 · Cited by 1,728 · cited 3×
“In addition, those water molecules that have a persistent interaction with groups of the protein, i.e. make more than two hydrogen bonds with the protein, are calculated explicitly in the D G wb term (8,9).”
BackgroundMethodInfluentialSelf-citationPrediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins
Fernández‐Escamilla, A. M., Rousseau, F., Schymkowitz, J. W. H., & Serrano, L. G. (2004) · Nature Biotechnology · doi:10.1038/nbt1012 · Cited by 1,667 · cited 1×
“In this fashion, TANGO predicts correctly both the disease causing and protective mutations in a wide range of proteins including Alzheimers’ A b peptide, lysozyme and transthyretin (12).”
BackgroundSelf-citationAbagyan, R. A., & Totrov, M. M. (1994) · Journal of Molecular Biology · doi:10.1006/jmbi.1994.1052 · Cited by 982 · cited 8×
“The statistical mechanics algorithm TANGO, publicly available at http:// TANGO.embl.de/, provides such a predictive model.”
MethodInfluentialMolecular basis for amyloid fibril formation and stability
Makin, O. S., Atkins, E. D. T., Sikorski, P., Johansson, J., & Serpell, L. C. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0406847102 · Cited by 649 · cited 1×
“For protein aggregation no such detail is available, although recently the first ever crystal structure of an amyloid fibre was released (23).”
BackgroundEffective energy functions for protein structure prediction
Lazaridis, T. (2000) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(00)00063-4 · Cited by 445 · cited 1×
“These are designated by Lazaridis and Karplus (1) as statistical (SEEF) and physical (PEEF) effective energy functions, respectively.”
BackgroundProtein-misfolding diseases: Getting out of shape
Dobson, C. M. (2002) · Nature · doi:10.1038/418729a · Cited by 341 · cited 1×
“The crystal structure confirms the widespread observation in the field that protein aggregates are generally rich in cross-beta structure (24), which allows a low-resolution structural approach to predict protein aggregation.”
BackgroundRational design of faster associating and tighter binding protein complexes.
Schreiber, G., Selzer, T., & Albeck, S. (2000) · Nature Structural Biology · doi:10.1038/76744 · Cited by 299 · cited 1×
“(10), which was shown to give a good estimation of the association rate ( k on ) of complex formation.”
Background
Cited by
Sorted by citations
Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas
Knijnenburg, T., Wang, L., Zimmermann, M. T., Chambwe, N., Gao, G. F., Cherniack, A. D., Fan, H., Shen, H., Way, G. P., Greene, C. S., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.076 · Cited by 1,145
DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability
Rodrigues, C. H., Pires, D. E., & Ascher, D. B. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky300 · Cited by 1,086
Pi-Pi contacts are an overlooked protein feature relevant to phase separation
Vernon, R. M., Chong, P. A., Tsang, B., Kim, T. H., Bah, A., Farber, P. J., Lin, H., & Forman-Kay, J. D. (2018) · eLife · doi:10.7554/elife.31486 · Cited by 904
“F), Analysis of the relationship between the energetic effects of point mutations (DDG) and pi-contacts for experimental DDGs (blue bars) and DDGs predicted by simulation against the FOLDX force field (Schymkowitz et al., 2005) (C,E) and Rosetta (D,F).”
BackgroundMethodInfluentialExploring the sequence determinants of amyloid structure using position-specific scoring matrices
Maurer‐Stroh, S., Debulpaep, M., Kuemmerer, N., de la Paz, M. L., Martins, I. C., Reumers, J., Morris, K. L., Copland, A., Serpell, L. C., Serrano, L. G., et al. (2010) · Nature Methods · doi:10.1038/nmeth.1432 · Cited by 700
“Therefore, we modeled a set of hexapeptides from amyloid-related disease proteins into available parallel and antiparallel fibril structures and evaluated their energy using Fold”
MethodSelf-citationWidespread macromolecular interaction perturbations in human genetic disorders
Sahni, N., Yi, S., Taipale, M., Fuxman Bass, J. I., Coulombe-Huntington, J., Yang, F., Peng, J., Weile, J., Karras, G. I., Wang, Y., et al. (2015) · Cell · doi:10.1016/j.cell.2015.04.013 · Cited by 594
“In addition, computational predictions by the FoldX program (Schymkowitz et al., 2005) suggest that mutant proteins with increased binding to QCFs are likely to be significantly less stable than their WT counterpart (Figure 3D and Table S2B).”
How Protein Stability and New Functions Trade Off
Tokuriki, N., Stricher, F., Serrano, L. G., & Tawfik, D. S. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000002 · Cited by 585
“The DDG Distributions The stability effects of mutations (DDG) were computed with the protein design software FoldX, whose force-field is based on empirical energy terms correlated with experimental DDG measurements [38,39].”
MethodSelf-citationGain of function of mutant p53 by coaggregation with multiple tumor suppressors
Xu, J., Reumers, J., Couceiro, J. R., De Smet, F., Gallardo, R., Rudyak, S., Cornelis, A., Rozenski, J., Zwolinska, A., Marine, J.-C., et al. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.546 · Cited by 543
Self-citationBat coronaviruses related to SARS-CoV-2 and infectious for human cells
Temmam, S., Vongphayloth, K., Baquero, E., Munier, S., Bonomi, M., Regnault, B., Douangboubpha, B., Karami, Y., Chrétien, D., Sanamxay, D., et al. (2022) · Nature · doi:10.1038/s41586-022-04532-4 · Cited by 553
FoldX 5.0: working with RNA, small molecules and a new graphical interface
Delgado, J., Radusky, L., Cianferoni, D., & Serrano, L. G. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz184 · Cited by 523
Self-citationThe landscape of somatic mutations in infant MLL-rearranged acute lymphoblastic leukemias
Andersson, A., Ma, J., Wang, J., Chen, X. '., Gedman, A. L., Dang, J., Nakitandwe, J., Holmfeldt, L., Parker, M. D., Easton, J. B., et al. (2015) · Nature Genetics · doi:10.1038/ng.3230 · Cited by 489
Mutations Strengthened SARS-CoV-2 Infectivity
Chen, J., Wang, R., Wang, M., & Wei, G. (2020) · Journal of Molecular Biology · doi:10.1016/j.jmb.2020.07.009 · Cited by 468
“These databases have been used as a benchmark for evaluating the predictive power of various computational methods [18, 21].”
MethodThe Stability Effects of Protein Mutations Appear to be Universally Distributed
Tokuriki, N., Stricher, F., Schymkowitz, J. W. H., Serrano, L. G., & Tawfik, D. S. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.03.069 · Cited by 415
Self-citationDefining and searching for structural motifs using DeepView/Swiss-PdbViewer
Johansson, M. U., Zoete, V., Michielin, O. A., & Guex, N. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-173 · Cited by 410
“According to FoldX (Version 2.5.1) [ 43,47] results, all residues belonging to the above mentioned motifs are expected to be important for the stability of the protein folds, with folding free energy changes upon alanine mutation ranging from 1.8 to 4.3 kcal/mol (Additional file 7, Additional file…”
BackgroundInfluentialMega-scale experimental analysis of protein folding stability in biology and design
Tsuboyama, K., Dauparas, J., Chen, J., Laine, E., Mohseni Behbahani, Y., Weinstein, J. J., Mangan, N. M., Ovchinnikov, S., & Rocklin, G. J. (2023) · Nature · doi:10.1038/s41586-023-06328-6 · Cited by 350
“…have been individually studied over decades to quantify the determinants of stability 11 , but these studies highlight a challenge: similar mutations can have widely varying effects in different protein contexts, and these sub-tleties remain difficult to predict despite substantial effort 12,13 .”
BackgroundPredicting Protein–Protein Interactions from the Molecular to the Proteome Level
Keskin, O., Tuncbag, N., & Gursoy, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00683 · Cited by 369
mCSM-PPI2: predicting the effects of mutations on protein–protein interactions
Rodrigues, C. H. M., Myung, Y., Pires, D. E. V., & Ascher, D. B. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz383 · Cited by 369
“Our method achieved a Pearson’s Correlation of 0.40 and an RMSE = 0.36 kcal / mol outperforming mCSM, iSEE, FoldX, BeAt-MuSiC (23) (Table 1).”
BackgroundMethodInfluentialChaperonin overexpression promotes genetic variation and enzyme evolution
Tokuriki, N., & Tawfik, D. S. (2009) · Nature · doi:10.1038/nature08009 · Cited by 347
Synthetic biology—putting engineering into biology
Heinemann, M., & Panke, S. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl469 · Cited by 346
Sumbalova, L., Štourač, J., Martínek, T., Bednář, D., & Damborský, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky417 · Cited by 346
“For stability evaluation, FoldX (44) is first used for repairing protein structure by filling in the missing atoms and patching the structure.”
MethodStructural analysis of the catalytically inactive kinase domain of the human EGF receptor 3
Jura, N., Shan, Y., Cao, X., Shaw, D. E., & Kuriyan, J. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912101106 · Cited by 338
“Empirical estimation of the binding free energy for dimer formation was carried out using FoldX (28), with conformations sampled from the molecular 21610 www.pnas.org cgi doi 10.1073 pnas.0912101106 Jura et al. dynamics trajectories.”
MethodInfluentialPrediction of water and metal binding sites and their affinities by using the Fold-X force field
Schymkowitz, J. W. H., Rousseau, F., Martins, I. C., Ferkinghoff‐Borg, J., Stricher, F., & Serrano, L. G. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0501980102 · Cited by 340
Self-citationA graphical interface for the FoldX forcefield
Durme, J. V., Delgado, J., Stricher, F., Serrano, L. G., Schymkowitz, J. W. H., & Rousseau, F. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr254 · Cited by 339
Self-citationProtein Design: From the Aspect of Water Solubility and Stability
Qing, R., Hao, S., Smorodina, E., Jin, D., Zalevsky, A. O., & Zhang, S. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00757 · Cited by 334
The common architecture of cross-beta amyloid.
Jahn, T., Makin, O. S., Morris, K., Marshall, K., Tian, P., Sikorski, P., & Serpell, L. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2009.09.039 · Cited by 271
FoldX as Protein Engineering Tool: Better Than Random Based Approaches?
Buß, O., Rudat, J., & Ochsenreither, K. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.002 · Cited by 294
Sannerud, R., Esselens, C., Ejsmont, P., Mattera, R., Rochin, L., Tharkeshwar, A. K., De Baets, G., De Wever, V., Habets, R., Baert, V., et al. (2016) · Cell · doi:10.1016/j.cell.2016.05.020 · Cited by 299
“Molecular Modeling The structure of the AP1 core (PDBID: 4P6Z) was used as template for modelling binding with the PSEN2 peptide motif VCDERTSLMS using FoldX force field (Schymkowitz et al., 2005).”
Self-citationPrinciples of Protein Stability and Their Application in Computational Design
Goldenzweig, A., & Fleishman, S. J. (2018) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-062917-012102 · Cited by 291
Analysis and Functional Prediction of Reactive Cysteine Residues
Marino, S. M., & Gladyshev, V. N. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.r111.275578 · Cited by 278
“After analyzing the typical arrangement of Cys ligands around zinc coordination sites, the method can recognize similar structural patterns in terms of both the nature of ligands clustered in space and their relative geometries and therefore predict new metal-binding sites (50).”
MethodInfluentialSNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants
Baets, G. D., Durme, J. V., Reumers, J., Maurer‐Stroh, S., Vanhee, P., Dopazo, J., Schymkowitz, J. W. H., & Rousseau, F. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr996 · Cited by 270
“SNPeffect on the other hand uses the FoldX (14) force field and aims”
MethodInfluentialSelf-citationDDMut: predicting effects of mutations on protein stability using deep learning
Zhou, Y., Pan, Q., Pires, D. E. V., Rodrigues, C. H. M., & Ascher, D. B. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad472 · Cited by 264
Bioinformatics challenges for personalized medicine
Fernald, G. H., Capriotti, E., Daneshjou, R., Karczewski, K. J., & Altman, R. B. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr295 · Cited by 268
Angiopoietin receptor TEK mutations underlie primary congenital glaucoma with variable expressivity
Souma, T., Tompson, S. W. J., Thomson, B. R., Siggs, O. M., Kizhatil, K., Yamaguchi, S., Feng, L., Limviphuvadh, V., Whisenhunt, K. N., Maurer‐Stroh, S., et al. (2016) · Journal of Clinical Investigation · doi:10.1172/jci85830 · Cited by 259
“To analyze the effect of missense variants on protein structure, FoldX (63) analyses were performed using known crystal structures when available.”
BackgroundInfluentialAGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures
Zambrano, R., Jamróz, M. H., Szczasiuk, A., Pujols, J., Kmiecik, S., & Ventura, S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv359 · Cited by 258
“Prior to A3D analysis, the input structure is energetically minimized using the FoldX force-field (10).”
MethodOpportunities and Challenges for Machine Learning-Assisted Enzyme Engineering
Yang, J., Li, F.-Z., & Arnold, F. H. (2024) · ACS Central Science · doi:10.1021/acscentsci.3c01275 · Cited by 268
Capturing the mutational landscape of the beta-lactamase TEM-1
Jacquier, H., Birgy, A., Nagard, H. L., Méchulam, Y., Schmitt, E., Glodt, J., Berçot, B., Petit, E., Poulain, J., Barnaud, G., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215206110 · Cited by 259
Thusberg, J., & Vihinen, M. (2009) · Human Mutation · doi:10.1002/humu.20938 · Cited by 256
“Cupsat [Parthiban et al., 2006], Eris [Yin et al., 2007], FoldX [Schymkowitz et al., 2005], DMUTANT [Zhou and Zhou, 2002], and PoPMuSic [Gilis and Rooman, 2000] calculate mutational free energy changes of the protein based on its 3D structure.”
BackgroundDeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks
Cao, H., Wang, J., He, L., Qi, Y., & Zhang, J. Z. H. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00697 · Cited by 244
Chevalier, F., Nieminen, K. M., Sánchez-Ferrero, J. C., Rodríguez, M. L., Chagoyen, M., Hardtke, C. S., & Cubas, P. (2014) · The Plant Cell · doi:10.1105/tpc.114.122903 · Cited by 241
“To assess the change in protein stability after mutation (DDG), we used the D14 structure (PDB:4ih4) with the prediction methods FoldX (Schymkowitz et al., 2005), I-Mutant (Capriotti et al.”
MethodSystematic identification of conditionally folded intrinsically disordered regions by AlphaFold2
Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304302120 · Cited by 240
“6 B ) (101, 102) and much larger than typical ΔΔ G values [1 ± 2 kcal mol −1 (103)].”
Do viral proteins possess unique biophysical features?
Tokuriki, N., Oldfield, C. J., Uversky, V. N., Berezovsky, I. N., & Tawfik, D. S. (2008) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2008.10.009 · Cited by 237
“These effects can be predicted by computing the DDG values for mutations [ 39 ].”
BackgroundHuman Cancer Protein-Protein Interaction Network: A Structural Perspective
Kar, G., Gursoy, A., & Keskin, O. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000601 · Cited by 235
“Next, to obtain a quantitative estimation of the importance of the interactions, we used FoldX algorithm [53,54] for calculating the interaction energy between two proteins, which serves as an estimate for the affinity of the interactions.”
MethodInfluentialYates, C. M., & Sternberg, M. J. E. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.012 · Cited by 241
Protein stability, flexibility and function
Teilum, K., Olsen, J. G., & Kragelund, B. B. (2010) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2010.11.005 · Cited by 237
“Despite enthalpy– entropy compensations, the effects of mutations on overall protein stability and ligand affinity are to a large degree predictable [19,20].”
BackgroundFunctional properties of insect olfactory receptors: ionotropic receptors and odorant receptors
Wicher, D., & Miazzi, F. (2021) · Cell and Tissue Research · doi:10.1007/s00441-020-03363-x · Cited by 228
“The ribbon representation of D. melanogaster Orco was modeled on the A. bakeri cryo-EM structure using the I-TASSER server (Yang and Zhang 2015) and optimized using FoldX (Schymkowitz et al.”
MethodMutations in KLHL40 Are a Frequent Cause of Severe Autosomal-Recessive Nemaline Myopathy
Ravenscroft, G., Miyatake, S., Lehtokari, V., Todd, E. J., Vornanen, P., Yau, K. S., Hayashi, Y. K., Miyake, N., Tsurusaki, Y., Doi, H., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.05.004 · Cited by 226
Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery
Buniello, A., Süveges, D., Cruz-Castillo, C., Llinares, M. B., Cornu, H., López, I., Tsukanov, K., Roldán‐Romero, J. M., Mehta, C., Fumis, L., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1128 · Cited by 251
“This new resource, funded through an Open Targets project ( 35 ) (Figure 2 A), includes predicted effects of missense variation based on AlphaMissense ( 36 ), interaction interfaces derived from AlphaFold ( 37 ) and protein abundance and stability based on protein structures using FoldX ( 38 ).”
Gayden, T., Sepulveda, F. E., Khuong-Quang, D.-A., Pratt, J., Valera, E. T., Garrigue, A., Kelso, S., Sicheri, F., Mikael, L. G., Hamel, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0251-4 · Cited by 218
Protein Thermostability Calculations Using Alchemical Free Energy Simulations
Seeliger, D., & de Groot, B. L. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2010.01.051 · Cited by 220
“Most of the successful applications of rational protein engineering, so far, have been built on knowledge-based scoring functions (10,11), implicit solvent models (12,13), or are SVM-based (14); however, molecular-dynamics-based methods utilizing explicit solvent have come of age and are developing into a high accurac…”
BackgroundVelarde‐Salcedo, A. J., Barrera-Pacheco, A., Lara‐González, S., Montero-Morán, G. M., Diaz-Gois, A., de Mejı́a, E. G., & de la Rosa, A. P. B. (2012) · Food Chemistry · doi:10.1016/j.foodchem.2012.08.032 · Cited by 211
“48 kCal/mol (Table 1), these are values in the range reported for stable interactions (Schymkowitz et al., 2005).”
BackgroundMethodAlternative evolutionary histories in the sequence space of an ancient protein
Starr, T. N., Picton, L. K., & Thornton, J. W. (2017) · Nature · doi:10.1038/nature23902 · Cited by 200
Learning protein fitness models from evolutionary and assay-labeled data
Hsu, C., Nisonoff, H. M., Fannjiang, C., & Listgarten, J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01146-5 · Cited by 240
“To get an initial sense of how bringing in structure-based prediction might alter our narrative, we compared augmentation approaches with and without FoldX-derived stability features on three data sets, finding that FoldX features could provide new, useful information.”
MethodInfluentialTransfer learning to leverage larger datasets for improved prediction of protein stability changes
Dieckhaus, H., Brocidiacono, M., Randolph, N. Z., & Kuhlman, B. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314853121 · Cited by 205
Wang, M., Cang, Z., & Wei, G.-W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0149-6 · Cited by 202
PupaSuite: finding functional single nucleotide polymorphisms for large-scale genotyping purposes
Conde, L., Vaquerizas, J. M., Dopazo, H., Arbiza, L., Reumers, J., Rousseau, F., Schymkowitz, J. W. H., & Dopazo, J. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl071 · Cited by 200
“Next to various external tools SNPeffect uses algorithms developed at the collaborating research groups, among which Tango (20) to predict b-aggregation regions in protein sequences and FoldX (21) to predict the stability change caused by the single amino acid variation.”
BackgroundMethodInfluentialSelf-citationTuning of Natural Killer Cell Reactivity by NKp46 and Helios Calibrates T Cell Responses
Narni-Mancinelli, É., Jaeger, B. N., Bernat, C., Fenis, A., Kung, S. K. P., Gassart, A. D., Mahmood, S., Gut, M., Heath, S. C., Estellé, J., et al. (2012) · Science · doi:10.1126/science.1215621 · Cited by 206
Characterizations of SARS-CoV-2 mutational profile, spike protein stability and viral transmission
Laha, S., Chakraborty, J., Das, S., Manna, S. K., Biswas, S., & Chatterjee, R. (2020) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2020.104445 · Cited by 199
“We used FoldX BuildModel function to construct the mutant 3D protein structures [23].”
Studer, R. A., Dessailly, B. H., & Orengo, C. A. (2013) · Biochemical Journal · doi:10.1042/bj20121221 · Cited by 202
Arnould, S., Chames, P., Perez, C., Lacroix, E., Duclert, A., Epinat, J.-C., Stricher, F., Petit, A.-S., Patin, A., Guillier, S., et al. (2005) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.10.065 · Cited by 191
Self-citationAggrescan3D (A3D) 2.0: prediction and engineering of protein solubility
Kuriata, A., Iglesias, V., Pujols, J., Kurcinski, M., Kmiecik, S., & Ventura, S. (2019) · Nucleic Acids Res. · doi:10.1093/nar/gkz321 · Cited by 172
“• Mutate residues––if selected, the option allows a user to select residues and perform point mutations on the input structure, which are built using FoldX.”
MethodInfluentialDuraiswamy, J., Turrini, R., Minasyan, A., Barras, D., Crespo, I., Grimm, A. J., Casado, J., Genolet, R., Benedetti, F., Wicky, A. M., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.10.008 · Cited by 191
Roberti, A., Dobay, M. P., Bisig, B., Vallois, D., Boéchat, C., Lanitis, E., Bouchindhomme, B., Parrens, M., Bossard, C., Quintanilla‐Martínez, L., et al. (2016) · Nature Communications · doi:10.1038/ncomms12602 · Cited by 189
Opportunities and challenges in design and optimization of protein function
Listov, D., Goverde, C. A., Correia, B. E., & Fleishman, S. J. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00718-y · Cited by 212
“Several stability design methods were implemented as automated web servers 42,90–92 accessible to scientists with no expertise in computational modelling.”
Hsp90 Mediates Membrane Deformation and Exosome Release
Lauwers, E., Wang, Y.-C., Gallardo, R. U., van der Kant, R., Michiels, E., Swerts, J., Baatsen, P., Zaiter, S. S., McAlpine, S. R., Gounko, N. V., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.07.016 · Cited by 188
Self-citationPeptide and peptide-based inhibitors of SARS-CoV-2 entry
Schütz, D., Ruiz‐Blanco, Y. B., Münch, J., Kirchhoff, F., Sánchez‐García, E., & Müller, J. A. (2020) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2020.11.007 · Cited by 180
Frappier, V., Chartier, M., & Najmanovich, R. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv343 · Cited by 181
“0 (7), Rosetta (8), DMutant (9) and PoPMusic (10), Eris (11), CUPSAT (12), I-Mutant (13) and AUTO-MUTE (14) on a”
BackgroundAB ‐Bind: Antibody binding mutational database for computational affinity predictions
Sirin, S., Apgar, J. R., Bennett, E. M., & Keating, A. E. (2016) · Protein Science · doi:10.1002/pro.2829 · Cited by 179
Rapid protein stability prediction using deep learning representations
Blaabjerg, L. M., Kassem, M. M., Good, L. L., Jonsson, N., Cagiada, M., Johansson, K. E., Boomsma, W., Stein, A., & Lindorff-Larsen, K. (2023) · eLife · doi:10.7554/elife.82593 · Cited by 177
Stability-activity tradeoffs constrain the adaptive evolution of RubisCO
Studer, R. A., Christin, P., Williams, M. A., & Orengo, C. A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310811111 · Cited by 177
“46 kcal/mol (38), and we used this value to bin the ΔΔGfold values into seven categories.”
MethodECNet is an evolutionary context-integrated deep learning framework for protein engineering
Luo, Y., Jiang, G., Yu, T., Liu, Y., Vo, L. T., Ding, H., Su, Y., Qian, W. W., Zhao, H., & Peng, J. (2021) · Nature Communications · doi:10.1038/s41467-021-25976-8 · Cited by 174
Pey, A. L., Stricher, F., Serrano, L. G., & Martı́nez, A. (2007) · The American Journal of Human Genetics · doi:10.1086/521879 · Cited by 171
“For a detailed explanation of the FoldX force field, see the work of Schymkowitz et al.(23,29) and the FoldX Web server.”
MethodSelf-citationPCSK1 Mutations and Human Endocrinopathies: From Obesity to Gastrointestinal Disorders
Stijnen, P., Ramos‐Molina, B., O’Rahilly, S., & Creemers, J. W. M. (2016) · Endocrine Reviews · doi:10.1210/er.2015-1117 · Cited by 177
Bzówka, M., Mitusińska, K., Raczyńska, A., Samol, A., Tuszyński, J. A., & Góra, A. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21093099 · Cited by 170
“If a substitution of a single nucleotide caused translation to a different amino acid compared to the correspon ing residue in the wild-type structure, an appropriate mutation was prop sed with FoldX calculations.”
InfluentialRahban, M., Zolghadri, S., Salehi, N., Ahmad, F., Haertlé, T., Rezaei‐Ghaleh, N., Sawyer, L., & Saboury, A. A. (2022) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2022.06.154 · Cited by 178
Phase Separation Mediates NUP98 Fusion Oncoprotein Leukemic Transformation
Chandra, B., Michmerhuizen, N. L., Shirnekhi, H. K., Tripathi, S., Pioso, B. J., Baggett, D. W., Mitrea, D. M., Iacobucci, I., White, M. R., Chen, J., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0674 · Cited by 175
“However, in contrast to constructs studied by others (23, ), we omitted the GLEBS domain, which mediates protein–protein interactions (17), from NHA9 Midi to probe the specific role of its 21 FG motifs in puncta formation.”
Selected‐fit versus induced‐fit protein binding: Kinetic differences and mutational analysis
Weikl, T. R., & von Deuster, C. I. E. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22223 · Cited by 173
“Empirical methods to calculate mutation-induced stability changes of proteins have been investigated intensively in the past years [20, 21, 22, 23, 24, 25, 26, 27, 28, 29].”
Methodger, D. M. K. ⁄., & Gohlke, H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq471 · Cited by 169
“To date, several computational tools have been developed to perform alanine scanning in silico (10–12).”
MethodOn Human Disease-Causing Amino Acid Variants: Statistical Study of Sequence and Structural Patterns
Petukh, M. G., Kucukkal, T. G., & Alexov, E. (2015) · Human Mutation · doi:10.1002/humu.22770 · Cited by 167
“Some methods are sequence based [Schymkowitz et al., 2005a, 2005b; Yang and Zhou, 2008a, 2008b; Dehouck et al., 2009, 2011; Chen et al., 2013; Dehouck et al., 2013; Pires et al., 2014 ], while other combine sequence information with physicochemical and structural characteristics [Capriotti et al.,…”
BackgroundMethodGenetic variability in progranulin contributes to risk for clinically diagnosed Alzheimer disease
Brouwers, N., Sleegers, K., Engelborghs, S., Maurer‐Stroh, S., Gijselinck, I., van der Zee, J., Pickut, B. A., den Broeck, M. V., Mattheijssens, M., Peeters, K., et al. (2008) · Neurology · doi:10.1212/01.wnl.0000319688.89790.7a · Cited by 162
Self-citationComputational design of peptide ligands
Vanhee, P., van der Sloot, A. M., Verschueren, E., Serrano, L. G., Rousseau, F., & Schymkowitz, J. W. H. (2011) · Trends in biotechnology · doi:10.1016/j.tibtech.2011.01.004 · Cited by 170
Self-citationAnalyzing Effects of Naturally Occurring Missense Mutations
Zhang, Z., Miteva, M. A., Wang, L., & Alexov, E. (2012) · Computational and Mathematical Methods in Medicine · doi:10.1155/2012/805827 · Cited by 173
“Molecular mechanics Using molecular mechanics force field and optimization to model molecular systems (i) 21-Hydroxylase-Deficiency: R132C, R149C, M283V, E431K; CYP450; C21 [161] (ii) Cancer: A159V, A161V, N235I, N239Y, T256I, S269I; p53 [162] (iii) Intellectual Disability: H101Q; CLIC2 protein; [10] (iv) Mutability o…”
BackgroundMethodInfluentialBiallelic Mutations in DNAJC12 Cause Hyperphenylalaninemia, Dystonia, and Intellectual Disability
Anikster, Y., Haack, T. B., Vilboux, T., Pode‐Shakked, B., Thöny, B., Shen, N., Guarani, V., Meißner, T., Mayatepek, E., Trefz, F. K., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.01.002 · Cited by 162
Tunçbağ, N., Kar, G., Keskin, Ö., Gürsoy, A., & Nussinov, R. (2008) · Briefings in Bioinformatics · doi:10.1093/bib/bbp001 · Cited by 167
“Then, the change in the binding energy is calculated [76].”
BackgroundContextual Specificity in Peptide-Mediated Protein Interactions
Stein, A., & Aloy, P. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002524 · Cited by 164
“Computation of binding contribution To quantify the contribution of motif and context to the interaction, we used the FoldX package [37,38] to conduct in silico alanine scanning experiments.”
BackgroundMethodComputational Design of Stable and Soluble Biocatalysts
Musil, M., Konegger, H., Hon, J., Bednář, D., & Damborský, J. (2018) · ACS Catalysis · doi:10.1021/acscatal.8b03613 · Cited by 159
Shan, S., Luo, S., Yang, Z., Hong, J., Su, Y., Ding, F., Fu, L., Li, C., Chen, P., Ma, J., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2122954119 · Cited by 157
Analysis of the human E2 ubiquitin conjugating enzyme protein interaction network
Markson, G., Kiel, C., Hyde, R., Brown, S., Charalabous, P., Bremm, A., Semple, J., Woodsmith, J., Duley, S., Salehi-Ashtiani, K., et al. (2009) · Genome Research · doi:10.1101/gr.093963.109 · Cited by 157
“Homology modeling was then achieved by replacing side-chain residues in accordance with structure-based alignments (Supplemental Files 7 and 8) using the BuildModel command of FoldX (Guerois et al. 2002; Schymkowitz et al. 2005a,b) vs 2.7.”
MethodInfluentialSelf-citationCurrent updates on computer aided protein modeling and designing
Khan, F. I., Wei, D., Gu, K., Hassan, M. I., & Tabrez, S. S. M. (2015) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2015.12.072 · Cited by 157
Engineering more stable proteins
Kazlauskas, R. J. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00014j · Cited by 151
Molecular basis of xeroderma pigmentosum group C DNA recognition by engineered meganucleases
Redondo, P. N., Prìeto, J., Muñoz, I. G., Alibés, A., Stricher, F., Serrano, L., Cabaniols, J., Daboussi, F., Arnould, S., Perez, C., et al. (2008) · Nature · doi:10.1038/nature07343 · Cited by 158
Self-citationMordret, E., Dahan, O., Asraf, O., Rak, R., Yehonadav, A., Barnabas, G. D., Cox, J., Geiger, T., Lindner, A. B., & Pilpel, Y. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.06.041 · Cited by 146
Protein stability: computation, sequence statistics, and new experimental methods
Magliery, T. J. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.09.002 · Cited by 156
“Among the best known, in addition to Rosetta (which was not actually designed for this purpose), are FoldX [35], Eris [36], and CC/PBSA [37].”
BackgroundNezhad, N. G., Rahman, R. N. Z. R. A., Normi, Y. M., Oslan, S. N., Shariff, F. M., & Leow, T. C. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.123440 · Cited by 150
Prediction and Reduction of the Aggregation of Monoclonal Antibodies
van der Kant, R., Karow‐Zwick, A. R., Durme, J. V., Blech, M., Gallardo, R. U., Seeliger, D., Aßfalg, K., Baatsen, P., Compernolle, G., Gils, A., et al. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.03.014 · Cited by 149
Self-citationDe novo SOX11 mutations cause Coffin–Siris syndrome
Tsurusaki, Y., Koshimizu, E., Ohashi, H., Phadke, S. R., Kou, I., Shiina, M., Suzuki, T., Okamoto, N., Imamura, S., Yamashita, M., et al. (2014) · Nature Communications · doi:10.1038/ncomms5011 · Cited by 145
Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2016) · Scientific Reports · doi:10.1038/srep29575 · Cited by 143
Evolutionary dynamics of SARS‐CoV‐2 nucleocapsid protein and its consequences
Rahman, M. S., Islam, M. R., Alam, A. S. M. R. U., Islam, I., Hoque, M. N., Akter, S., Rahaman, M. M., Sultana, M., & Hossain, M. A. (2020) · Journal of Medical Virology · doi:10.1002/jmv.26626 · Cited by 142
“We have also observed the effect of the double mutations in FoldX.40 For the paired mutations, we found ΔΔG = 3.42262 kcal/mol which indicates a highly destabilizing structure.”
BackgroundInfluentialPancotti, C., Benevenuta, S., Birolo, G., Alberini, V., Repetto, V., Sanavia, T., Capriotti, E., & Fariselli, P. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab555 · Cited by 147
“• FoldX [40] (stand-alone tool): empirical force field-based method predicting the effect of a single-point variation through a linear combination of empirical free energy terms, including entropy contribution, Van der Walls forces, hydrogen bonds and electrostatic interactions.”
Brender, J. R., & Zhang, Y. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004494 · Cited by 140
“For all the sequence and physics based energies except the docking functions PIE [45], PISA [46], and DCOMPLEX [47] and the all atomic energy functions Talaris 2013 [44] and FoldX [42, 43, 63] energies were calculated separately for the mutant and WT complex structures and Recognizing Mutations on Protein Binding Inte…”
BackgroundMethodDiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations
Høie, M. H., Gade, F. S., Johansen, J. M., Würtzen, C., Winther, O., Nielsen, M., & Marcatili, P. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1322712 · Cited by 151
Xiong, P., Zhang, C., Zheng, W., & Zhang, Y. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.11.022 · Cited by 140
“118), which was based on FoldX potential [14] using the complex structure refined from MacroMoleculeBuilder (MMB) simulations after the structural-alignment overlay [21].”
BackgroundMethodResultInfluential
Metadata
- Authors
- Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L.
- Affiliations
- Vrije Universiteit Brussel (BE)
- University of Copenhagen (DK)
- European Molecular Biology Laboratory (DE)
- Corresponding author
- Schymkowitz, J.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-07-01
- Volume
- 33
- Issue
- Web Server
- Pages
- W382-W388
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC1160148
- OpenAlex
- W2023490488
- Semantic Scholar
- 18ceb01ed1614dfc42404e0e2d838443cc0a4731
- Cited by
- 2,759
- References
- 23
- Citations by year
- 2026: 222 · 2025: 298 · 2024: 254 · 2023: 264 · 2022: 215 · 2021: 227 · 2020: 223 · 2019: 181 · 2018: 150 · 2017: 113 · 2016: 99 · 2015: 96 · 2014: 90 · 2013: 65 · 2012: 65
- FWCI
- 9.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Locations
- Nucleic Acids Research (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Funders
- European Commission
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- FoldX, empirical force fields, protein stability, free energy calculation, protein folding, protein dynamics, nucleic acids, mutation effects, proton positions, water bridges, metal binding sites, complex formation, alanine scanning
- MeSH terms
- Mutation, Nucleic Acid Conformation, Protein Conformation, Proteins, Software, Molecular Structure, Protein Folding, Internet, Polymorphism, Single Nucleotide, Databases, Protein
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