Cited by 2,759Protein Structure and DynamicsEnzyme Structure and FunctionRNA and protein synthesis mechanisms

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

The FoldX web server: an online force field

Open access

Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., Serrano, L.·2005·Nucleic Acids Research

Open this paper in CoChat — free account Open PDF

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1093/nar/gki387

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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 11,297 · 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).”
Method
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 · 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).”
Method
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 · 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).”
Method
Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations
Guerois, 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-citation
Prediction 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-citation
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 · cited 8×
“The statistical mechanics algorithm TANGO, publicly available at http:// TANGO.embl.de/, provides such a predictive model.”
MethodInfluential
Molecular 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).”
Background
Effective 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.”
Background
Protein-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.”
Background
Rational 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

Flagged first, then by citations

Show all 23 references

Sorted by citations

Items 701–800 of 1,005

The alteration of the C‐terminal region of human frataxin distorts its structural dynamics and function
Faraj, S. E., Román, E. A., Arán, M., Gallo, M. L., & Santos, J. (2014) · FEBS Journal · doi:10.1111/febs.12869 · Cited by 29
Shedding Light on the Inhibitory Mechanisms of SARS-CoV-1/CoV-2 Spike Proteins by ACE2-Designed Peptides
Freitas, F. C., Ferreira, P. H. B., Favaro, D. C., & de Oliveira, R. J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01320 · Cited by 29
Comprehensive Deep Mutational Scanning Reveals the Immune-Escaping Hotspots of SARS-CoV-2 Receptor-Binding Domain Targeting Neutralizing Antibodies
Tsai, K., Lee, Y.-C., & Tseng, T.-S. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.698365 · Cited by 32
Agonist antibody discovery: Experimental, computational, and rational engineering approaches
Schardt, J. S., Jhajj, H. S., O’Meara, R. L., Lwo, T. S., Smith, M. D., & Tessier, P. M. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.09.008 · Cited by 32
Protein Function Analysis through Machine Learning
Avery, C., Patterson, J., Grear, T. J., Frater, T. D., & Jacobs, D. J. (2022) · Biomolecules · doi:10.3390/biom12091246 · Cited by 30
The Challenge of Next Generation Sequencing in the Context of Neuromuscular Diseases
Lek, M., & MacArthur, D. G. (2014) · Journal of Neuromuscular Diseases · doi:10.3233/jnd-140032 · Cited by 29
Crystal Structure of Inhibitor of Growth 4 (ING4) Dimerization Domain Reveals Functional Organization of ING Family of Chromatin-binding Proteins
Culurgioni, S., Muñoz, I. G., Moreno, A., Palacios, A., Villate, M., Palmero, I., Montoya, G., & Blanco, F. J. (2012) · Journal of Biological Chemistry · doi:10.1074/jbc.m111.330001 · Cited by 32
In silico prediction of the effects of mutations in the human UDP-galactose 4′-epimerase gene: Towards a predictive framework for type III galactosemia
McCorvie, T. J., & Timson, D. J. (2013) · Gene · doi:10.1016/j.gene.2013.04.061 · Cited by 29
A Rational Engineering Strategy for Designing Protein A-Binding Camelid Single-Domain Antibodies
Henry, K. A., Sulea, T. I., van Faassen, H., Hussack, G., Purisima, E. O., MacKenzie, C. R., & Arbabi‐Ghahroudi, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0163113 · Cited by 31
Molecular modeling and molecular dynamic simulation of the effects of variants in the TGFBR2 kinase domain as a paradigm for interpretation of variants obtained by next generation sequencing
Zimmermann, M. T., Urrutia, R. A., Oliver, G. R., Blackburn, P. R., Cousin, M. A., Bozeck, N. J., & Klee, E. W. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170822 · Cited by 29
Free energy perturbation calculations of mutation effects on SARS-CoV-2 RBD::ACE2 binding affinity
Sergeeva, A. P., Katsamba, P. S., Liao, J., Sampson, J. M., Bahna, F., Mannepalli, S., Morano, N. C., Shapiro, L., Friesner, R. A., & Honig, B. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168187 · Cited by 27
“[11] Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L.”
Influential
Redox sensitive human mitochondrial aconitase and its interaction with frataxin: In vitro and in silico studies confirm that it takes two to tango
Mansilla, S., Tórtora, V., Pignataro, M. F., Sastre, S., Castro, I. H., Chiribao, M. L., Robello, C., Zeida, A., Santos, J., & Castro, L. (2023) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2023.01.028 · Cited by 27
Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation
Bárcenas, O., Kuriata, A., Zalewski, M., Iglesias, V., Pintado‐Grima, C., Firlik, G., Burdukiewicz, M. J., Kmiecik, S., & Ventura, S. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae382 · Cited by 28
Optimization of polyethylene terephthalate biodegradation using a self-assembled multi-enzyme cascade strategy
Aer, L., Jiang, Q., Zhong, L., Si, Q., Liu, X., Pan, Y., Feng, J., Zeng, H., & Tang, L. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.134887 · Cited by 28
Rational enzyme design by reducing the number of hotspots and library size
Qin, Z., Yuan, B., Qu, G., & Sun, Z. (2024) · Chemical Communications · doi:10.1039/d4cc01394h · Cited by 28
Molecular Binding Mechanism of TtgR Repressor to Antibiotics and Antimicrobials
Fernández‐Escamilla, A. M., Fernandez-Ballester, G. J., Morel, B., Casares, S., & Ramos, J. L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138469 · Cited by 28
Delineating the relationship between amyotrophic lateral sclerosis and frontotemporal dementia: Sequence and structure-based predictions
Kumar, V., Islam, A., Hassan, M. I., & Ahmad, F. (2016) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2016.06.011 · Cited by 29
Organization and evolution of the chalcone synthase gene family in bread wheat and relative species
Glagoleva, A. Y., Ivanisenko, N. V., & Хлесткина, Е. К. (2019) · BMC Genetics · doi:10.1186/s12863-019-0727-y · Cited by 28
Unveiling the Interplay between the TLR4/MD2 Complex and HSP70 in the Human Cardiovascular System: A Computational Approach
de Oliveira, A. A., Faustino, J., de Lima, M. E., de Menezes, R. P., & Nunes, K. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20133121 · Cited by 29
Computational re-design of protein structures to improve solubility
Navarro, S., & Ventura, S. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1637413 · Cited by 29
Computational Nanoscopy of Tight Junctions at the Blood–Brain Barrier Interface
Rajagopal, N., Irudayanathan, F. J., & Nangia, S. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20225583 · Cited by 30
Computational saturation mutagenesis to predict structural consequences of systematic mutations in the beta subunit of RNA polymerase in Mycobacterium leprae
Vedithi, S. C., Rodrigues, C. H. M., Portelli, S., Skwark, M. J., Das, M., Ascher, D. B., Blundell, T. L., & Malhotra, S. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.01.002 · Cited by 27
Specific targeting of IL-1β activity to CD8+ T cells allows for safe use as a vaccine adjuvant
Eeckhout, B. V. D., Hoecke, L. V., Burg, E., Lint, S. V., Peelman, F., Kley, N., Uzé, G., Saelens, X., Tavernier, J. H., & Gerlo, S. (2020) · npj Vaccines · doi:10.1038/s41541-020-00211-5 · Cited by 28
A pan-cancer assessment of alterations of the kinase domain of ULK1, an upstream regulator of autophagy
Kumar, M., & Papaleo, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-71527-4 · Cited by 26
Conformational changes in tubulin upon binding cryptophycin-52 reveal its mechanism of action
Eren, E., Watts, N. R. M., Sackett, D. L., & Wingfield, P. T. (2021) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.101138 · Cited by 29
Mapping the sequence specificity of heterotypic amyloid interactions enables the identification of aggregation modifiers
Louros, N., Ramakers, M., Michiels, E., Konstantoulea, K., Morelli, C., Garcia, T., Moonen, N., D’Haeyer, S., Goossens, V., Thal, D. R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28955-9 · Cited by 28
Self-citation
Mutant MESD links cellular stress to type I collagen aggregation in osteogenesis imperfecta type XX
Ghosh, D. K., Udupa, P., Shrikondawar, A. N., Bhavani, G. S., Shah, H. H., Ranjan, A., & Girisha, K. M. (2022) · Matrix Biology · doi:10.1016/j.matbio.2022.12.001 · Cited by 29
A Novel Kleefstra Syndrome-associated Variant That Affects the Conserved TPLX Motif within the Ankyrin Repeat of EHMT1 Leads to Abnormal Protein Folding
Blackburn, P. R., Tischer, A., Zimmermann, M. T., Kemppainen, J. L., Sastry, S., Johnson, A. E. K., Cousin, M. A., Boczek, N. J., Oliver, G. R., Misra, V. K., et al. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.m116.770545 · Cited by 28
A novel CYCS mutation in the α‐helix of the CYCS C‐terminal domain causes non‐syndromic thrombocytopenia
Uchiyama, Y., Yanagisawa, K., Kunishima, S., et al. (2018) · Clinical Genetics · doi:10.1111/cge.13423 · Cited by 28
Distinct Allosteric Networks Underlie Mechanistic Speciation of ABC Transporters
Acar, B., Rose, J., Fas, B. A., Ben‐Tal, N., Lewinson, O., & Haliloǧlu, T. (2020) · Structure · doi:10.1016/j.str.2020.03.014 · Cited by 29
Recent advances in automated protein design and its future challenges
Setiawan, D., Brender, J., & Zhang, Y. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1465922 · Cited by 26
“Almost every scoring function used in popular protein design algorithms, such as those used in RosettaDesign [128,153,171], FoldX [122], or OSPREY [106], falls into this category.”
BackgroundMethodInfluential
Peptide design through binding interface mimicry with PepMimic
(2025) · doi:10.1038/s41551-025-01507-4 · Cited by 30
Analysing protein complexes in plant science: insights and limitation with AlphaFold 3
Lin, P.-Y., Huang, S.-C., Chen, K.-L., Huang, Y., Liao, C.-Y., Lin, G.-J., Lee, H., & Chen, P. (2025) · Botanical studies · doi:10.1186/s40529-025-00462-2 · Cited by 38
IFIT5 Participates in the Antiviral Mechanisms of Rainbow Trout Red Blood Cells
Chico, V., Salvador-Mira, M. E., Nombela, I., et al. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00613 · Cited by 27
Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning
Conti, S., & Karplus, M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006954 · Cited by 27
LONP1 de novo dominant mutation causes mitochondrial encephalopathy with loss of LONP1 chaperone activity and excessive LONP1 proteolytic activity
Besse, A., Brezavar, D., Hanson, J., Larson, A. A., & Bonnen, P. E. (2020) · Mitochondrion · doi:10.1016/j.mito.2020.01.004 · Cited by 28
Desmosomal COP9 regulates proteome degradation in arrhythmogenic right ventricular dysplasia/cardiomyopathy
Liang, Y., Lyon, R. C., Pellman, J., Bradford, W. H., Lange, S., Bogomolovas, J., Dalton, N. D., Gu, Y., Bobar, M., Lee, M., et al. (2021) · Journal of Clinical Investigation · doi:10.1172/jci137689 · Cited by 27
The MYC oncoprotein directly interacts with its chromatin cofactor PNUTS to recruit PP1 phosphatase
Wei, Y., Redel, C., Ahlner, A., Lemak, A. S., Johansson-Åkhe, I., Houliston, S., Kenney, T. M. G., Tamachi, A., Morad, V., Duan, S., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac138 · Cited by 28
Predicting protein stability changes upon mutation using a simple orientational potential
Hernández, I. M., Dehouck, Y., Bastolla, U., López‐Blanco, J. R., & Chacón, P. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad011 · Cited by 29
Association Between Foldability and Aggregation Propensity in Small Disulfide-Rich Proteins
Fraga, H., Graña‐Montes, R., Illa, R., Covaleda, G., & Ventura, S. (2014) · Antioxidants and Redox Signaling · doi:10.1089/ars.2013.5543 · Cited by 26
Using structural bioinformatics to investigate the impact of non synonymous SNPs and disease mutations: scope and limitations
Reumers, J., Schymkowitz, J. W. H., & Rousseau, F. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s8-s9 · Cited by 27
Self-citation
Asymmetric synthesis of (S)-3-chloro-1-phenyl-1-propanol using Saccharomyces cerevisiae reductase with high enantioselectivity
Choi, Y., Choi, H. J., Kim, D., Uhm, K.-N., & Kim, H. K. (2010) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-010-2442-5 · Cited by 27
Accurate Prediction of the Dynamical Changes within the Second PDZ Domain of PTP1e
Cilia, E., Vuister, G. W., & Lenaerts, T. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002794 · Cited by 27
The characterization of a thermostable and cambialistic superoxide dismutase from Thermus filiformis
Mandelli, F., Cairo, J. P. L. F., Citadini, A. P., et al. (2013) · Letters in Applied Microbiology · doi:10.1111/lam.12071 · Cited by 27
Folding RaCe: a robust method for predicting changes in protein folding rates upon point mutations
Chaudhary, P., Naganathan, A. N., & Gromiha, M. M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv091 · Cited by 27
Thermal stability and kinetic constants for 129 variants of a family 1 glycoside hydrolase reveal that enzyme activity and stability can be separately designed
Carlin, D. A., Hapig-Ward, S., Chan, B. W., Damrau, N., Riley, M. G., Caster, R. W., Bethards, B., & Siegel, J. B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176255 · Cited by 27
Improved Solution-State Properties of Monoclonal Antibodies by Targeted Mutations
Kuhn, A. B., Kube, S., Karow‐Zwick, A. R., et al. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b09126 · Cited by 27
High‐affinity PD ‐1 molecules deliver improved interaction with PD ‐L1 and PD ‐L2
Li, Y., Liang, Z., Tian, Y., Cai, W., Weng, Z., Chen, L., Zhang, H., Bao, Y., Zeng, S.-H., Zeng, S., et al. (2018) · Cancer Science · doi:10.1111/cas.13666 · Cited by 27
RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions
Hodges, A. M., Fenton, A. W., Dougherty, L. L., Overholt, A. C., & Swint‐Kruse, L. (2018) · Human Mutation · doi:10.1002/humu.23616 · Cited by 27
Rotamer Density Estimator is an Unsupervised Learner of the Effect of Mutations on Protein-Protein InteractionPreprint
Luo, S., Su, Y., Wu, Z., Su, C., Peng, J., & Ma, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.28.530137 · Cited by 31
Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs
Sen, N., Anishchenko, I., Bordin, N., Sillitoe, I., Velankar, S., Baker, D., & Orengo, C. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac187 · Cited by 25
Learning the local landscape of protein structures with convolutional neural networks
(2021) · doi:10.1007/s10867-021-09593-6 · Cited by 23
Quantification of biases in predictions of protein–protein binding affinity changes upon mutations
Tsishyn, M., Pucci, F., & Rooman, M. J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad491 · Cited by 33
From traditional to AI-driven: The evolution of intelligent enzyme engineering for biocatalysis
Lv, F., Zhang, J., You, S., & Qi, W. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108788 · Cited by 31
Modelling binding between CCR5 and CXCR4 receptors and their ligands suggests the surface electrostatic potential of the co-receptor to be a key player in the HIV-1 tropism
Kalinina, O. V., Pfeifer, N., & Lengauer, T. (2013) · Retrovirology · doi:10.1186/1742-4690-10-130 · Cited by 26
Molecular dynamics simulations and structure-based network analysis reveal structural and functional aspects of G-protein coupled receptor dimer interactions
Baltoumas, F. A., Theodoropoulou, M. C., & Hamodrakas, S. J. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9919-y · Cited by 26
Grafting PNIPAAm from β-barrel shaped transmembrane nanopores
Charan, H., Kinzel, J., Glebe, U., Anand, D., Garakani, T. M., Zhu, L., Bocola, M., Schwaneberg, U., & Böker, A. (2016) · Biomaterials · doi:10.1016/j.biomaterials.2016.08.033 · Cited by 26
Identification and characterization of a novel nanobody against human placental growth factor to modulate angiogenesis
Arezumand, R., Mahdian, R., Zeinali, S., Hassanzadeh‐Ghassabeh, G., Mansouri, K., Khanahmad, H., Asl, N. N., Rahimi, H., Behdani, M., Cohan, R. A., et al. (2016) · Molecular Immunology · doi:10.1016/j.molimm.2016.09.012 · Cited by 26
A non-canonical di-acidic signal at the C-terminal of Kv1.3 determines anterograde trafficking and surface expression
Martínez‐Mármol, R., Pérez-Verdaguer, M., Roig, S. R., Vallejo-Gracia, A., Gotsi, P., Serrano-Albarrás, A., Bahamonde, M. I., Ferrer-Montiel, A. V., Fernandez-Ballester, G. J., Comes, N., et al. (2013) · Journal of Cell Science · doi:10.1242/jcs.134825 · Cited by 26
Dynamic View of Allosteric Regulation in the Hsp70 Chaperones by J-Domain Cochaperone and Post-Translational Modifications: Computational Analysis of Hsp70 Mechanisms by Exploring Conformational Landscapes and Residue Interaction Networks
Astl, L., & Verkhivker, G. M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01045 · Cited by 24
Accurately Predicting Mutation-Caused Stability Changes from Protein Sequences Using Extreme Gradient Boosting
Lv, X., Chen, J., Lu, Y., Chen, Z., Xiao, N., & Yang, Y. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00064 · Cited by 26
Creation of a robust and R-selective ω-amine transaminase for the asymmetric synthesis of sitagliptin intermediate on a kilogram scale
Cheng, F., Chen, X., Li, M., et al. (2020) · Enzyme and Microbial Technology · doi:10.1016/j.enzmictec.2020.109655 · Cited by 26
High affinity Na + transport by wheat HKT1;5 is blocked by K +
Xu, B., Hrmová, M., & Gilliham, M. (2020) · Plant Direct · doi:10.1002/pld3.275 · Cited by 27
SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation
L, Y. S. B., Raghunathan, S., & Priyakumar, U. D. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c04913 · Cited by 24
G protein inactive and active forms investigated by simulation methods
Khafizov, K., Lattanzi, G., & Carloni, P. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22303 · Cited by 27
A Role for Saccharomyces cerevisiae Tpa1 Protein in Direct Alkylation Repair
Shivange, G., Kodipelli, N., Mohan, M., & Anindya, R. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m114.590216 · Cited by 26
SPROUTS: a database for the evaluation of protein stability upon point mutation
Lonquety, M., Lacroix, Z., Papandreou, N. C., & Chomilier, J. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn704 · Cited by 26
Positive Selection during the Evolution of the Blood Coagulation Factors in the Context of Their Disease-Causing Mutations
Rallapalli, P. M., Orengo, C. A., Studer, R. A., & Perkins, S. J. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu248 · Cited by 27
Mutations in the KDM5C ARID Domain and Their Plausible Association with Syndromic Claes-Jensen-Type Disease
Peng, Y., Suryadi, J., Yang, Y., Kucukkal, T. G., Cao, W., & Alexov, E. G. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms161126022 · Cited by 26
Conserved Water Mediated H-bonding Dynamics of Inhibitor, Cofactor, Asp 364 and Asn 303 in Human IMPDH II
Bairagya, H. R., Mukhopadhyay, B. P., & Sekar, K. (2009) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2009.10507265 · Cited by 26
C.c. & c.C.
Perdriel, J. L. (2012) · Cited by 26
Chromatin landscape at cis-regulatory elements orchestrates cell fate decisions in early embryogenesis
Cardamone, F., Piva, A., Löser, E., Eichenberger, B., Romero-Mulero, M. C., Zenk, F. L., Shields, E. J., Cabezas‐Wallscheid, N., Bonasio, R., Tiana, G., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57719-4 · Cited by 28
Protein design in biological networks: from manipulating the input to modifying the output
van der Sloot, A. M., Kiel, C., Serrano, L. G., & Stricher, F. (2009) · Protein Engineering Design and Selection · doi:10.1093/protein/gzp032 · Cited by 25
Self-citation
Computing the Pathogenicity of Alzheimer’s Disease Presenilin 1 Mutations
Tang, N., Dehury, B., & Kepp, K. P. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00896 · Cited by 23
Autosomal Dominant Tubulointerstitial Kidney Disease with Adult Onset due to a Novel Renin Mutation Mapping in the Mature Protein
Schaeffer, C., Izzi, C., Vettori, A., Pasqualetto, E., Cittaro, D., Lazarević, D., Caridi, G., Gnutti, B., Mazza, C., Jovine, L., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-48014-6 · Cited by 25
Directed evolution of VanR biosensor specificity in yeast
D’Ambrosio, V., Pramanik, S., Goroncy, K., et al. (2020) · Biotechnology Notes · doi:10.1016/j.biotno.2020.01.002 · Cited by 25
Studying the Effects of ACE2 Mutations on the Stability, Dynamics, and Dissociation Process of SARS-CoV‑2 S1/hACE2 Complexes
Hadi‐Alijanvand, H., & Rouhani, M. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00348 · Cited by 24
Generating Informative Sequence Tags from Antigen-Binding Regions of Heavily Glycosylated IgA1 Antibodies by Native Top-Down Electron Capture Dissociation
Greisch, J., den Boer, M. A., Beurskens, F. J. M., Schuurman, J., Tamara, S., Bondt, A., & Heck, A. J. R. (2021) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.0c00461 · Cited by 25
Homozygous missense VPS16 variant is associated with a novel disease, resembling mucopolysaccharidosis‐plus syndrome in two siblings
Yıldız, Y., Yıldız, Y., Koşukçu, C., et al. (2021) · Clinical Genetics · doi:10.1111/cge.14002 · Cited by 25
Phenotypic spectrum and genetics of PAX2-related disorder in the Chinese cohort
Yang, X., Li, Y., Ye, F., et al. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-01102-x · Cited by 25
PyUUL provides an interface between biological structures and deep learning algorithms
Orlando, G., Raimondi, D., Duran‐Romaña, R., Moreau, Y., Schymkowitz, J. W. H., & Rousseau, F. (2022) · Nature Communications · doi:10.1038/s41467-022-28327-3 · Cited by 27
Self-citation
PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
Ammar, A., Cavill, R., Evelo, C. T., & Willighagen, E. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00701-3 · Cited by 25
Structure-Based Rational Design of a Toll-like Receptor 4 (TLR4) Decoy Receptor with High Binding Affinity for a Target Protein
Han, J., Kim, H. J., Lee, S., Hong, S. P., Park, K., Jeon, Y. H., Kim, D., Cheong, H., & Kim, H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030929 · Cited by 25
Gene networks and liar paradoxes
Isalan, M. (2009) · BioEssays · doi:10.1002/bies.200900072 · Cited by 26
Protein structural robustness to mutations: an in silico investigation
Achoch, M., Dorantes-Gilardi, R., Wymant, C., Feverati, G., Salamatian, K., Vuillon, L., & Lesieur, C. (2015) · Physical Chemistry Chemical Physics · doi:10.1039/c5cp06091e · Cited by 25
Competing Lipid-Protein and Protein-Protein Interactions Determine Clustering and Gating Patterns in the Potassium Channel from Streptomyces lividans (KcsA)
Molina, M. L., Giudici, A. M., Poveda, J. A., Fernandez-Ballester, G. J., Montoya, E., Renart, M. L., Fernández-Carvajal, A. M., Encinar, J. A., Riquelme, G., Morales, A., et al. (2015) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.669598 · Cited by 25
Structural basis of activation of mammalian heme peroxidases
Singh, P. K., Iqbal, N., Sirohi, H. V., Bairagya, H. R., Kaur, P., Sharma, S. P., & Singh, T. P. (2017) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2017.11.003 · Cited by 25
Exploring the selective vulnerability in Alzheimer disease using tissue specific variant analysis
Dharshini, S. A. P., Taguchi, Y., & Gromiha, M. M. (2018) · Genomics · doi:10.1016/j.ygeno.2018.05.024 · Cited by 25
Duplication history and molecular evolution of the rbcS multigene family in angiosperms
Yamada, K., Davydov, I. I., Besnard, G., et al. (2019) · Journal of Experimental Botany · doi:10.1093/jxb/erz363 · Cited by 25
Exchange of water for sterol underlies sterol egress from a StARkin domain
Khelashvili, G., Pandey, K., Chauhan, N., Eliezer, D., & Menon, A. K. (2019) · eLife · doi:10.7554/elife.53444 · Cited by 23
Decoding the intricate network of molecular interactions of a hyperstable engineered biocatalyst
Marková, K., Chmelova, K., Marques, S. M., et al. (2020) · Chemical Science · doi:10.1039/d0sc03367g · Cited by 25
Solubis: optimize your protein
Baets, G. D., Durme, J. V., van der Kant, R., Schymkowitz, J. W. H., & Rousseau, F. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv162 · Cited by 25
Self-citation
Development of scoring functions for antibody sequence assessment and optimization
Seeliger, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076909 · Cited by 24
SIGMA leverages protein structural information to predict the pathogenicity of missense variants
Zhao, H., Du, H., Zhao, S., Chen, Z., Li, Y., Xu, K., Liu, B., Cheng, X., Wen, W., Li, G., et al. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100687 · Cited by 25
Protein stability models fail to capture epistatic interactions of double point mutations
Dieckhaus, H., & Kuhlman, B. A. (2024) · Protein Science · doi:10.1002/pro.70003 · Cited by 27
The mammalian complement system as an epitome of host–pathogen genetic conflicts
Cagliani, R., Forni, D., Filippi, G., Mozzi, A., GIOIA, L. D., Pontremoli, C., Pozzoli, U., Bresolin, N., Clerici, M., & Sironi, M. (2016) · Molecular Ecology · doi:10.1111/mec.13558 · Cited by 26
Structure-based protein engineering enables prenyl donor switching of a fungal aromatic prenyltransferase
Mai, P., Zocher, G., Stehle, T., et al. (2018) · Organic & Biomolecular Chemistry · doi:10.1039/c8ob02037j · Cited by 24
Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness
Yang, J., Naik, N. M., Patel, J. S., Wylie, C. S., Gu, W., Huang, J., Ytreberg, F. M., Naik, M. T., Weinreich, D. M., & Rubenstein, B. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233509 · Cited by 24
MPTherm-pred: Analysis and Prediction of Thermal Stability Changes upon Mutations in Transmembrane Proteins
Kulandaisamy, A., Zaucha, J. M., Frishman, D., & Gromiha, M. M. (2020) · Journal of Molecular Biology · doi:10.1016/j.jmb.2020.09.005 · Cited by 23
Two families with TET3-related disorder showing neurodevelopmental delay with craniofacial dysmorphisms
Seyama, R., Tsuchida, N., Okada, Y., Sakata, S., Hamada, K., Azuma, Y., Hamanaka, K., Fujita, A., Koshimizu, E., Miyatake, S., et al. (2021) · Journal of Human Genetics · doi:10.1038/s10038-021-00986-y · Cited by 25

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.
ORCID: Schymkowitz, J.
Stricher, F.
Nys, R.
Rousseau, F.
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
DOI: 10.1093/nar/gki387
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

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

The FoldX web server: an online force field

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks