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 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.
Overview
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
Cites
Statistics
Verification
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?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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 11,297 · 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).”
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
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–200 of 1,005
The amyloid stretch hypothesis: Recruiting proteins toward the dark side
Esteras‐Chopo, A., Serrano, L. G., & de la Paz, M. L. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0505905102 · Cited by 140
Self-citationSingle nucleotide variations: Biological impact and theoretical interpretation
Katsonis, P., Koire, A. M., Wilson, S. J., Hsu, T., Lua, R. C., Wilkins, A. D., & Lichtarge, O. (2014) · Protein Science · doi:10.1002/pro.2552 · Cited by 145
Third generation antibody discovery methods: in silico rational design
Sormanni, P., Aprile, F. A., & Vendruscolo, M. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00523k · Cited by 143
Pires, D. E. V., & Ascher, D. B. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw458 · Cited by 134
Racle, J., Guillaume, P., Schmidt, J., Michaux, J., Larabi, A., Lau, K., Perez, M. A. S., Croce, G., Genolet, R., Coukos, G., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.03.009 · Cited by 136
Deep geometric representations for modeling effects of mutations on protein-protein binding affinity
Liu, X., Luo, Y., Li, P., Song, S., & Peng, J. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009284 · Cited by 140
“Based on the mutation information, we used the “buildmodel” function in FoldX (Schymkowitz et al., 2005) to build the 3D structure of the mutated complex, and transformed the wide-type complex and mutated one the trained GNN to obtain meaningful descriptors for the fitting and prediction of the GBT in GraphPPI.”
BackgroundMethodMassively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk
Jia, X., Burugula, B. B., Chen, V., Lemons, R. M., Jayakody, S., Maksutova, M., & Kitzman, J. O. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.12.003 · Cited by 141
RANKL Employs Distinct Binding Modes to Engage RANK and the Osteoprotegerin Decoy Receptor
Nelson, C. A., Warren, J. T., Wang, M. W.-H., Teitelbaum, S. L., & Fremont, D. H. (2012) · Structure · doi:10.1016/j.str.2012.08.030 · Cited by 134
Accurate Prediction of DnaK-Peptide Binding via Homology Modelling and Experimental Data
Durme, J. V., Maurer‐Stroh, S., Gallardo, R. U., Wilkinson, H. R., Rousseau, F., & Schymkowitz, J. W. H. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000475 · Cited by 132
Self-citationRedox Proteomics of Protein-bound Methionine Oxidation
Ghesquière, B., Jonckheere, V., Colaert, N., Durme, J. V., Timmerman, E., Goethals, M., Schymkowitz, J. W. H., Rousseau, F., Vandekerckhove, J. S., & Gevaert, K. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.006866 · Cited by 132
Self-citationLi, X., Yang, Y., Jiang, F., Hu, G., Wan, S., Yan, W., He, X., Xiao, F., Yang, X., Guo, X., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113688 · Cited by 149
Fang, J. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz071 · Cited by 130
Role of distal sites in enzyme engineering
Gu, J., Xu, Y., & Nie, Y. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108094 · Cited by 128
Mutations in PAX2 Associate with Adult-Onset FSGS
Barua, M., Stellacci, E., Stella, L., et al. (2014) · Journal of the American Society of Nephrology · doi:10.1681/asn.2013070686 · Cited by 127
Towards precision medicine: advances in computational approaches for the analysis of human variants
Peterson, T. A., Doughty, E., & Kann, M. G. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.08.008 · Cited by 123
Variation Interpretation Predictors: Principles, Types, Performance, and Choice
Niroula, A., & Vihinen, M. (2016) · Human Mutation · doi:10.1002/humu.22987 · Cited by 127
Mercurio, I., Tragni, V., Busto, F., Grassi, A., & Pierri, C. L. (2020) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03580-1 · Cited by 129
David, A., & Sternberg, M. J. E. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.004 · Cited by 125
van der Zee, J., Ber, I. L., Maurer‐Stroh, S., Engelborghs, S., Gijselinck, I., Camuzat, A., Brouwers, N., Vandenberghe, R. R., Sleegers, K., Hannequin, D., et al. (2007) · Human Mutation · doi:10.1002/humu.9484 · Cited by 124
Self-citationEvolution and dynamics of protein interactions and networks
Levy, E. D., & Pereira‐Leal, J. B. (2008) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2008.03.003 · Cited by 122
Miotto, P., Cabibbe, A. M., Borroni, E., Degano, M., & Cirillo, D. M. (2018) · Journal of Clinical Microbiology · doi:10.1128/jcm.01599-17 · Cited by 125
Notch1 mutations drive clonal expansion in normal esophageal epithelium but impair tumor growth
Abby, E., Dentro, S. C., Hall, M. W. J., Fowler, J. C., Ong, S. H., Sood, R., Herms, A., Piedrafita, G., Abnizova, I., Siebel, C. W., et al. (2023) · Nature Genetics · doi:10.1038/s41588-022-01280-z · Cited by 121
Wu, H., Chen, Q., Zhang, W., & Mu, W. (2021) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2021.1970508 · Cited by 122
Louros, N. N., Konstantoulea, K., Vleeschouwer, M. D., Ramakers, M., Schymkowitz, J. W. H., & Rousseau, F. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz758 · Cited by 122
Self-citationEnhancing the thermostability of Rhizopus chinensis lipase by rational design and MD simulations
Wang, R. Y., Wang, S., Xu, Y., & Yu, X. (2020) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2020.05.243 · Cited by 119
Conformational buffering underlies functional selection in intrinsically disordered protein regions
González-Foutel, N. S., Glavina, J., Borcherds, W. M., Safranchik, M., Barrera-Vilarmau, S., Sagar, A., Estaña, A., Barozet, A., Garrone, N. A., Fernandez-Ballester, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00811-w · Cited by 118
Structural consensus among antibodies defines the antigen binding site
Kunik, V., Peters, B., & Ofran, Y. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002388 · Cited by 117
“Using the FoldX algorithm [21,22], we performed an in-silico alanine scan in which each Paratome-unique residue and each Ag binding residue identified by the CDR identification methods (2707 residues) within the 104 Abs were mutated to Alanine.”
Teng, S., Sobitan, A., Rhoades, R. E., Liu, D., & Tang, Q. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa233 · Cited by 117
Villoutreix, B. O., Kuenemann, M. A., Poyet, J., Bruzzoni‐Giovanelli, H., Labbé, C. M., Lagorce, D., Spérandio, O., & Miteva, M. A. (2014) · Molecular Informatics · doi:10.1002/minf.201400040 · Cited by 118
Machine learning-assisted enzyme engineering
Siedhoff, N. E., Schwaneberg, U., & Davari, M. D. (2020) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2020.05.005 · Cited by 115
Wang, J., Pan, Y., Yang, L., Yang, W., Lv, K., Luo, C., Wang, J., Kuang, G., Wu, W.-C., Gou, Q., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39835-1 · Cited by 119
Patani, H., Bunney, T., Thiyagarajan, N., Norman, R., Ogg, D., Breed, J., Ashford, P., Potterton, A., Edwards, M., Williams, S. V., et al. (2016) · OncoTarget · doi:10.18632/oncotarget.8132 · Cited by 108
Akbar, R., Bashour, H., Rawat, P., Robert, P. A., Smorodina, E., Cotet, T., Karlsen, K. F., Frank, R., Mehta, B. B., Vu, M. H., et al. (2022) · mAbs · doi:10.1080/19420862.2021.2008790 · Cited by 121
Thermodynamics and dynamics of amyloid peptide oligomerization are sequence dependent
Lü, Y., Derreumaux, P., Guo, Z. H., Mousseau, N., & Wei, G. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22305 · Cited by 113
Identification of pathogenic missense mutations using protein stability predictors
Gerasimavicius, L., Liu, X., & Marsh, J. A. (2020) · Scientific Reports · doi:10.1038/s41598-020-72404-w · Cited by 117
PepX: a structural database of non-redundant protein–peptide complexes
Vanhee, P., Reumers, J., Stricher, F., Baeten, L., Serrano, L. G., Schymkowitz, J. W. H., & Rousseau, F. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp893 · Cited by 112
Self-citationBogaerts, V., Nuytemans, K., Reumers, J., Pals, P., Engelborghs, S., Pickut, B. A., Corsmit, E., Peeters, K., Schymkowitz, J. W. H., Deyn, P. P. D., et al. (2008) · Human Mutation · doi:10.1002/humu.20713 · Cited by 111
Self-citationComputational enzyme design approaches with significant biological outcomes: progress and challenges
Li, X.-M., Zhang, Z.-D., & Song, J. (2012) · Computational and Structural Biotechnology Journal · doi:10.5936/csbj.201209007 · Cited by 103
Functional significance of rare neuroligin 1 variants found in autism
Nakanishi, M., Nomura, J., Ji, X., Tamada, K., Arai, T., Takahashi, E., Bućan, M., & Takumi, T. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006940 · Cited by 115
Mátyás, G., Arnold, E., Carrel, T. P., Baumgartner, D., Boileau, C., Berger, W., & Steinmann, B. U. (2006) · Human Mutation · doi:10.1002/humu.20353 · Cited by 111
Proteome Instability Is a Therapeutic Vulnerability in Mismatch Repair-Deficient Cancer
McGrail, D. J., Garnett, J., Yin, J., Dai, H., Shih, D. J. H., Lam, T., Li, Y., Sun, C., Li, Y., Schmandt, R. E., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.01.011 · Cited by 109
SAAMBE-3D: Predicting Effect of Mutations on Protein–Protein Interactions
Pahari, S., Li, G., Murthy, A. K., Liang, S., Fragoza, R., Yu, H., & Alexov, E. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072563 · Cited by 111
FireProt 2.0: web-based platform for the fully automated design of thermostable proteins
Musil, M., Jezik, A., Horackova, J., Borko, S., Kabourek, P., Damborský, J., & Bednář, D. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad425 · Cited by 113
Almeida-Souza, L., Goethals, S., Winter, V. D., et al. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m109.082644 · Cited by 110
Self-citationAutonomous zinc-finger nuclease pairs for targeted chromosomal deletion
Şöllü, C., Pars, K., Cornu, T. I., Thibodeau-Beganny, S., Maeder, M. L., Joung, J. K., Heilbronn, R., & Cathomen, T. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq720 · Cited by 110
Computational tools help improve protein stability but with a solubility tradeoff
Broom, A., Jacobi, Z. E., Trainor, K., & Meiering, E. M. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.m117.784165 · Cited by 113
Robin, G., Sato, Y., Desplancq, D., Rochel, N., Weiss, É., & Martineau, P. (2014) · Journal of Molecular Biology · doi:10.1016/j.jmb.2014.08.013 · Cited by 112
RosettaBackrub--a web server for flexible backbone protein structure modeling and design
Lauck, F., Smith, C. A., Friedland, G. F., Humphris, E. L., & Kortemme, T. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq369 · Cited by 108
Computational design of enzymes for biotechnological applications
Planas-Iglesias, J., Marques, S. M., Pinto, G., Musil, M., Štourač, J., Damborský, J., & Bednář, D. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107696 · Cited by 107
Petukh, M., Li, M., & Alexov, E. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004276 · Cited by 107
“Similarly one can construct a function made of linear combination of weighted terms, either statistically or empirically delivered, to predict binding free energy and the change of it due to mutations [21,35].”
BackgroundHot-spot analysis for drug discovery targeting protein-protein interactions
Rosell, M., & Fernández-Recio, J. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1430763 · Cited by 105
“Some examples of this strategy are HSPred [50], Rosetta [51] and FoldX [52].”
MethodTeruel, N., Mailhot, O., & Najmanovich, R. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009286 · Cited by 99
Masson, P., & Lushchekina, S. V. (2022) · Molecules · doi:10.3390/molecules27206861 · Cited by 118
Medin co-aggregates with vascular amyloid-β in Alzheimer’s disease
Wagner, J., Degenhardt, K., Veit, M., Louros, N., Konstantoulea, K., Skodras, A., Wild, K., Liu, P., Obermüller, U., Bansal, V., et al. (2022) · Nature · doi:10.1038/s41586-022-05440-3 · Cited by 110
Self-citationFerriero, R., Nusco, E., Cegli, R. D., Carissimo, A., Manco, G., & Brunetti‐Pierri, N. (2018) · Journal of Hepatology · doi:10.1016/j.jhep.2018.03.016 · Cited by 103
DLAB-deep learning methods for structure-based virtual screening of antibodies
Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab660 · Cited by 102
“using Foldx5 [32], using the PDB files listed in Supplementary Table 1 as templates 255 and copied the epitope definition for docking purposes from the templates onto the 256”
MethodPires, D. E. V., Chen, J., Blundell, T. L., & Ascher, D. B. (2016) · Scientific Reports · doi:10.1038/srep19848 · Cited by 102
Chemical chaperones assist intracellular folding to buffer mutational variations
Bandyopadhyay, A., Saxena, K., Kasturia, N., Dalal, V., Bhatt, N. R., Rajkumar, A., Maity, S., Sengupta, S., & Chakraborty, K. (2012) · Nature Chemical Biology · doi:10.1038/nchembio.768 · Cited by 101
Ayaz, P., Lyczek, A., Paung, Y., Mingione, V. R., Iacob, R. E., de Waal, P. W., Engen, J. R., Seeliger, M. A., Shan, Y., & Shaw, D. E. (2023) · Nature Communications · doi:10.1038/s41467-023-36956-5 · Cited by 110
Structural evolution of fibril polymorphs during amyloid assembly
Wilkinson, M., Xu, Y., Thacker, D., Taylor, A. I. P., Fisher, D. G., Gallardo, R. U., Radford, S. E., & Ranson, N. A. (2023) · Cell · doi:10.1016/j.cell.2023.11.025 · Cited by 113
Frappier, V., & Najmanovich, R. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003569 · Cited by 104
Wettstein, S., Underhaug, J., Pérez, B., Marsden, B. D., Yue, W. W., Martı́nez, A., & Blau, N. (2014) · European Journal of Human Genetics · doi:10.1038/ejhg.2014.114 · Cited by 101
Rutten, L., Lai, Y., Blokland, S., Truan, D., Bisschop, I. J. M., Strokappe, N. M., Koornneef, A., van Manen, D., Chuang, G., Farney, S. K., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.061 · Cited by 101
Pandurangan, A. P., & Blundell, T. L. (2019) · Protein Science · doi:10.1002/pro.3774 · Cited by 102
Geng, C., Xue, L. C., Roel‐Touris, J., & Bonvin, A. M. J. J. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1410 · Cited by 104
Evolution of protein phosphorylation across 18 fungal species
Studer, R. A., Rodríguez‐Mias, R. A., Haas, K. M., Hsu, J. I., Viéitez, C., Solé, C., Swaney, D. L., Stanford, L. B., Liachko, I., Böttcher, R., et al. (2016) · Science · doi:10.1126/science.aaf2144 · Cited by 96
Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations
Nielsen, S. V., Stein, A., Dinitzen, A. B., Papaleo, E., Tatham, M. H., Poulsen, E. G., Kassem, M. M., Rasmussen, L. J., Lindorff‐Larsen, K., & Hartmann‐Petersen, R. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006739 · Cited by 101
Hashemi, Z. S., Zarei, M., Fath, M. K., Ganji, M., Farahani, M. S., Afsharnouri, F., Pourzardosht, N., Khalesi, B., Jahangiri, A., Rahbar, M. R., et al. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.669431 · Cited by 105
Computational Prediction of Protein Hot Spot Residues
Morrow, J. K., & Zhang, S. (2012) · Current Pharmaceutical Design · doi:10.2174/138161212799436412 · Cited by 97
Sukhwal, A., & Sowdhamini, R. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s25928 · Cited by 96
Stability of domain structures in multi-domain proteins
Bhaskara, R. M., & Srinivasan, N. (2011) · Scientific Reports · doi:10.1038/srep00040 · Cited by 98
Hasan, M. R., Alsaiari, A. A., Fakhurji, B., Molla, M. H. R., Asseri, A. H., Sumon, M. A. A., Park, M. N., Ahammad, F., & Kim, B. (2022) · Molecules · doi:10.3390/molecules27134169 · Cited by 106
Rakoczy, E. P., Kiel, C., McKeone, R., Stricher, F., & Serrano, L. G. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.11.003 · Cited by 101
Self-citationHyaluronan modulates TRPV1 channel opening, reducing peripheral nociceptor activity and pain
Caires, R., Luis, E., Taberner, F. J., Fernandez-Ballester, G. J., Ferrer-Montiel, A. V., Balazs, E. A., Gomis, A., Belmonte, C., & de la Peña, E. (2015) · Nature Communications · doi:10.1038/ncomms9095 · Cited by 103
Kunik, V., & Ofran, Y. (2013) · Protein Engineering Design and Selection · doi:10.1093/protein/gzt027 · Cited by 96
“We used the FoldX algorithm (Guerois et al., 2002; Schymkowitz et al., 2005), to assess DDG effects of mutations to alanine.”
MethodBeaver, S. K., Mesa‐Torres, N., Pey, A. L., & Timson, D. J. (2019) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2019.05.002 · Cited by 96
Selection of Oncogenic Mutant Clones in Normal Human Skin Varies with Body Site
Fowler, J. C., King, C., Bryant, C., Hall, M. W. J., Sood, R. K., Ong, S. H., Earp, E., Fernández‐Antorán, D., Koeppel, J., Dentro, S. C., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1092 · Cited by 95
Mutations in RIPK4 Cause the Autosomal-Recessive Form of Popliteal Pterygium Syndrome
Kalay, E., Sezgin, O., Chellappa, V., Mutlu, M., Morsy, H., Kayserili, H., Kreiger, E., Cansu, A., Toraman, B., Abdalla, E. M., et al. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.014 · Cited by 98
Structural Basis of Egg Coat-Sperm Recognition at Fertilization
Raj, I., Al-Hosseini, H. S., Dioguardi, E., Nishimura, K., Han, L., Villa, A., de Sanctis, D., & Jovine, L. (2017) · Cell · doi:10.1016/j.cell.2017.05.033 · Cited by 94
Xu, P., Ni, Z., Zong, M., et al. (2020) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2020.02.033 · Cited by 94
Usmanova, D. R., Bogatyreva, N. S., Ariño Bernad, J., Eremina, A. A., Gorshkova, A. A., Kanevskiy, G. M., Lonishin, L. R., Meister, A. V., Yakupova, A. G., Kondrashov, F. A., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty340 · Cited by 93
AS-48 bacteriocin: close to perfection
Sánchez‐Hidalgo, M., Montalbán‐López, M., Cebrián, R., Valdivia, E., Martínez‐Bueno, M., & Maqueda, M. (2011) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-011-0724-4 · Cited by 93
Gating mechanism of the extracellular entry to the lipid pathway in a TMEM16 scramblase
Lee, B., Khelashvili, G., Falzone, M. E., Menon, A. K., Weinstein, H., & Accardi, A. (2018) · Nature Communications · doi:10.1038/s41467-018-05724-1 · Cited by 93
Bentzen, A. K., Such, L., Jensen, K. K., Marquard, A. M., Jessen, L. E., Miller, N. J., Church, C. D., Lyngaa, R., Koelle, D. M., Becker, J. C., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4303 · Cited by 94
A resource of variant effect predictions of single nucleotide variants in model organisms
Wagih, O., Busby, B. P., Galardini, M., Memon, D., Typas, A., & Beltrão, P. (2018) · Molecular Systems Biology · doi:10.15252/msb.20188430 · Cited by 97
An immune receptor complex evolved in soybean to perceive a polymorphic bacterial flagellin
Wei, Y., Balaceanu, A., Rufián, J. S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17573-y · Cited by 93
Sullivan, B. J., Nguyen, T., Durani, V., Mathur, D., Rojas, S., Thomas, M., Syu, T., & Magliery, T. J. (2012) · Journal of Molecular Biology · doi:10.1016/j.jmb.2012.04.025 · Cited by 93
Kim, Y., Cheon, S., Min, C.-K., Sohn, K. M., Kang, Y. J., Cha, Y., Kang, J.-I., Han, S. K., Ha, N.-Y., Kim, G., et al. (2016) · mBio · doi:10.1128/mbio.00019-16 · Cited by 92
Enzyme Hydration Determines Resistance in Organic Cosolvents
Cui, H., Zhang, L., Eltoukhy, L., et al. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c03233 · Cited by 92
Distant residues modulate conformational opening in SARS-CoV-2 spike protein
Ray, D., Le, L., & Andricioaei, I. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2100943118 · Cited by 89
Pathophysiology of chikungunya virus infection associated with fatal outcomes
de Souza, W. M., Fumagalli, M. J., de Lima, S. T. S., Parise, P. L., Carvalho, D. C. M., Hernandez, C., de Jesus, R., Delafiori, J., Cândido, D. D. S., Carregari, V. C., et al. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.02.011 · Cited by 96
Predicting and Experimentally Validating Hot-Spot Residues at Protein–Protein Interfaces
Ibarra, A. Á., Bartlett, G. J., Hegedüs, Z., Dutt, S., Hóbor, F., Horner, K. A., Hetherington, K., Spence, K., Nelson, A. S., Edwards, T. A., et al. (2019) · ACS Chemical Biology · doi:10.1021/acschembio.9b00560 · Cited by 90
Structure of the Dimerization Domain of the Rabies Virus Phosphoprotein
Ivanov, I., Crépin, T., Jamin, M., et al. (2010) · Journal of Virology · doi:10.1128/jvi.02557-09 · Cited by 91
Weetman, D., Wilding, C. S., Neafsey, D. E., Müller, P., Ochomo, E., Isaacs, A. T., Steen, K., Rippon, E. J., Morgan, J. C., Mawejje, H. D., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-21265-5 · Cited by 82
Jansen, S., Anastassis Perrakis, A., Ulens, C., Winkler, C., Andries, M., Joosten, R. P., Acker, M. V., Luyten, F. P., Moolenaar, W. H., & Bollen, M. (2012) · Structure · doi:10.1016/j.str.2012.09.001 · Cited by 92
Computational analysis of missense mutations causing Snyder-Robinson syndrome
Zhang, Z., Teng, S., Wang, L., Schwartz, C. E., & Alexov, E. G. (2010) · Human Mutation · doi:10.1002/humu.21310 · Cited by 90
Predicting folding free energy changes upon single point mutations
Zhang, Z., Wang, L., Gao, Y., Zhang, J., Zhenirovskyy, M., & Alexov, E. G. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts005 · Cited by 90
Louros, N. N., Orlando, G., Vleeschouwer, M. D., Rousseau, F., & Schymkowitz, J. W. H. (2020) · Nature Communications · doi:10.1038/s41467-020-17207-3 · Cited by 91
Self-citationUnderstanding activity-stability tradeoffs in biocatalysts by enzyme proximity sequencing
Vanella, R., Küng, C., Schoepfer, A. A., Doffini, V., Ren, J., & Nash, M. A. (2024) · Nature Communications · doi:10.1038/s41467-024-45630-3 · Cited by 91
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
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