The DOI matches its Crossref record; 118 of the 118 references it cites were found in a registry or an index.
Hot-spot analysis for drug discovery targeting protein-protein interactions
Open accessAbstract
INTRODUCTION: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“However, abnormalities in PPIs can result in a various diseases, such as cancers, infectious diseases, and neurodegenerative diseases.306–308”
From Recent advances in targeting the “undruggable” proteins: from drug discovery to clinical trials (2023) · cites this paper as [308]
Citations
Cites
Protein folding and misfolding
Dobson, C. M. (2003) · Nature · doi:10.1038/nature02261 · Cited by 4,791
From molecular to modular cell biology.
Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999) · Nature · doi:10.1038/35011540 · Cited by 3,632
Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701361104 · Cited by 3,242
HADDOCK: A Protein−Protein Docking Approach Based on Biochemical or Biophysical Information
Dominguez, C., Boelens, R., & Bonvin, A. M. J. J. (2003) · Journal of the American Chemical Society · doi:10.1021/ja026939x · Cited by 3,187
PatchDock and SymmDock: servers for rigid and symmetric docking
Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., & Wolfson, H. J. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki481 · Cited by 3,018
Towards a proteome-scale map of the human protein–protein interaction network
Rual, J.-F. M., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G. F., Gibbons, F. D., Dreze, M., Ayivi-Guedehoussou, N., et al. (2005) · Nature · doi:10.1038/nature04209 · Cited by 2,904
The FoldX web server: an online force field
Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki387 · Cited by 2,759 · cited 2×
“Some examples of this strategy are HSPred [50], Rosetta [51] and FoldX [52].”
MethodPhase transitions in the assembly of multivalent signalling proteins
Li, P., Banjade, S., Cheng, H., Kim, S., Chen, B., Guo, L., Llaguno, M. C., Hollingsworth, J. V., King, D., Banani, S. F., et al. (2012) · Nature · doi:10.1038/nature10879 · Cited by 2,565
A comprehensive map of molecular drug targets
Santos, R., Ursu, O., Gaulton, A., Bento, A. P., Donadi, R. S., Bologa, C. G., Karlsson, A., Al-Lazikani, B., Hersey, A., Oprea, T. I., et al. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.230 · Cited by 2,427
A geometric approach to macromolecule-ligand interactions
Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., & Ferrin, T. E. (1982) · Journal of Molecular Biology · doi:10.1016/0022-2836(82)90153-x · Cited by 2,287
Reaching for high-hanging fruit in drug discovery at protein–protein interfaces
Wells, J. A., & McClendon, C. L. (2007) · Nature · doi:10.1038/nature06526 · Cited by 2,002
Anatomy of hot spots in protein interfaces
Bogan, A. A., & Thorn, K. S. (1998) · Journal of Molecular Biology · doi:10.1006/jmbi.1998.1843 · Cited by 1,988
A Hot Spot of Binding Energy in a Hormone-Receptor Interface
Clackson, T., & Wells, J. A. (1995) · Science · doi:10.1126/science.7529940 · Cited by 1,987
Interactome Networks and Human Disease
Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771
Fpocket: An open source platform for ligand pocket detection
Guilloux, V. L., Schmidtke, P., & Tufféry, P. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-168 · Cited by 1,724
ZDOCK: An initial‐stage protein‐docking algorithm
Chen, R., Li, L., & Weng, Z. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10389 · Cited by 1,393
A Proteome-Scale Map of the Human Interactome Network
Rolland, T., Taşan, M., Charloteaux, B., Pevzner, S. J., Zhong, Q., Sahni, N., Yi, S. S., Lemmens, I. H., Fontanillo, C., Mosca, R., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.050 · Cited by 1,381
Katchalski-Katzir, E., Shariv, I., Eisenstein, M., Friesem, A., Aflalo, C., & Vakser, I. (1992) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.89.6.2195 · Cited by 1,143
Small molecules, big targets: drug discovery faces the protein–protein interaction challenge
Scott, D. E., Bayly, A. R., Abell, C., & Skidmore, J. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.29 · Cited by 1,137
Small-Molecule Inhibitors of Protein-Protein Interactions: Progressing toward the Reality
Arkin, M. R., Tang, Y., & Wells, J. A. (2014) · Chemistry & Biology · doi:10.1016/j.chembiol.2014.09.001 · Cited by 1,065
Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites
Laurie, A. T. R., & Jackson, R. M. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti315 · Cited by 1,049
An empirical framework for binary interactome mapping
Venkatesan, K., Rual, J.-F. M., Vázquez, A., Stelzl, U., Lemmens, I. H., Hirozane-Kishikawa, T., Hao, T., Zenkner, M., Xin, X., Goh, K. I., et al. (2008) · Nature Methods · doi:10.1038/nmeth.1280 · Cited by 925
Gabb, H. A., Jackson, R. M., & Sternberg, M. J. E. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1203 · Cited by 820
A simple physical model for binding energy hot spots in protein–protein complexes
Kortemme, T., & Baker, D. A. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202485799 · Cited by 818
FireDock: Fast interaction refinement in molecular docking
Andrusier, N., Nussinov, R., & Wolfson, H. J. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21495 · Cited by 679
Widespread 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
The RosettaDock server for local protein-protein docking
Lyskov, S., & Gray, J. J. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn216 · Cited by 593
Fast prediction and visualization of protein binding pockets with PASS
Brady, G. P., & Stouten, P. F. W. (2000) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008124202956 · Cited by 532
CAVER: a new tool to explore routes from protein clefts, pockets and cavities
Petřek, M., Otyepka, M., Banáš, P., Košinová, P., Koča, J., & Damborský, J. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-316 · Cited by 523
Interactome3D: adding structural details to protein networks
Mosca, R., Céol, A., & Aloy, P. (2013) · Nature Methods · doi:10.1038/nmeth.2289 · Cited by 479
ZRANK: Reranking protein docking predictions with an optimized energy function
Pierce, B., & Weng, Z. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21373 · Cited by 468
Neuvirth, H., Raz, R., & Schreiber, G. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.02.040 · Cited by 436
Drug discovery beyond the ‘rule-of-five’
Zhang, M., & Wilkinson, B. (2007) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2007.10.005 · Cited by 421
Assembly reflects evolution of protein complexes
Levy, E. D., Erba, E. B., Robinson, C. V., & Teichmann, S. A. (2008) · Nature · doi:10.1038/nature06942 · Cited by 420
MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories
Schmidtke, P., Bidon‐Chanal, A., Luque, F. J., & Barril, X. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr550 · Cited by 398
Three-dimensional reconstruction of protein networks provides insight into human genetic disease
Wang, X., Wei, X., Thijssen, B., Das, J., Lipkin, S. M., & Yu, H. (2012) · Nature Biotechnology · doi:10.1038/nbt.2106 · Cited by 385
Widespread reorganization of metabolic enzymes into reversible assemblies upon nutrient starvation
Narayanaswamy, R., Levy, M., Tsechansky, M., Stovall, G. M., O’Connell, J. D., Mirrielees, J. A., Ellington, A. D., & Marcotte, E. M. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0812771106 · Cited by 368
Network‐based Approaches in Pharmacology
Boezio, B., Audouze, K., Ducrot, P., & Taboureau, O. (2017) · Molecular Informatics · doi:10.1002/minf.201700048 · Cited by 364
Improved prediction of protein-protein binding sites using a support vector machines approach
Bradford, J. R., & Westhead, D. R. (2004) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti242 · Cited by 363
pyDock: Electrostatics and desolvation for effective scoring of rigid‐body protein–protein docking
Cheng, T. M. K., Blundell, T. L., & Fernández‐Recio, J. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21419 · Cited by 341
Self-citationPredicting druggable binding sites at the protein–protein interface
Fuller, J. C., Burgoyne, N. J., & Jackson, R. M. (2008) · Drug Discovery Today · doi:10.1016/j.drudis.2008.10.009 · Cited by 311
Noree, C., Sato, B. K., Broyer, R. M., & Wilhelm, J. E. (2010) · Journal of Cell Biology · doi:10.1083/jcb.201003001 · Cited by 299
Cimermančič, P., Weinkam, P., Rettenmaier, T. J., Bichmann, L., Keedy, D. A., Woldeyes, R. A., Schneidman‐Duhovny, D., Demerdash, O., Mitchell, J. C., Wells, J. A., et al. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.01.029 · Cited by 284
Moal, I. H., & Fernández-Recio, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts489 · Cited by 281
Self-citationChen, H., & Zhou, H. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20514 · Cited by 279
Identification of Protein–Protein Interaction Sites from Docking Energy Landscapes
Fernández‐Recio, J., Totrov, M. M., & Abagyan, R. A. (2003) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.10.069 · Cited by 274
Self-citationA simple definition of structural regions in proteins and its use in analyzing interface evolution
Levy, E. D. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.09.028 · Cited by 254
Proteins evolve on the edge of supramolecular self-assembly
Garcia-Seisdedos, H., Empereur-Mot, C., Elad, N., & Levy, E. D. (2017) · Nature · doi:10.1038/nature23320 · Cited by 253
Structural conservation of druggable hot spots in protein–protein interfaces
Kozakov, D., Hall, D. R., Chuang, G., Cencic, R., Brenke, R., Grove, L. E., Beglov, D., Pelletier, J., Whitty, A., & Vajda, S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1101835108 · Cited by 245
Yates, C. M., & Sternberg, M. J. E. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.012 · Cited by 241
Protein binding site prediction using an empirical scoring function
Liang, S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl454 · Cited by 237
Druggable protein–protein interactions – from hot spots to hot segments
London, N., Raveh, B., & Schueler‐Furman, O. (2013) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2013.10.011 · Cited by 229
Docking and scoring protein interactions: CAPRI 2009
Lensink, M. F., & Wodak, S. J. (2010) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22818 · Cited by 226
Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins
Kucukkal, T. G., Petukh, M. G., Li, L., & Alexov, E. G. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.01.003 · Cited by 220
Transient Pockets on Protein Surfaces Involved in Protein−Protein Interaction
Eyrisch, S., & Helms, V. (2007) · Journal of Medicinal Chemistry · doi:10.1021/jm070095g · Cited by 212
HotSpot Wizard: a web server for identification of hot spots in protein engineering
Pavelka, A., Chovancová, E., & Damborský, J. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp410 · Cited by 199
Hot spots in protein–protein interfaces: Towards drug discovery
Cukuroglu, E., Engin, H. B., Gürsoy, A., & Keskin, Ö. (2014) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2014.06.003 · Cited by 184
Expanding the Number of ‘Druggable’ Targets: Non‐Enzymes and Protein–Protein Interactions
Makley, L. N., & Gestwicki, J. E. (2012) · Chemical Biology & Drug Design · doi:10.1111/cbdd.12066 · Cited by 183
A network medicine approach to human disease
Zanzoni, A., Soler‐López, M., & Aloy, P. (2009) · FEBS Letters · doi:10.1016/j.febslet.2009.03.001 · Cited by 172
Protein-protein interaction sites are hot spots for disease-associated nonsynonymous SNPs
David, A., Razali, R., Wass, M. N., & Sternberg, M. J. E. (2011) · Human Mutation · doi:10.1002/humu.21656 · Cited by 169
Higueruelo, A. P., Schreyer, A. M., Bickerton, G. R., Pitt, W. R., Groom, C. R., & Blundell, T. L. (2009) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2009.00889.x · Cited by 166
Stabilization of protein-protein interactions in drug discovery
Andrei, S. A., Sijbesma, E., Hann, M. M., Davis, J., O’Mahony, G., Perry, M. W. D., Karawajczyk, A., Eickhoff, J., Brunsveld, L., Doveston, R. G., et al. (2017) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2017.1346608 · Cited by 166
The binding interface database (BID): a compilation of amino acid hot spots in protein interfaces
Fischer, T. B., Arunachalam, K. V., Bailey, D., Mangual, V., Bakhru, S., Russo, R., Huang, D. S., Paczkowski, M., Lalchandani, V., Ramachandra, C., et al. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg163 · Cited by 164
Flexibility and small pockets at protein–protein interfaces: New insights into druggability
Jubb, H. C., Blundell, T. L., & Ascher, D. B. (2015) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2015.01.009 · Cited by 157
SwarmDock and the Use of Normal Modes in Protein-Protein Docking
Moal, I. H., & Bates, P. A. (2010) · International Journal of Molecular Sciences · doi:10.3390/ijms11103623 · Cited by 154
2P2Idb: a structural database dedicated to orthosteric modulation of protein–protein interactions
Basse, M. J., Betzi, S., Bourgeas, R., Bouzidi, S., Chetrit, B., Hamon, V., Morelli, X., & Roche, P. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1002 · Cited by 148
Hot spot-based design of small-molecule inhibitors for protein–protein interactions
Guo, W., Wisniewski, J. A., & Ji, H. (2014) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2014.03.095 · Cited by 147
Exploring the human diseasome: the human disease network
Goh, K.-I., & Choi, I. (2012) · Briefings in Functional Genomics · doi:10.1093/bfgp/els032 · Cited by 146
Pizzuti, C., & Rombo, S. E. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu034 · Cited by 139
LightDock: a new multi-scale approach to protein–protein docking
Jiménez‐García, B., Roel‐Touris, J., Romero‐Durana, M., Vidal, M., Jiménez-González, D., & Fernández‐Recio, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx555 · Cited by 139
Self-citationA brief history of novel drug discovery technologies
Gershell, L. J., & Atkins, J. H. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1064 · Cited by 129
Pathway and network analysis in proteomics
Wu, X., Hasan, M. A., & Chen, J. Y. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.05.031 · Cited by 128
Metz, A., Pfleger, C., Kopitz, H., Pfeiffer‐Marek, S., Baringhaus, K. H., & Gohlke, H. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200322s · Cited by 128
David, A., & Sternberg, M. J. E. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.004 · Cited by 125
Modeling Effects of Human Single Nucleotide Polymorphisms on Protein-Protein Interactions
Teng, S., Madej, T., Panchenko, A. R., & Alexov, E. G. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2008.12.3904 · Cited by 125
Identification of hot-spot residues in protein-protein interactions by computational docking
Grosdidier, S., & Fernández‐Recio, J. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-447 · Cited by 123
Self-citationWinter, A., Higueruelo, A. P., Marsh, M., Sigurdardottir, A., Pitt, W. R., & Blundell, T. L. (2012) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583512000108 · Cited by 119
Labbé, C. M., Laconde, G., Kuenemann, M. A., Villoutreix, B. O., & Sperandio, O. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.05.003 · Cited by 104
Computational Prediction of Protein Hot Spot Residues
Morrow, J. K., & Zhang, S. (2012) · Current Pharmaceutical Design · doi:10.2174/138161212799436412 · Cited by 97
Koes, D. R., & Camacho, C. J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks336 · Cited by 96
dSysMap: exploring the edgetic role of disease mutations
Mosca, R., Tenorio‐Laranga, J., Olivella, R., Alcalde, V., Ceol, A. G. M., Soler‐López, M., & Aloy, P. (2015) · Nature Methods · doi:10.1038/nmeth.3289 · Cited by 96
Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines
Lise, S., Buchan, D. W. A., Pontil, M., & Jones, D. T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016774 · Cited by 94
Johnson, D. K., & Karanicolas, J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002951 · Cited by 92
In silico structure-based approaches to discover protein-protein interaction-targeting drugs
Shin, W., Christoffer, C. W., & Kihara, D. (2017) · Methods · doi:10.1016/j.ymeth.2017.08.006 · Cited by 89
Li, H., Liu, A., Zhao, Z., Xu, Y. F., Lin, J., Jou, D., & Li, C. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm101330h · Cited by 85
Analyzing Protein Interaction Networks Using Structural Information
Kiel, C., Beltrão, P., & Serrano, L. G. (2008) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.77.062706.133317 · Cited by 85
Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces
Engin, H. B., Kreisberg, J. F., & Carter, H. K. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152929 · Cited by 84
Assi, S. A., Tanaka, T. U., Rabbitts, T. H., & Fernández‐Fuentes, N. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp1158 · Cited by 83
Context-Based Identification of Protein-Protein Interfaces and “Hot-Spot” Residues
Geppert, T., Hoy, B., Wessler, S., & Schneider, G. (2011) · Chemistry & Biology · doi:10.1016/j.chembiol.2011.01.005 · Cited by 80
CCharPPI web server: computational characterization of protein–protein interactions from structure
Moal, I. H., Jiménez‐García, B., & Fernández‐Recio, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu594 · Cited by 78
Self-citationBai, F., Morcos, F., Cheng, R. R., Jiang, H., & Onuchic, J. N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615932113 · Cited by 77
Small-Molecule Inhibitors of IL-2/IL-2R: Lessons Learned and Applied
Wilson, C. G. M., & Arkin, M. R. (2010) · Current topics in microbiology and immunology · doi:10.1007/82_2010_93 · Cited by 74
Structure‐energy‐based predictions and network modelling of RASopathy and cancer missense mutations
Kiel, C., & Serrano, L. G. (2014) · Molecular Systems Biology · doi:10.1002/msb.20145092 · Cited by 67
Pons, C., Talavera, D., de la Cruz, X., Orozco, M., & Fernández‐Recio, J. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci100353e · Cited by 64
Self-citationEstablished and Emerging Trends in Computational Drug Discovery in the Structural Genomics Era
Taboureau, O., Baell, J. B., Fernández‐Recio, J., & Villoutreix, B. O. (2012) · Chemistry & Biology · doi:10.1016/j.chembiol.2011.12.007 · Cited by 63
Self-citationiPPI-DB: an online database of modulators of protein–protein interactions
Labbé, C. M., Kuenemann, M. A., Zarzycka, B. A., Vriend, G., Nicolaes, G. A. F., Lagorce, D., Miteva, M. A., Villoutreix, B. O., & Spérandio, O. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv982 · Cited by 60
AlQuraishi, M., Koytiger, G., Jenney, A., MacBeath, G., & Sorger, P. K. (2014) · Nature Genetics · doi:10.1038/ng.3138 · Cited by 59
Prediction of protein binding sites and hot spots
Fernández‐Recio, J. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.45 · Cited by 58
Self-citationSunaga, N., Kaira, K., Tomizawa, Y., Shimizu, K., Imai, H., Takahashi, G., Kakegawa, S., Ohtaki, Y., Nagashima, T., Kasahara, N., et al. (2014) · British Journal of Cancer · doi:10.1038/bjc.2014.110 · Cited by 47
From Systems to Structure: Bridging Networks and Mechanism
Fraser, J. S., Gross, J. D., & Krogan, N. J. (2013) · Molecular Cell · doi:10.1016/j.molcel.2013.01.003 · Cited by 47
Janjić, V., & Pržulj, N. (2012) · Molecular BioSystems · doi:10.1039/c2mb25230a · Cited by 44
Vázquez, M., Valencia, A., & Pons, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv142 · Cited by 42
Stockwell, B. R., & Schreiber, S. L. (1998) · Chemistry & Biology · doi:10.1016/s1074-5521(98)90072-2 · Cited by 41
Geppert, T., Bauer, S., Hiss, J. A., Conrad, E., Reutlinger, M., Schneider, P., Weisel, M., Pfeiffer, B., Altmann, K., Waibler, Z., et al. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201105901 · Cited by 40
Engin, H. B., Güney, E., Keskin, Ö., Oliva, B., & Gürsoy, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081035 · Cited by 39
Computational Method To Identify Druggable Binding Sites That Target Protein–Protein Interactions
Li, H., Kasam, V., Tautermann, C. S., Seeliger, D., & Vaidehi, N. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400750x · Cited by 30
Pérot, S., Regad, L., Reynès, C., Spérandio, O., Miteva, M. A., Villoutreix, B. O., & Camproux, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0063730 · Cited by 26
Voltage sensor of ion channels and enzymes
Gonzalez, C. L., Contreras, G. F., Peyser, A., Larsson, H. P., Neely, A., & Latorre, R. (2011) · Biophysical Reviews · doi:10.1007/s12551-011-0061-8 · Cited by 23
Halakou, F., Sen-Kilic, E., Cukuroglu, E., Keskin, Ö., & Gürsoy, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-07351-0 · Cited by 21
PCRPi - DB: a database of computationally annotated hot spots in protein interfaces
Segura, J., & Fernández‐Fuentes, N. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1068 · Cited by 20
ECMIS: computational approach for the identification of hotspots at protein-protein interfaces
Shingate, P., Malini, M., Sukhwal, A., & Sowdhamini, R. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-303 · Cited by 18
Holland, D. O., Shapiro, B. H., Xue, P., & Johnson, M. E. (2017) · Scientific Reports · doi:10.1038/s41598-017-05686-2 · Cited by 18
Fresh from the biotech pipeline—2016
Morrison, C. (2017) · Nature Biotechnology · doi:10.1038/nbt.3783 · Cited by 17
MOLFIT: A computer program for molecular superposition
Redington, P. K. (1992) · Computers & Chemistry · doi:10.1016/0097-8485(92)80005-k · Cited by 14
Vellani, V., & Giacomoni, C. (2017) · The Scientific World JOURNAL · doi:10.1155/2017/3595903 · Cited by 11
Barradas‐Bautista, D., & Fernández‐Recio, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183643 · Cited by 10
Self-citationGraph-Based Clustering of Predicted Ligand-Binding Pockets on Protein Surfaces
Degac, J., Winter, U., & Helms, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00045 · Cited by 8
Krüger, D., Garzón, J., Montes, P., & Gohlke, H. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-s1-p36 · Cited by 4
Research works printed but not linked: 1
- Wilson CGM, 2011, Curr Top Microbiol Immunol, 348, 25
Cited by
Sorted by citations
Lu, H., Zhou, Q., He, J., Jiang, Z., Peng, C., Tong, R., & Shi, J. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00315-3 · Cited by 891
Recent advances in targeting the “undruggable” proteins: from drug discovery to clinical trials
Xie, X., Yu, T., Li, X., Zhang, N., Foster, L. J., Peng, C., Huang, W., & He, G. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01589-z · Cited by 396
New insights into protein–protein interaction modulators in drug discovery and therapeutic advance
Nada, H., Choi, Y., Kim, S.-D., Jeong, K. S., Meanwell, N. A., & Lee, K. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-02036-3 · Cited by 241
Aromatic clusters in protein–protein and protein–drug complexes
Lanzarotti, E., Defelipe, L. A., Marti, M. A., & Turjanski, A. G. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00437-4 · Cited by 102
BAlaS: fast, interactive and accessible computational alanine-scanning using BudeAlaScan
Wood, C. W., Ibarra, A. Á., Bartlett, G. J., Wilson, A. J., Woolfson, D. N., & Sessions, R. B. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa026 · Cited by 75
Metadata
- Authors
- Rosell, M., & Fernández-Recio, J.
- Affiliations
- Barcelona Supercomputing Center (ES)
- Department of Life Sciences, Barcelona Supercomputing Center (BSC), Barcelona, Spain
- ORCID
- Rosell, Mireia
- Venue
- Expert Opinion on Drug Discovery
- Publisher
- Informa UK Limited
- Published
- 2018-01-29
- Volume
- 13
- Issue
- 4
- Pages
- 327-338
- ISSN
- 1746-0441 (ISSN-L)
- 1746-045X
- OpenAlex
- W2791454012
- Cited by
- 105
- References
- 118
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 19 · 2023: 19 · 2022: 12 · 2021: 9 · 2020: 16 · 2019: 8 · 2018: 1
- FWCI
- 0.66
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- Expert Opinion on Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion, other-oa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (acceptedVersion, other-oa)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, public-domain)
- Funders
- the Spanish Ministry of Economy, Industry and Competitiveness (EFA086/15)
- EU Interreg V POCTEFA
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Keywords
- protein-protein interaction, hot spot residues, protein-protein interfaces, small molecule inhibitors, rational drug design, molecular docking, molecular dynamics, mutation analysis
- MeSH terms
- Humans, Models, Molecular, Proteins, Drug Design, Computational Biology, Drug Discovery, Molecular Dynamics Simulation, Molecular Docking Simulation
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