Hot-spot analysis for drug discovery targeting protein-protein interactions

Protein folding and misfolding

Dobson, C. M. (2003) · Nature · doi:10.1038/nature02261 · Cited by 4,791 · see citing works

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 · see citing works

The human disease network

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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× · see citing works

Phase 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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 1,963 · see citing works

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 · see citing works

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 1,666 · see citing works

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 · see citing works

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 1,342 · see citing works

Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques.

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 907 · see citing works

Modelling protein docking using shape complementarity, electrostatics and biochemical information 1 1Edited by J. Thornton

Gabb, H. A., Jackson, R. M., & Sternberg, M. J. E. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1203 · Cited by 820 · see citing works

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 801 · see citing works

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 680 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Interactome3D: adding structural details to protein networks

Mosca, R., Céol, A., & Aloy, P. (2013) · Nature Methods · doi:10.1038/nmeth.2289 · Cited by 479 · see citing works

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 · see citing works

ProMate: A Structure Based Prediction Program to Identify the Location of Protein–Protein Binding Sites

Neuvirth, H., Raz, R., & Schreiber, G. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.02.040 · Cited by 436 · see citing works

Drug discovery beyond the ‘rule-of-five’

Zhang, M., & Wilkinson, B. M. (2007) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2007.10.005 · Cited by 421 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Network‐based Approaches in Pharmacology

Boezio, B., Audouze, K., Ducrot, P., & Taboureau, O. (2017) · Molecular Informatics · doi:10.1002/minf.201700048 · Cited by 364 · see citing works

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 · see citing works

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 345 · see citing works

Predicting 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 · see citing works

Identification of novel filament-forming proteins in Saccharomyces cerevisiae and Drosophila melanogaster

Noree, C., Sato, B. K., Broyer, R. M., & Wilhelm, J. E. (2010) · Journal of Cell Biology · doi:10.1083/jcb.201003001 · Cited by 299 · see citing works

Prediction of interface residues in protein–protein complexes by a consensus neural network method: Test against NMR data

Chen, H., & Zhou, H. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20514 · Cited by 281 · see citing works

SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models

Moal, I. H., & Fernández-Recio, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts489 · Cited by 281 · see citing works

CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites

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 277 · see citing works

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 · see citing works

A 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 · see citing works

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 · see citing works

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 · see citing works

The Effects of Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) on Protein–Protein Interactions

Yates, C. M., & Sternberg, M. J. E. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.012 · Cited by 239 · see citing works

Protein binding site prediction using an empirical scoring function

Liang, S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl454 · Cited by 237 · see citing works

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 · see citing works

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 224 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 177 · see citing works

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 175 · see citing works

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 170 · see citing works

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 · see citing works

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 · see citing works

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 162 · see citing works

Atomic Interactions and Profile of Small Molecules Disrupting Protein–Protein Interfaces: the TIMBAL Database

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 155 · see citing works

SwarmDock and the Use of Normal Modes in Protein-Protein Docking

Moal, I., & Bates, P. (2010) · International Journal of Molecular Sciences · doi:10.3390/ijms11103623 · Cited by 154 · see citing works

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 154 · see citing works

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 · see citing works

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 142 · see citing works

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 139 · see citing works

Algorithms and tools for protein–protein interaction networks clustering, with a special focus on population-based stochastic methods

Pizzuti, C., & Rombo, S. E. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu034 · Cited by 139 · see citing works

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 · see citing works

A 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 · see citing works

The Contribution of Missense Mutations in Core and Rim Residues of Protein–Protein Interfaces to Human Disease

David, A., & Sternberg, M. J. E. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.004 · Cited by 125 · see citing works

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 · see citing works

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 123 · see citing works

Hot Spots and Transient Pockets: Predicting the Determinants of Small-Molecule Binding to a Protein–Protein Interface

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 123 · see citing works

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 120 · see citing works

Biophysical and computational fragment-based approaches to targeting protein–protein interactions: applications in structure-guided drug discovery

Winter, 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 115 · see citing works

iPPI-DB: a manually curated and interactive database of small non-peptide inhibitors of protein–protein interactions

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 · see citing works

Computational Prediction of Protein Hot Spot Residues

Morrow, J. K., & Zhang, S. (2012) · Current Pharmaceutical Design · doi:10.2174/138161212799436412 · Cited by 98 · see citing works

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 · see citing works

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 · see citing works

Druggable Protein Interaction Sites Are More Predisposed to Surface Pocket Formation than the Rest of the Protein Surface

Johnson, D. K., & Karanicolas, J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002951 · Cited by 92 · see citing works

PocketQuery: protein-protein interaction inhibitor starting points from protein-protein interaction structure

Koes, D. R., & Camacho, C. J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks336 · Cited by 91 · see citing works

Fragment-Based Drug Design and Drug Repositioning Using Multiple Ligand Simultaneous Docking (MLSD): Identifying Celecoxib and Template Compounds as Novel Inhibitors of Signal Transducer and Activator of Transcription 3 (STAT3)

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 · see citing works

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 · see citing works

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 · see citing works

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 84 · see citing works

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 · see citing works

PCRPi: Presaging Critical Residues in Protein interfaces, a new computational tool to chart hot spots in protein interfaces

Assi, S. A., Tanaka, T. U., Rabbitts, T. H., & Fernández‐Fuentes, N. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp1158 · Cited by 80 · see citing works

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 · see citing works

Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

Bai, 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 76 · see citing works

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 70 · see citing works

Scoring by Intermolecular Pairwise Propensities of Exposed Residues (SIPPER): A New Efficient Potential for Protein−Protein Docking

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 66 · see citing works

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 64 · see citing works

Established 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 · see citing works

iPPI-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 61 · see citing works

A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks

AlQuraishi, M., Koytiger, G., Jenney, A., MacBeath, G., & Sorger, P. K. (2014) · Nature Genetics · doi:10.1038/ng.3138 · Cited by 59 · see citing works

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 56 · see citing works

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 46 · see citing works

Clinicopathological and prognostic significance of interleukin-8 expression and its relationship to KRAS mutation in lung adenocarcinoma

Sunaga, 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 45 · see citing works

The Core Diseasome

Janjić, V., & Pržulj, N. (2012) · Molecular BioSystems · doi:10.1039/c2mb25230a · Cited by 44 · see citing works

Structure-PPi: a module for the annotation of cancer-related single-nucleotide variants at protein–protein interfaces

Vázquez, M., Valencia, A., & Pons, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv142 · Cited by 42 · see citing works

TGF-β-signaling with small molecule FKBP12 antagonists that bind myristoylated FKBP12-TGF-β type I receptor fusion proteins

Stockwell, B. R., & Schreiber, S. L. (1998) · Chemistry & Biology · doi:10.1016/s1074-5521(98)90072-2 · Cited by 41 · see citing works

Immunosuppressive Small Molecule Discovered by Structure‐Based Virtual Screening for Inhibitors of Protein–Protein Interactions

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 · see citing works

Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer

Engin, H. B., Güney, E., Keskin, Ö., Oliva, B., & Gürsoy, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081035 · Cited by 39 · see citing works

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 · see citing works

Insights into an Original Pocket-Ligand Pair Classification: A Promising Tool for Ligand Profile Prediction

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 25 · see citing works

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 · see citing works

Enriching Traditional Protein-protein Interaction Networks with Alternative Conformations of Proteins

Halakou, F., Sen-Kilic, E., Cukuroglu, E., Keskin, Ö., & Gürsoy, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-07351-0 · Cited by 21 · see citing works

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 · see citing works

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 19 · see citing works

Fresh from the biotech pipeline—2016

Morrison, C. (2017) · Nature Biotechnology · doi:10.1038/nbt.3783 · Cited by 17 · see citing works

Protein-protein binding selectivity and network topology constrain global and local properties of interface binding networks

Holland, D. O., Shapiro, B. H., Xue, P., & Johnson, M. E. (2017) · Scientific Reports · doi:10.1038/s41598-017-05686-2 · Cited by 16 · see citing works

MOLFIT: A computer program for molecular superposition

Redington, P. K. (1992) · Computers & Chemistry · doi:10.1016/0097-8485(92)80005-k · Cited by 14 · see citing works

Gabapentin Inhibits Protein Kinase C Epsilon Translocation in Cultured Sensory Neurons with Additive Effects When Coapplied with Paracetamol (Acetaminophen)

Vellani, V., & Giacomoni, C. (2017) · The Scientific World JOURNAL · doi:10.1155/2017/3595903 · Cited by 11 · see citing works

Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations

Barradas‐Bautista, D., & Fernández‐Recio, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183643 · Cited by 10 · see citing works

Graph-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 7 · see citing works

Predicting protein-protein interactions with DrugScorePPI: fully-flexible docking, scoring, and in silicoalanine-scanning

Krüger, D., Garzón, J., Montes, P., & Gohlke, H. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-s1-p36 · Cited by 4 · see citing works

Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials

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 · see citing works

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 · see citing works

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 233 · see citing works

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 93 · see citing works

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 74 · see citing works

Small molecules targeting protein–protein interactions for cancer therapy

Wu, D., Li, Y., Zheng, L., Xiao, H., Ouyang, L., Wang, G., & Sun, Q. (2023) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2023.05.035 · Cited by 71 · see citing works

DDMut-PPI: predicting effects of mutations on protein–protein interactions using graph-based deep learning

Zhou, Y., Myung, Y., Rodrigues, C. H. M., & Ascher, D. B. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae412 · Cited by 65 · see citing works

Fragment-based drug discovery supports drugging ‘undruggable’ protein–protein interactions

Wang, Z.-Z., Shi, X.-X., Huang, G.-Y., Hao, G., & Yang, G. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2023.01.008 · Cited by 64 · see citing works

Targeting protein-protein interaction interfaces in COVID-19 drug discovery

Chang, C., Lin, S., Satange, R., Lin, S., Sun, S.-C., Wu, H., Kehn‐Hall, K., & Hou, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.003 · Cited by 46 · see citing works

Computational methods-guided design of modulators targeting protein-protein interactions (PPIs)

Qiu, Y., Li, X., He, X., Pu, J., Zhang, J., & Lu, S. (2020) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2020.112764 · Cited by 41 · see citing works

Machine learning methods for protein-protein binding affinity prediction in protein design

Guo, Z., & Yamaguchi, R. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.1065703 · Cited by 37 · see citing works

ProtInteract: A deep learning framework for predicting protein–protein interactions

Soleymani, F., Paquet, E., Viktor, H. L., Michalowski, W. J., & Spinello, D. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.01.028 · Cited by 39 · see citing works

Blocker-SELEX: a structure-guided strategy for developing inhibitory aptamers disrupting undruggable transcription factor interactions

Li, T., Liu, X., Qian, H., Zhang, S., Hou, Y., Zhang, Y., Luo, G., Zhu, X., Tao, Y., Fan, M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51197-w · Cited by 37 · see citing works

Targeting protein-protein interactions in drug discovery: Modulators approved or in clinical trials for cancer treatment

Camps-Fajol, C., Cavero, D., Minguillón, J., & Surrallés, J. (2024) · Pharmacological Research · doi:10.1016/j.phrs.2024.107544 · Cited by 35 · see citing works

Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing

Özdemir, E. S., Halakou, F., Nussinov, R., Gürsoy, A., & Keskin, Ö. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8955-3_1 · Cited by 34 · see citing works

An Overview of Computational Methods, Tools, Servers, and Databases for Drug Repurposing

Sarvagalla, S., Syed, S. B., & Coumar, M. S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816125-8.00025-0 · Cited by 32 · see citing works

Docking-based identification of small-molecule binding sites at protein-protein interfaces

Rosell, M., & Fernández‐Recio, J. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.11.029 · Cited by 32 · see citing works

Targeting protein–protein interactions in the DNA damage response pathways for cancer chemotherapy

McPherson, K. S., & Korzhnev, D. M. (2021) · RSC Chemical Biology · doi:10.1039/d1cb00101a · Cited by 33 · see citing works

Selective fluorescent labeling of cellular proteins and its biological applications.

Choi, J., Kim, S., Kang, O.-Y., Choi, S. Y., Ji, Y. H., Lee, H. S., & Shin, I. (2024) · Chemical Society Reviews · doi:10.1039/d4cs00094c · Cited by 12 · see citing works

Protein–Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Panday, S. K., & Alexov, E. G. (2022) · ACS Omega · doi:10.1021/acsomega.1c07037 · Cited by 32 · see citing works

Main protease inhibitors and drug surface hotspots for the treatment of COVID-19: A drug repurposing and molecular docking approach

Hasan, M., Parvez, M. S. A., Azim, K. F., Imran, M. A. S., Raihan, T., Gulshan, A., Muhit, S., Akhand, M. R. N., Ahmed, S. S. U., & Uddin, M. B. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2021.111742 · Cited by 25 · see citing works

A Bionic‐Homodimerization Strategy for Optimizing Modulators of Protein–Protein Interactions: From Statistical Mechanics Theory to Potential Clinical Translation

Yan, J., Zheng, X., You, W., He, W., & Xu, G. (2022) · Advanced Science · doi:10.1002/advs.202105179 · Cited by 24 · see citing works

Molecular Dynamics Simulation-Driven Focused Virtual Screening and Experimental Validation of Inhibitors for MTDH-SND1 Protein–Protein Interaction

Xu, Y.-D., Guo, X.-Y., Yan, D., Dang, X., Guo, L., Jia, T., & Wang, Q. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00310 · Cited by 24 · see citing works

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Nandi, S., Bhaduri, S., Das, D., Ghosh, P., Mandal, M., & Mitra, P. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01161 · Cited by 23 · see citing works

Prediction and targeting of GPCR oligomer interfaces

Barreto, C. A. V., Baptista, S. J., Preto, A. J., Matos‐Filipe, P., Mourão, J., Melo, R., & Moreira, I. S. (2020) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2019.11.007 · Cited by 22 · see citing works

Proteomics for cancer drug design

Haymond, A. N., Davis, J. B., & Espina, V. A. (2019) · Expert Review of Proteomics · doi:10.1080/14789450.2019.1650025 · Cited by 22 · see citing works

Decoding Protein-protein Interactions: An Overview

Slater, O., Miller, B., & Kontoyianni, M. (2020) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026620666200226105312 · Cited by 21 · see citing works

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Nikam, R., Yugandhar, K., & Gromiha, M. M. (2023) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2023.140948 · Cited by 20 · see citing works

Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery

Trisciuzzi, D., Villoutreix, B. O., Siragusa, L., Baroni, M., Cruciani, G., & Nicolotti, O. (2023) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2218641 · Cited by 19 · see citing works

Obtaining Protein Association Energy Landscape for Integral Membrane Proteins

Rajagopal, N., & Nangia, S. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00626 · Cited by 19 · see citing works

Conformational Selection Mechanism Provides Structural Insights into the Optimization of APC-Asef Inhibitors

He, X., Huang, N., Qiu, Y., Zhang, J., Liu, Y., Yin, X., & Lu, S. (2021) · Molecules · doi:10.3390/molecules26040962 · Cited by 18 · see citing works

Survivin - caspase protein-protein interaction: Experimental evidence and computational investigations to decipher the hotspot residues for drug targeting

Sarvagalla, S., Lin, T., Kondapuram, S. K., Cheung, C. H. A., & Coumar, M. S. (2020) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2020.129619 · Cited by 17 · see citing works

Crystallographic mining of ASK1 regulators to unravel the intricate PPI interfaces for the discovery of small molecule

Agrahari, A. K., Dikshit, M., & Asthana, S. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.07.008 · Cited by 17 · see citing works

Application of Nanopore Sensors for Biomolecular Interactions and Drug Discovery

Jeong, K., Kim, J., Dhanasekar, N. N., Lee, M., & Chi, S. (2022) · Chemistry - An Asian Journal · doi:10.1002/asia.202200679 · Cited by 17 · see citing works

In silico identification of small molecule modulators for disruption of Hsp90–Cdc37 protein–protein interaction interface for cancer therapeutic application

Dike, P. P., Bhowmick, S., Eldesoky, G. E., Wabaidur, S. M., Patil, P. C., & Islam, M. A. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1835714 · Cited by 17 · see citing works

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

Cicaloni, V., Trezza, A., Pettini, F., & Spiga, O. (2019) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026619666190304153901 · Cited by 17 · see citing works

Cyclobutane-bearing restricted anchoring residues enabled geometry-specific hydrocarbon peptide stapling

Chen, B., Liu, C., Cong, W., Gao, F., Zou, Y., Su, L., Liu, L., Hillisch, A., Lehmann, L. E., Bierer, D. E., et al. (2023) · Chemical Science · doi:10.1039/d3sc04279k · Cited by 16 · see citing works

Apoptosis Inhibitor 5: A Multifaceted Regulator of Cell Fate

Abbas, H., Derkaoui, D. K., Jeammet, L., Adicéam, E., Tiollier, J., Sicard, H., Braun, T., & Poyet, J. (2024) · Biomolecules · doi:10.3390/biom14010136 · Cited by 15 · see citing works

Using machine‐learning‐driven approaches to boost hot‐spot's knowledge

Rosário‐Ferreira, N., Bonvin, A. M. J. J., & Moreira, I. S. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1602 · Cited by 14 · see citing works

Large-Scale Crystallographic Fragment Screening Expedites Compound Optimization and Identifies Putative Protein–Protein Interaction Sites

Barthel, T., Wollenhaupt, J., Lima, G. M. A., Wahl, M. C., & Weiss, M. S. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c01165 · Cited by 14 · see citing works

ProBAN : Neural network algorithm for predicting binding affinity in protein–protein complexes

Bogdanova, E. A., & Novoseletsky, V. N. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26700 · Cited by 13 · see citing works

Mapping of the FGF14:Nav1.6 complex interface reveals FLPK as a functionally active peptide modulating excitability

Singh, A. K., Wadsworth, P. A., Tapia, C. M., Aceto, G., Ali, S. R., Chen, H., D’Ascenzo, M., Zhou, J., & Laezza, F. (2020) · Physiological Reports · doi:10.14814/phy2.14505 · Cited by 13 · see citing works

Extraction of molecular features for the drug discovery targeting protein‐protein interaction of Helicobacter pylori CagA and tumor suppressor protein ASSP2

Junaid, M., Li, C., Shah, M., Khan, A., Guo, H., & Wei, D. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25748 · Cited by 12 · see citing works

Structural insights into Beclin 1 interactions with it's regulators for autophagy modulation

Sarmadhikari, D., & Asthana, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.044 · Cited by 11 · see citing works

Unleashing the power of DNA-encoded libraries for challenging targets in drug discovery

Du, N., Li, Y., Lou, Q., Zhang, G., Li, Y., & Li, Y. (2026) · Pharmaceutical Science Advances · doi:10.1016/j.pscia.2026.100115 · Cited by 10 · see citing works

Recent developments in the utility of saturated azaheterocycles in peptidomimetics

Singh, M. K., & Lakshman, M. K. (2021) · Organic & Biomolecular Chemistry · doi:10.1039/d1ob01329g · Cited by 9 · see citing works

State-of-the-art strategies to prioritize Mycobacterium tuberculosis drug targets for drug discovery using a subtractive genomics approach

Akinnuwesi, A., Egieyeh, S. A., & Cloete, R. E. A. (2023) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2023.1254656 · Cited by 9 · see citing works

Cold spots are universal in protein–protein interactions

Gurusinghe, S. N. S., Oppenheimer, B., & Shifman, J. M. (2022) · Protein Science · doi:10.1002/pro.4435 · Cited by 9 · see citing works

Electrophile Scanning Reveals Reactivity Hotspots for the Design of Covalent Peptide Binders

Grob, N. M., Remarcik, C., Rössler, S. L., Wong, J. Y. K., Wang, J. C. K., Tao, J., Smith, C. L., Loas, A., Buchwald, S. L., Eaton, D., et al. (2023) · ACS Chemical Biology · doi:10.1021/acschembio.3c00538 · Cited by 9 · see citing works

Reinstating apoptosis using putative Bcl-xL natural product inhibitors: Molecular docking and ADMETox profiling investigations

Boyenle, I. D., Ogunlana, A. T., Oyedele, A.-Q. K., Olokodana, B. K., Owolabi, N., Salahudeen, A. K., Aderenle, O. T., Oloyede, T., & Adelusi, T. I. (2022) · Journal of Taibah University Medical Sciences · doi:10.1016/j.jtumed.2022.10.014 · Cited by 8 · see citing works

Structure-based Virtual screening, Biological Evaluation and Biophysical Study of Novel Mcl-1 Inhibitors

Du, J., Liu, L., Liu, B., Yang, J., Hou, X., Yu, J., & Fang, H. (2020) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0114 · Cited by 8 · see citing works

Village Regrouping in the Eastern Plains of China: A Perspective on Home-Field Distance

Li, X., Liu, Y., Chen, Y., Li, P., & Yu, Z. (2019) · Sustainability · doi:10.3390/su11061630 · Cited by 8 · see citing works

Monte Carlo simulations using PELE to identify a protein–protein inhibitor binding site and pose

Díaz, L., Soler, D., Tresadern, G., Buyck, C., Pérez‐Benito, L., Saen‐oon, S., Guallar, V., & Soliva, R. (2020) · RSC Advances · doi:10.1039/d0ra01127d · Cited by 8 · see citing works

Structure-informed machine learning for drug discovery: a task-centric perspective

Li, Y., Zhan, R.-H., Rao, J., Liu, M., Sang, P., Zeng, X., Zheng, M., Li, X., & Yang, L. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag081 · Cited by 8 · see citing works

Effect of Direct and Water-Mediated Interactions on the Identification of Hotspots in Biomolecular Complexes with Multiple Subsystems

Sládek, V., Artiushenko, P. V., & Fedorov, D. G. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00973 · Cited by 8 · see citing works

Development of R7BP inhibitors through cross-linking coupled mass spectrometry and integrated modeling

Adikaram, P. R., Zhang, J., Kittock, C. M., Pandey, M., Hassan, S. A., Lue, N., Wang, G., Guček, M., & Simonds, W. F. (2019) · Communications Biology · doi:10.1038/s42003-019-0585-1 · Cited by 7 · see citing works

Protein-protein interactions: a structural view of inhibition strategies and the IL-23/IL-17 axis

Allen, S. J., & Lumb, K. J. (2020) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2019.12.006 · Cited by 7 · see citing works

PPI-hotspotID for detecting protein–protein interaction hot spots from the free protein structure

Chen, Y. C., Sargsyan, K., Wright, J. D., Chen, Y., Huang, Y., & Lim, C. (2024) · eLife · doi:10.7554/elife.96643.3 · Cited by 6 · see citing works

Accurate Protein Dynamic Conformational Ensembles: Combining AlphaFold, MD, and Amide 15N(1H) NMR Relaxation

Lesovoy, D., Roshchin, K., Sala, B. M., Sandalova, T., Achour, A., Agback, T., Agback, P., & Orekhov, V. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26188917 · Cited by 6 · see citing works

Computational Design of Peptide-Based Binders to Therapeutic Targets

Mulligan, V. K., & Hosseinzadeh, P. (2022) · ACS symposium series · doi:10.1021/bk-2022-1417.ch003 · Cited by 7 · see citing works

Computational methods for predicting hotspots at protein–RNA interfaces

Mei, L., Hao, G., & Yang, G. (2021) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1675 · Cited by 6 · see citing works

Computational Analysis of Protein–Protein Interactions in Motile T-Cells

Kumar, S., Fazil, M. H. U. T., Ahmad, K., Tripathy, M., Rajapakse, J. C., & Verma, N. K. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9036-8_18 · Cited by 6 · see citing works

Accurate Protein Dynamic Conformational Ensembles: Combining AlphaFold, MD and Amide ¹⁵ N( ¹ H) NMR Relaxation

Lesovoy, D., Roshchin, K., Sala, B. M., Sandalova, T., Achour, A., Agback, T., Orekhov, V., & Agback, P. (2025) · doi:10.1101/2025.02.07.637034 · Cited by 6 · see citing works

Laser capture microdissection

Liotta, L. A., Conrads, T. P., Howard, M. A., Araujo, R. P., & Petricoin, E. (2025) · Nature Reviews Methods Primers · doi:10.1038/s43586-025-00453-4 · Cited by 5 · see citing works

Improved prediction of protein-protein interactions by a modified strategy using three conventional docking software in combination

Choi, S., Son, S. H., Kim, M. Y., Na, I., Uversky, V. N., & Kim, C. G. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.126526 · Cited by 5 · see citing works

Exploring computational tools for improved structural design and stability of helical AApeptides

Li, T., Song, S., & He, Y. J. (2023) · Supramolecular Materials · doi:10.1016/j.supmat.2023.100038 · Cited by 5 · see citing works

PPI-hotspotID for detecting protein–protein interaction hot spots from the free protein structure

Chen, Y. C., Sargsyan, K., Wright, J. D., Chen, Y., Huang, Y., & Lim, C. (2024) · eLife · doi:10.7554/elife.96643 · Cited by 5 · see citing works

Computational and Experimental Characterization of NF023, A Candidate Anticancer Compound Inhibiting cIAP2/TRAF2 Assembly

Cossu, F., Sorrentino, L., Fagnani, E., Zaffaroni, M., Milani, M., Toni Giorgino, A., & Mastrangelo, E. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00518 · Cited by 5 · see citing works

Computationally-obtained structural insights into the molecular interactions between Pidilizumab and binding partners DLL1 and PD-1

Albuquerque, A., da Silva, H. C., Sartori, G., & da Silva, J. H. M. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1885492 · Cited by 5 · see citing works

Rewards of divergence in sequences, 3-D structures and dynamics of yeast and human spliceosome SF3b complexes

Yazhini, A., Sandhya, S., & Srinivasan, N. (2021) · Current Research in Structural Biology · doi:10.1016/j.crstbi.2021.05.003 · Cited by 5 · see citing works

Phage transcription activator RinA regulates Staphylococcus aureus virulence by governing sarA expression

Jiang, M., Li, Y., Sun, B., Xu, S., Pan, T. S., & Li, Y. (2022) · Genes & Genomics · doi:10.1007/s13258-022-01352-8 · Cited by 4 · see citing works

Hot-Spot-Guided Generative Deep Learning for Drug-Like PPI Inhibitor Design

Sun, H., Li, J., Zhang, Y., Lin, S., Chen, J., Tan, H., Wang, R., Mao, X., Zhao, J., Li, R., et al. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00756-w · Cited by 5 · see citing works

Targeting protein–protein interactions with reversible covalent modalities: Non‐cysteine chemistries

Basu, R., & Fletcher, S. (2026) · British Journal of Pharmacology · doi:10.1111/bph.70377 · Cited by 3 · see citing works

Categorization of hotspots into three types – weak, moderate and strong to distinguish protein–protein versus protein–peptide interactions

Kumar, A. K., & Rathore, R. S. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2252077 · Cited by 3 · see citing works

Computed Protein–Protein Enthalpy Signatures as a Tool for Identifying Conformation Sampling Problems

Çınaroğlu, S. S., & Biggin, P. C. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01041 · Cited by 3 · see citing works

Mycobacterium susceptibility to ivermectin by inhibition of eccD3, an ESX-3 secretion system component.

Granados-Tristán, A. L., Carrillo‐Tripp, M., Hernández-Luna, C. E., Herrera-Rodulfo, A. F., González-Escalante, L. A., Arriaga-Guerrero, A. L., Silva-Ramírez, B., Escobedo-Guajardo, B. L., Mercado-Hernández, R., de León, M. B., et al. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012936 · Cited by 3 · see citing works

Revisiting Protein–Protein Docking: A Systematic Evaluation Framework

Jiang, L., Zhang, K., Zhu, K., Wang, Y., Kang, Y., & Hou, T. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01399 · Cited by 3 · see citing works

Prediction of protein interactions with function in protein (de-)phosphorylation

Vagiona, A.-C., Notopoulou, S., Zdráhal, Z., Gonçalves-Kulik, M., Petrakis, S., & Andrade-Navarro, M. A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319084 · Cited by 3 · see citing works

Drug Design Methods to Regulate Protein–Protein Interactions

Poluri, K. M., Gulati, K., Tripathi, D. K., & Nagar, N. (2023) · Springer eBooks · doi:10.1007/978-981-99-2423-3_6 · Cited by 2 · see citing works

Deep learning identification of novel autophagic protein-protein interactions and experimental validation of Beclin 2-Ubiquilin 1 axis in triple-negative breast cancer

Wang, H., Xie, X., Li, X., Huang, W. J., Wu, L., JIN, W., & Liu, B. (2024) · Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics · doi:10.32604/or.2024.055921 · Cited by 2 · see citing works

MENSAdb: a thorough structural analysis of membrane protein dimers

Matos‐Filipe, P., Preto, A. J., Koukos, P. I., Mourão, J., Bonvin, A. M. J. J., & Moreira, I. S. (2021) · Database · doi:10.1093/database/baab013 · Cited by 2 · see citing works

Target-specific design of drug-like PPI inhibitors via hot-spot-guided generative deep learningPreprint

Sun, H., Li, J., Zhang, Y., Lin, S., Chen, J., Tan, H., Wang, R., Mao, X., Zhao, J., Li, R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.29.620869 · Cited by 2 · see citing works

Druggability assessments of small peptides as protein–protein interaction inhibitors targeting EED-EZH2 binding within the Polycomb Repressive Complex 2 (PRC2), an epigenetic regulator

Kundil, V. T., Suresh, S. C., Chellappan, S., Kizhakkepurayil, A., Basheer, S. M., & Gangadharan, A. K. (2025) · Molecular Diversity · doi:10.1007/s11030-025-11321-4 · Cited by 1 · see citing works

A hybrid DNN model using novel integrated interface features for predicting protein-protein complexes binding affinity

Zhang, L., Bian, Z., Wang, X., & Kong, L. (2025) · Biosystems · doi:10.1016/j.biosystems.2025.105688 · Cited by 1 · see citing works

Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases

Oliva, F., Musiani, F., Giorgetti, A., Rubeis, S. D., Sorokina, O., Armstrong, J. D., Carloni, P., & Ruggerone, P. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2022.1059593 · Cited by 1 · see citing works

Application of Post Solid-Phase Oxime Ligation to Fine-Tune Peptide–Protein Interactions

Zhao, X. Z., Liu, F., & Burke, T. R. (2020) · Molecules · doi:10.3390/molecules25122807 · Cited by 1 · see citing works

PPI-hotspotID: A Method for Detecting Protein-Protein Interaction Hot Spots from the Free Protein StructurePreprint

Chen, Y. C., Sargsyan, K., Wright, J. D., Chen, Y., Huang, Y., & Lim, C. (2023) · Research Square · doi:10.21203/rs.3.rs-3400169/v1 · Cited by 1 · see citing works

PPI-hotspotID: A Method for Detecting Protein-Protein Interaction Hot Spots from the Free Protein StructurePreprint

Chen, Y. C., Sargsyan, K., Wright, J. D., Chen, Y., Huang, Y., & Lim, C. (2024) · eLife · doi:10.7554/elife.96643.1 · Cited by 1 · see citing works

PPI-hotspotID: A Method for Detecting Protein-Protein Interaction Hot Spots from the Free Protein StructurePreprint

Chen, Y. C., Sargsyan, K., Wright, J. D., Chen, Y., Huang, Y., & Lim, C. (2024) · eLife · doi:10.7554/elife.96643.2 · Cited by 1 · see citing works

Three types of hotspots- weak, moderate and strong in protein-protein and protein-peptide interactionsPreprint

Kumar, A. K., & Rathore, R. S. (2022) · doi:10.22541/au.167101052.24746048/v1

Selection of collagen IV fragments forming the outer sphere of the native protein: Assessment of biological activity for regenerative medicine

Becht, A., Frączyk, J., Waśko, J., Menaszek, E., Kajdanek, J., Miłowska, K., & Kolesińska, B. (2023) · Journal of Peptide Science · doi:10.1002/psc.3537

Molecular recognition of anion binding proteins: Characterization and inhibition

Schuurs, Z. P. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.246091

PPI-hotspotID: A Method for Detecting Protein-Protein Interaction Hot Spots from the Free Protein StructurePreprint

Chen, Y. C., Sargsyan, K., Wright, J. D., Chen, Y., Huang, Y., & Lim, C. (2024) · Research Square · doi:10.21203/rs.3.rs-3400169/v2

PPI-hotspotID: A Method for Detecting Protein-Protein Interaction Hot Spots from the Free Protein StructurePreprint

Chen, Y. C., Sargsyan, K., Wright, J. D., Chen, Y., Huang, Y., & Lim, C. (2024) · Research Square · doi:10.21203/rs.3.rs-3400169/v3

Prediction of Protein-Protein Interactions

Pathak, A., Tiwari, V. R., Menon, R., & Sowdhamini, R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00146-9 · Cited by 1 · see citing works

Pinpointing potent hits for cancer immunotherapy targeting the TIGIT/PVR pathway using the XGBoost model, centroid-based virtual screening, and MD simulation

Lü, X., Wang, C., Tang, M., Li, J., Xia, Z., Fan, S., Jin, Y., & Yang, Z. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108450

Trends Assessment of the Genetic Mutation Induced Protein-Protein Interaction Variation via Protein Large Language Driven MethodPreprint

Kuang, Z.-J., Li, Y.-K., Liu, Z., Zhao, J., Shi, L.-J., Akira-Kawai, & Wang, H. (2025) · bioRxiv · doi:10.1101/2025.05.23.655070

Integrating SICLOPPS screening with next-generation sequencing for the discovery of cyclic peptide modulators of protein–protein interactions

Muro, J. A. S., & Tavassoli, A. (2025) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2025.08.011

Protocol for mapping murine transcription factor interactomes and composite motifs combining affinity purification mass spectrometry and ChIP-seq

Gabele, A., Cihan, M., Sprang, M., Klein, M., Nurbekova, A., Romaniuk, K., Lemmermann, N. A. W., Tenzer, S., Andrade-Navarro, M. A., Bopp, T., et al. (2025) · STAR Protocols · doi:10.1016/j.xpro.2025.104184

Structural Impacts of Insertion Mutations on Protein-Protein Interfaces

Tessmer, J., Day, L., Trigubov, B., Galant, E., & Jagodzinski, F. (2025) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3768322.3769030

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 177 · see citing works

Protein-protein Docking and Hot-spot Prediction for Drug Discovery

Grosdidier, S., & Fernández‐Recio, J. (2012) · Current Pharmaceutical Design · doi:10.2174/138161212802651599 · Cited by 48 · see citing works

Docking and scoring: applications to drug discovery in the interactomics era

Grosdidier, S., & Fernández‐Recio, J. (2009) · Expert Opinion on Drug Discovery · doi:10.1517/17460440903002067 · Cited by 15 · see citing works

Hotspot and binding site prediction: Strategy to target protein–protein interactions

Silakari, O., & Singh, P. K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820546-4.00012-x

Protein-Protein Interface Analysis and Hot Spots Identification for Chemical Ligand Design

Chen, J., Ma, X., Yuan, Y., Pei, J., & Lai, L. (2014) · Current Pharmaceutical Design · doi:10.2174/13816128113199990065 · Cited by 28 · see citing works