Cited by 726Bioinformatics and Genomic NetworksProtein Structure and DynamicsMachine Learning in Bioinformatics

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Structure-based prediction of protein–protein interactions on a genome-wide scale

Open access

Zhang, Qiangfeng Cliff, Petrey, Donald, Deng, Lei, Qiang, Li, Shi, Yu, Thu, Chan Aye, Bisikirska, Brygida, Lefebvre, Celine, Accili, Domenico, Hunter, Tony, Maniatis, Tom, Califano, Andrea, Honig, Barry·2012·Nature

DOI 10.1038/nature11503PMC PMC3482288

Open this paper in CoChat — free account

Abstract

The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms. Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification, as well as from manual curation of experiments on individual systems. A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein-protein interactions (PPIs). Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.

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

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

Integrity signals

Registry

Crossref · 10.1038/nature11503

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

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

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

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

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

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

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
The Protein Data Bank
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
Inference of Macromolecular Assemblies from Crystalline State
Krissinel, E., & Henrick, K. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.05.022 · Cited by 10,007
UniProt: the Universal Protein knowledgebase
Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,593
Comparative assessment of large-scale data sets of protein–protein interactions
von Mering, C., Krause, R., Snel, ‎., Cornell, M. J., Oliver, S. G., Fields, S., & Peer Bork, B. (2002) · Nature · doi:10.1038/nature750 · Cited by 2,339
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
STRING: known and predicted protein-protein associations, integrated and transferred across organisms
von Mering, C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki005 · Cited by 1,768
High-Quality Binary Protein Interaction Map of the Yeast Interactome Network
Yu, H., Braun, P., Yıldırım, M. A., Lemmens, I., Venkatesan, K., Sahalie, J., Hirozane-Kishikawa, T., Gebreab, F., Li, N., Simonis, N., et al. (2008) · Science · doi:10.1126/science.1158684 · Cited by 1,392
A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data
Jansen, R., Yu, H., Greenbaum, D. S., Kluger, Y., Krogan, N. J., Chung, S., Emili, A., Snyder, M. P., Greenblatt, J. F., & Gerstein, M. (2003) · Science · doi:10.1126/science.1087361 · Cited by 1,254

Flagged first, then by citations

Show all 45 references

Sorted by citations

Items 201–300 of 726

An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences
Wang, L., You, Z., Chen, X., Li, J., Yan, X., Zhang, W., & Huang, Y. (2016) · Oncotarget · doi:10.18632/oncotarget.14103 · Cited by 45
Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study
Zhou, H., Wang, W., Jin, J., Zheng, Z., & Zhou, B. (2022) · Molecules · doi:10.3390/molecules27186135 · Cited by 43
A hybrid method for protein–protein interface prediction
Hwang, H., Petrey, D. S., & Honig, B. H. (2015) · Protein Science · doi:10.1002/pro.2744 · Cited by 44
Self-citation
Understanding Protein–Protein Interactions Using Local Structural Features
Planas-Iglesias, J., Bonet, J., Garcı́a-Garcı́a, J., Marín-López, M. A., Feliu, E., & Oliva, B. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.01.014 · Cited by 44
Higher order structures in purine and pyrimidine metabolism
Chitrakar, I., Kim, D., Zhou, W., & French, J. B. (2017) · Journal of Structural Biology · doi:10.1016/j.jsb.2017.01.003 · Cited by 45
2D Zernike polynomial expansion: Finding the protein-protein binding regions
Milanetti, E., Miotto, M., Rienzo, L. D., Monti, M., Gosti, G., & Ruocco, G. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.11.051 · Cited by 42
Recent advances in predicting protein–protein interactions with the aid of artificial intelligence algorithms
Li, S., Wu, S., Wang, L., Li, F., Jiang, H., & Bai, F. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102344 · Cited by 42
PPISB: A Novel Network-Based Algorithm of Predicting Protein-Protein Interactions With Mixed Membership Stochastic Blockmodel
Wang, X., Yang, W., Yang, Y., He, Y.-Z., Zhang, J., Wang, L., & Hu, L. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3196336 · Cited by 42
Deep Neural Network and Extreme Gradient Boosting Based Hybrid Classifier for Improved Prediction of Protein-Protein Interaction
Mahapatra, S., Gupta, V. R., Sahu, S. S., & Panda, G. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3061300 · Cited by 42
Proteomics: An In‐Depth Review on Recent Technical Advances and Their Applications in Biomedicine
Liang, J., Tian, J., Zhang, H., Li, H., & Chen, L. (2025) · Medicinal Research Reviews · doi:10.1002/med.22098 · Cited by 39
Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification
Glaab, E. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv044 · Cited by 42
Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning
Du, T., Liao, L., Wu, C. H., & Sun, B. (2016) · Methods · doi:10.1016/j.ymeth.2016.06.001 · Cited by 41
Novel function discovery through sequence and structural data mining
Lobb, B., & Doxey, A. C. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.05.017 · Cited by 41
Structural origins of clustered protocadherin-mediated neuronal barcoding
Rubinstein, R., Goodman, K. M., Maniatis, T., Shapiro, L. S., & Honig, B. H. (2017) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2017.07.023 · Cited by 41
Self-citation
Prediction of DNA-Binding Residues in Local Segments of Protein Sequences with Fuzzy Cognitive Maps
Amirkhani, A., Kolahdoozi, M., Wang, C., & Kurgan, L. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2890261 · Cited by 40
Luminescence- and Fluorescence-Based Complementation Assays to Screen for GPCR Oligomerization: Current State of the Art
Wouters, E., Vasudevan, L., Crans, R. A. J., Saini, D. K., & Stove, C. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20122958 · Cited by 41
Application of docking methodologies to modeled proteins
Singh, A., Dauzhenka, T. A., Kundrotas, P. J., Sternberg, M. J. E., & Vakser, I. A. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25889 · Cited by 41
Physiological Tau Interactome in Brain and Its Link to Tauopathies
Šinský, J., Majerová, P., Kováč, A., Kotlyar, M., Jurišica, I., & Hanes, J. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00137 · Cited by 44
A Sweep of Earth’s Virome Reveals Host-Guided Viral Protein Structural Mimicry and Points to Determinants of Human Disease
Lasso, G., Honig, B. H., & Shapira, S. D. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.006 · Cited by 41
Self-citation
Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis
Gan, Y., Zheng, S., Baak, J. P. A., Zhao, S.-L., Zheng, Y., Luo, N., Liao, W., & Fu, C. (2015) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2015.09.005 · Cited by 40
Network Analysis as a Grand Unifier in Biomedical Data Science
McGillivray, P. D., Clarke, D., Meyerson, W. U., Zhang, J., Lee, D., Gu, M., Kumar, S., Zhou, H., & Gerstein, M. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013444 · Cited by 41
Genome-wide prediction and validation of peptides that bind human prosurvival Bcl-2 proteins
DeBartolo, J., Taipale, M., & Keating, A. E. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003693 · Cited by 39
Role of the Axonal Initial Segment in Psychiatric Disorders: Function, Dysfunction, and Intervention
Hsu, J. W.-C., Nilsson, C. L., & Laezza, F. (2014) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2014.00109 · Cited by 41
Specialized Dynamical Properties of Promiscuous Residues Revealed by Simulated Conformational Ensembles
Fornili, A., Pandini, A., Lu, H., & Fraternali, F. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400486p · Cited by 38
Identification of ULK1 as a novel biomarker involved in miR-4487 and miR-595 regulation in neuroblastoma SH-SY5Y cell autophagy
Chen, Y., Wang, S., Zhang, L., Xie, T., Song, S., Huang, J., Zhang, Y., Ouyang, L., & Liu, B. (2015) · Scientific Reports · doi:10.1038/srep11035 · Cited by 34
Combination of novel and public RNA-seq datasets to generate an mRNA expression atlas for the domestic chicken
Bush, S. J., Freem, L. J., MacCallum, A. J., O’Dell, J., Wu, C., Afrasiabi, C., Psifidi, A., Stevens, M. P., Smith, J., SUMMERS, K. M., et al. (2018) · BMC Genomics · doi:10.1186/s12864-018-4972-7 · Cited by 40
Target-based drug discovery: Applications of fluorescence techniques in high throughput and fragment-based screening
Kumar, V., Lakshman, P. K. C., Prasad, T. K., Manjunath, K., Bairy, S. G., Vasu, A. S., Ganavi, B., Jasti, S., & Kamariah, N. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e23864 · Cited by 42
FMvPCI: A Multiview Fusion Neural Network for Identifying Protein Complex via Fuzzy Clustering
Yang, Y., Hu, L., Li, G., Li, D., Hu, P., & Luo, X. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3578348 · Cited by 43
Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM
Gao, Z.-G., Wang, L., Xia, S., You, Z., Yan, X., & Zhou, Y. Z. (2016) · BioMed Research International · doi:10.1155/2016/4563524 · Cited by 38
Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions
Wang, L., You, Z., Yan, X., Xia, S., Liu, F., Li, L., Zhang, W., & Zhou, Y. Z. (2018) · Scientific Reports · doi:10.1038/s41598-018-30694-1 · Cited by 38
Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches
Zheng, N., Wang, K., Zhan, W., & Deng, L. (2018) · Current Drug Metabolism · doi:10.2174/1389200219666180829121038 · Cited by 39
Self-citation
PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning
Li, P., & Liu, Z. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac078 · Cited by 38
Applications of protein ubiquitylation and deubiquitylation in drug discovery
Chen, Y., Xue, H., & Jin, J. (2024) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2024.107264 · Cited by 41
Engineered Proteins and Chemical Tools to Probe the Cell Surface Proteome
Leung, K., Schaefer, K., Lin, Z., Yao, Z. W., & Wells, J. A. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00554 · Cited by 44
Highly precise protein-protein interaction prediction based on consensus between template-based and de novo docking methods
Ohue, M., Matsuzaki, Y., Shimoda, T., Ishida, T., & Akiyama, Y. (2013) · BMC Proceedings · doi:10.1186/1753-6561-7-s7-s6 · Cited by 37
Demonstration of protein-fragment complementation assay using purified firefly luciferase fragments
Ohmuro‐Matsuyama, Y., Chung, C.-I., & Ueda, H. (2013) · BMC Biotechnology · doi:10.1186/1472-6750-13-31 · Cited by 39
Global and local structural similarity in protein–protein complexes: Implications for template‐based docking
Kundrotas, P. J., & Vakser, I. A. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24392 · Cited by 37
An Efficient Ensemble Learning Approach for Predicting Protein-Protein Interactions by Integrating Protein Primary Sequence and Evolutionary Information
You, Z., Huang, W., Zhang, S., Huang, Y., Yu, C.-Q., & Li, L. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2882423 · Cited by 36
Prediction of Protein–Protein Interactions by Evidence Combining Methods
Chang, J.-W., Zhou, Y., Qamar, M. T. U., Chen, L., & Ding, Y. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17111946 · Cited by 37
From proteomes to complexomes in the era of systems biology
Clancy, T., & Hovig, E. J. (2013) · PROTEOMICS · doi:10.1002/pmic.201300230 · Cited by 37
Challenges in bridging the gap between protein structure prediction and functional interpretation
Varadi, M., Tsenkov, M., & Velankar, S. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26614 · Cited by 36
Precision medicine — networks to the rescue
Yadav, A., Vidal, M., & Luck, K. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.02.005 · Cited by 37
Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses
Broyde, J., Simpson, D. R., Murray, D., Paull, E., Chu, B., Tagore, S., Jones, S. J., Griffin, A. T., Giorgi, F. M., Lachmann, A., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0652-7 · Cited by 33
Self-citation
On the Use of Knowledge-Based Potentials for the Evaluation of Models of Protein–Protein, Protein–DNA, and Protein–RNA Interactions
Fornés, O., Garcı́a-Garcı́a, J., Bonet, J., & Oliva, B. (2014) · Advances in protein chemistry and structural biology · doi:10.1016/b978-0-12-800168-4.00004-4 · Cited by 35
Predicting protein-protein interactions using high-quality non-interacting pairs
Zhang, L., Yu, G., Guo, M., & Wang, J. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2525-3 · Cited by 36
Assessing the Applicability of Template-Based Protein Docking in the Twilight Zone
Negroni, J., Mosca, R., & Aloy, P. (2014) · Structure · doi:10.1016/j.str.2014.07.009 · Cited by 35
Covering complete proteomes with X-ray structures: a current snapshot
Mizianty, M. J., Fan, X., Yan, J. R., Chalmers, E., Woloschuk, C., Joachimiak, A., & Kurgan, L. (2014) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s1399004714019427 · Cited by 35
PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes
Dick, K., Samanfar, B., Barnes, B., Cober, E. R., Mimee, B., Tan, L. H., Molnar, S. J., Biggar, K. K., Golshani, A., Dehne, F., et al. (2020) · Scientific Reports · doi:10.1038/s41598-019-56895-w · Cited by 35
Deciphering the Potential Pharmaceutical Mechanism of Chinese Traditional Medicine (Gui-Zhi-Shao-Yao-Zhi-Mu) on Rheumatoid Arthritis
Huang, L., Lv, Q., Xie, D., Shi, T., & Wen, C. (2016) · Scientific Reports · doi:10.1038/srep22602 · Cited by 34
InterPred: A pipeline to identify and model protein–protein interactions
Mirabello, C., & Wallner, B. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25280 · Cited by 34
Topological and system-level protein interaction network (PIN) analyses to deduce molecular mechanism of curcumin
Dhasmana, A., Uniyal, S., Anukriti, A., Kashyap, V. K., Somvanshi, P., Gupta, M., Bhardwaj, U., Jaggi, M., Yallapu, M. M., Haque, S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69011-0 · Cited by 35
Amalgamation of 3D structure and sequence information for protein–protein interaction prediction
Jha, K., & Saha, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-75467-x · Cited by 34
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 36
Protein complex analysis: From raw protein lists to protein interaction networks
Meysman, P., Titeca, K., Eyckerman, S., Tavernier, J. H., Goethals, B., Martens, L., Valkenborg, D., & Laukens, K. (2015) · Mass Spectrometry Reviews · doi:10.1002/mas.21485 · Cited by 33
Current status and future perspectives of computational studies on human–virus protein–protein interactions
Lian, X., Yang, X., Yang, S., & Zhang, Z. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab029 · Cited by 34
ADH-PPI: An attention-based deep hybrid model for protein-protein interaction prediction
Asim, M. N., Ibrahim, M. A., Malik, M. I., Dengel, A. R., & Ahmed, S. (2022) · iScience · doi:10.1016/j.isci.2022.105169 · Cited by 32
ProteinPrompt : a webserver for predicting protein–protein interactions
Canzler, S., Fischer, M., Ulbricht, D., Ristic, N., Hildebrand, P. W., & Staritzbichler, R. (2022) · Bioinformatics Advances · doi:10.1093/bioadv/vbac059 · Cited by 32
CTD2 Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network
Aksoy, B. A., Dančík, V., Smith, K. C., Mazerik, J. N., Ji, Z., Gross, B. E., Nikolova, O. H., Jaber, N. L., Califano, A., Schreiber, S. L., et al. (2017) · Database · doi:10.1093/database/bax054 · Cited by 32
Self-citation
A survey of computational methods in protein–protein interaction networks
Rasti, S., & Vogiatzis, C. (2018) · Annals of Operations Research · doi:10.1007/s10479-018-2956-2 · Cited by 36
Protein Interaction Network Reconstruction Through Ensemble Deep Learning With Attention Mechanism
Li, F., Zhu, F., Ling, X., & Liu, Q. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00390 · Cited by 31
Die Bedeutung von additiven und nicht‐additiven Mutationseffekten beim Protein‐Engineering
Reetz, M. T. (2013) · Angewandte Chemie · doi:10.1002/ange.201207842 · Cited by 31
TNF Superfamily Protein-Protein Interactions: Feasibility of Small- Molecule Modulation
Song, Y., & Buchwald, P. (2015) · Current Drug Targets · doi:10.2174/1389450116666150223115628 · Cited by 32
Current Trends in Macrocyclic Drug Discovery and beyond -Ro5
Alihodžić, S., Bukvić, M., Elenkov, I., Hutinec, A., Koštrun, S., Pešić, D., Saxty, G., Tomašković, L., & Žiher, D. (2018) · Progress in medicinal chemistry · doi:10.1016/bs.pmch.2018.01.002 · Cited by 31
Discovery of multiple interacting partners of gankyrin, a proteasomal chaperone and an oncoprotein—Evidence for a common hot spot site at the interface and its functional relevance
Nanaware, P. P., Ramteke, M. P., Somavarapu, A. K., & Venkatraman, P. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24494 · Cited by 31
Beyond sequence: Structure-based machine learning
Durairaj, J., de Ridder, D., & van Dijk, A. D. J. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.039 · Cited by 32
An improved interolog mapping-based computational prediction of protein–protein interactions with increased network coverage
Folador, E. L., Hassan, S. S., Lemke, N., Barh, D., Silva, A., Ferreira, R. S., & Azevedo, V. (2014) · Integrative Biology · doi:10.1039/c4ib00136b · Cited by 30
Structure learning in Bayesian Networks using regular vines
Haff, I. H., Aas, K., Frigessi, A., & Lacal, V. (2016) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2016.03.003 · Cited by 30
Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies
Macari, G., Toti, D., & Polticelli, F. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00235-7 · Cited by 30
Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network
Yi, H.-C., You, Z., Huang, D.-S., Guo, Z.-H., Chan, K. C. C., & Li, Y. (2020) · iScience · doi:10.1016/j.isci.2020.101261 · Cited by 30
Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome
Wang, C., & Kurgan, L. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby069 · Cited by 32
Inhibitors to degraders: Changing paradigm in drug discovery
Haridas, V., Dutta, S., Munjal, A., & Singh, S. (2024) · iScience · doi:10.1016/j.isci.2024.109574 · Cited by 28
Prediction of Host–Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer
Guven‐Maiorov, E., Tsai, C., Ma, B., & Nussinov, R. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.10.023 · Cited by 31
Identification of 14-3-3 Proteins Phosphopeptide-Binding Specificity Using an Affinity-Based Computational Approach
Li, Z., Tang, J., & Guo, F. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147467 · Cited by 29
A Novel Feature Extraction Scheme with Ensemble Coding for Protein–Protein Interaction Prediction
Du, X., Cheng, J., Zheng, T., Duan, Z., & Qian, F. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms150712731 · Cited by 28
Exploring mechanisms of human disease through structurally resolved protein interactome networks
Das, J., Fragoza, R., Lee, H. R., Cordero, N. A., Guo, Y., Meyer, M. J., Vo, T. V., Wang, X., & Yu, H. (2013) · Molecular BioSystems · doi:10.1039/c3mb70225a · Cited by 28
PCLPred: A Bioinformatics Method for Predicting Protein–Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation
Li, L., Wang, Y., You, Z., Li, Y., & An, J. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19041029 · Cited by 28
Protein–protein interaction and non-interaction predictions using gene sequence natural vector
Zhao, N., Zhuo, M., Tian, K., & Gong, X. (2022) · Communications Biology · doi:10.1038/s42003-022-03617-0 · Cited by 29
Prediction and redesign of protein–protein interactions
Lua, R. C., Marciano, D. C., Katsonis, P., Adikesavan, A. K., Wilkins, A. D., & Lichtarge, O. (2014) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2014.05.004 · Cited by 29
Template-based identification of protein–protein interfaces using eFindSitePPI
Maheshwari, S., & Bryliński, M. (2015) · Methods · doi:10.1016/j.ymeth.2015.07.017 · Cited by 28
Multi-view heterogeneous molecular network representation learning for protein–protein interaction prediction
Su, X., Hu, L., You, Z., Hu, P., & Zhao, B.-W. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04766-z · Cited by 27
Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning
Kiouri, D. P., Batsis, G., & Chasapis, C. T. (2025) · Biomolecules · doi:10.3390/biom15010141 · Cited by 29
The role of electrostatic energy in prediction of obligate protein-protein interactions
Maleki, M., Vasudev, G., & Rueda, L. G. (2013) · Proteome Science · doi:10.1186/1477-5956-11-s1-s11 · Cited by 27
An Integrated Framework for Functional Annotation of Protein Structural Domains
Deng, L., & Chen, Z. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2389213 · Cited by 27
Self-citation
Toward a “Structural BLAST”: Using structural relationships to infer function
Dey, F., Zhang, Q. C., Petrey, D. S., & Honig, B. H. (2013) · Protein Science · doi:10.1002/pro.2225 · Cited by 27
Self-citation
Regulation of NDR1 activity by PLK1 ensures proper spindle orientation in mitosis
Yan, M., Chu, L., Qin, B., Wang, Z., Liu, X., Jin, C., Zhang, G., Gómez-Ferrer, M., Hergovich, A., Chen, Z., et al. (2015) · Scientific Reports · doi:10.1038/srep10449 · Cited by 27
Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
Li, Y., Wang, Z., Li, L., You, Z., Huang, W., Zhan, X., & Wang, Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-96265-z · Cited by 27
Recent advances in structural studies of 14-3-3 protein complexes
Sluchanko, N. N. (2022) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2021.12.004 · Cited by 27
Structural templates for comparative protein docking
Anishchenko, I. V., Kundrotas, P. J., Tuzikov, A. V., & Vakser, I. A. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24736 · Cited by 26
Recent Advances in Computational Models for the Study of Protein–Peptide Interactions
Kilburg, D., & Gallicchio, E. (2016) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2016.06.002 · Cited by 27
AutoPPI: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction
Czibula, G., Albu, A.-I., Bocicor, M. I., & Chira, C. (2021) · Entropy · doi:10.3390/e23060643 · Cited by 27
AGF-PPIS: A protein–protein interaction site predictor based on an attention mechanism and graph convolutional networks
Fu, X., Yuan, Y., Qiu, H., Suo, H., Song, Y., Li, A., Zhang, Y., Xiao, C., Li, Y., Dou, L., et al. (2024) · Methods · doi:10.1016/j.ymeth.2024.01.006 · Cited by 27
Exploring Viral–Host Protein Interactions as Antiviral Therapies: A Computational Perspective
Idrees, S., Chen, H., Panth, N., Paudel, K. R., & Hansbro, P. M. (2024) · Microorganisms · doi:10.3390/microorganisms12030630 · Cited by 27
Computational prediction and experimental validation of a novel synthesized pan-PIM inhibitor PI003 and its apoptosis-inducing mechanisms in cervical cancer
Liu, Z., He, W., Gao, J., Luo, J., Huang, X. L., & Gao, C. (2015) · Oncotarget · doi:10.18632/oncotarget.3139 · Cited by 26
Low-stringency selection of TEM1 for BLIP shows interface plasticity and selection for faster binders
Cohen-Khait, R., & Schreiber, G. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1613122113 · Cited by 24
Effects of Chang-Kang-Fang Formula on the Microbiota-Gut-Brain Axis in Rats With Irritable Bowel Syndrome
Ling, X., Peng, S., Zhong, J., Guo, L., Xu, Y., Jin, X., & Chu, F. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.778032 · Cited by 26
CEMTDD: The database for elucidating the relationships among herbs, compounds, targets and related diseases for Chinese ethnic minority traditional drugs
Huang, J., Zheng, Y., Wu, W., Xie, T., Yao, H. W., Pang, X., Sun, F.-Z., Ouyang, L., & Wang, J. (2015) · Oncotarget · doi:10.18632/oncotarget.3789 · Cited by 26
Toward a three-dimensional view of protein networks between species
Franzosa, E. A., Garamszegi, S., & Xia, Y. (2012) · Frontiers in Microbiology · doi:10.3389/fmicb.2012.00428 · Cited by 26
A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions
Rao, J., Xie, J., Yuan, Q., Liu, D., Wang, Z., Lu, Y., Zheng, S., & Yang, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-48801-4 · Cited by 26
A systems wide mass spectrometric based linear motif screen to identify dominant in-vivo interacting proteins for the ubiquitin ligase MDM2
Nicholson, J., Scherl, A., Way, L., Blackburn, E. A., Walkinshaw, M. D., Ball, K. L., & Hupp, T. R. (2014) · Cellular Signalling · doi:10.1016/j.cellsig.2014.02.011 · Cited by 25
Antibacterial mechanisms identified through structural systems pharmacology
Chang, R. L., Xie, L., Bourne, P. E., & Palsson, B. Ø. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-102 · Cited by 25

Authors: Zhang, Q. C., Petrey, D., Deng, L., Qiang, L., Shi, Y., Thu, C. A., Bisikirska, B., Lefebvre, C., Accili, D., Hunter, T., Maniatis, T., Califano, A., & Honig, B.
Affiliations: Howard Hughes Medical Institute (US)
Tongji University (CN)
Salk Institute for Biological Studies (US)
Columbia University (US)
Corresponding author: Califano, Andrea
Honig, Barry
ORCID: Zhang, Qiangfeng Cliff
Deng, Lei
Shi, Yu
Bisikirska, Brygida
Accili, Domenico
Hunter, Tony
Maniatis, Tom
Califano, Andrea
Honig, Barry
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2012-09-30
Volume: 490
Issue: 7421
Pages: 556-560
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/nature11503
PMC: PMC3482288
OpenAlex: W2039473152
Cited by: 723
References: 45
Citations by year: 2026: 23 · 2025: 35 · 2024: 42 · 2023: 30 · 2022: 55 · 2021: 62 · 2020: 47 · 2019: 43 · 2018: 52 · 2017: 56 · 2016: 59 · 2015: 81 · 2014: 62 · 2013: 71 · 2012: 5
FWCI: 28.39
Type: journal article
Language: en
Update notices: correction (2013-03-06)
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: Howard Hughes Medical Institute
National Cancer Institute (U54CA121852, CA082683, CA121852., R01CA082683)
National Institute of General Medical Sciences (R01GM030518, GM030518, R37GM030518, GM094597, U54GM094597)
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK057539, DK057539)
National Institute of Neurological Disorders and Stroke (R01NS043915)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords: protein-protein interaction, protein-protein interaction prediction, protein structure, protein-protein interaction network, homology model, remote homology, yeast, human interactome, cell regulation
MeSH terms: Algorithms, Animals, Bayes Theorem, Brain, Humans, Models, Molecular, Phylogeny, Protein Binding, Protein Conformation, Protein Kinases, Proteins, ROC Curve, Saccharomyces cerevisiae, Transcription Factors, Reproducibility of Results, Cadherins, Proteome, Protein Interaction Mapping, Matrix Attachment Region Binding Proteins, Proteomics, PPAR gamma, Suppressor of Cytokine Signaling Proteins, Mice, High-Throughput Screening Assays, Protein Interaction Maps

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

Structure-based prediction of protein–protein interactions on a genome-wide scale

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks