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

Show all 45 references

Sorted by citations

Items 101–200 of 726

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors
Sable, R. V., & Jois, S. D. S. (2015) · Molecules · doi:10.3390/molecules200611569 · Cited by 93
Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions
Betts, M. J., Lu, Q., Jiang, Y., Drusko, A., Wichmann, O., Utz, M., Valtierra-Gutiérrez, I. A., Schlesner, M., Jäger, N., Jones, D., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1094 · Cited by 90
Use of Bioinformatics Tools in Different Spheres of Life Sciences
Mehmood, M. A. (2014) · Journal of Data Mining in Genomics & Proteomics · doi:10.4172/2153-0602.1000158 · Cited by 90
Computational identification of protein-protein interactions in model plant proteomes
Ding, Z., & Kihara, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-45072-8 · Cited by 90
Topsy-Turvy: integrating a global view into sequence-based PPI prediction
Singh, R., Devkota, K., Sledzieski, S., Berger, B., & Cowen, L. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac258 · Cited by 83
InterEvScore: a novel coarse-grained interface scoring function using a multi-body statistical potential coupled to evolution
Andréani, J., Faure, G., & Guérois, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt260 · Cited by 84
Stabilization and Inhibition of Protein–Protein Interactions: The 14-3-3 Case Study
Milroy, L.-G., Brunsveld, L., & Ottmann, C. (2013) · ACS Chemical Biology · doi:10.1021/cb300599t · Cited by 88
The contractome – a systems view of actomyosin contractility in non-muscle cells
Zaidel‐Bar, R., Zhenhuan, G., & Luxenburg, C. (2015) · Journal of Cell Science · doi:10.1242/jcs.170068 · Cited by 96
Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues
Michailidis, G. C., & d’Alché–Buc, F. (2013) · Mathematical Biosciences · doi:10.1016/j.mbs.2013.10.003 · Cited by 90
Machine-learning techniques for the prediction of protein–protein interactions
Sarkar, D., & Saha, S. (2019) · Journal of Biosciences · doi:10.1007/s12038-019-9909-z · Cited by 89
Machine learning on protein–protein interaction prediction: models, challenges and trends
Tang, T., Zhang, X., Liu, Y., Peng, H., Zheng, B., Yin, Y., & Zeng, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad076 · Cited by 95
Synthetic biology for pharmaceutical drug discovery
Trosset, J., & Carbonell, P. (2015) · Drug Design Development and Therapy · doi:10.2147/dddt.s58049 · Cited by 91
Dockground: A comprehensive data resource for modeling of protein complexes
Kundrotas, P. J., Anishchenko, I. V., Dauzhenka, T. A., Kotthoff, I., Mnevets, D., Copeland, M. M., & Vakser, I. A. (2017) · Protein Science · doi:10.1002/pro.3295 · Cited by 82
Intestinal epithelial HMGB1 inhibits bacterial infection via STAT3 regulation of autophagy
Zhang, Y., Zhu, X., Lu, R., Messer, J. S., Xia, Y., Chang, E. B., & Sun, J. (2019) · Autophagy · doi:10.1080/15548627.2019.1596485 · Cited by 81
Rare variant analysis of 4241 pulmonary arterial hypertension cases from an international consortium implicates FBLN2, PDGFD, and rare de novo variants in PAH
Zhu, N., Swietlik, E. M., Welch, C. L., Pauciulo, M. W., Hagen, J., Zhou, X., Guo, Y., Karten, J., Pandya, D., Tilly, T., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00891-1 · Cited by 87
IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis
Kotlyar, M., Pastrello, C., Ahmed, Z., Chee, J., Varyova, Z., & Jurišica, I. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1034 · Cited by 83
Toward Small-Molecule Inhibition of Protein–Protein Interactions: General Aspects and Recent Progress in Targeting Costimulatory and Coinhibitory (Immune Checkpoint) Interactions
Bojadzic, D., & Buchwald, P. (2018) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026618666180531092503 · Cited by 85
Recent advances in predicting and modeling protein–protein interactions
Durham, J., Zhang, J., Humphreys, I. R., Pei, J., & Cong, Q. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2023.03.003 · Cited by 83
Positive-unlabeled learning in bioinformatics and computational biology: a brief review
Li, F., Dong, S., Leier, A., Han, M., Guo, X., Xu, J., Wang, X., Pan, S., Jia, C., Zhang, Y., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab461 · Cited by 87
The Structural Pathway of Interleukin 1 (IL-1) Initiated Signaling Reveals Mechanisms of Oncogenic Mutations and SNPs in Inflammation and Cancer
Acuner, S. E., Gürsoy, A., Nussinov, R., & Keskin, Ö. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003470 · Cited by 80
Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions
Baranwal, M., Magner, A., Saldinger, J. C., Emre, E. S. T., Elvati, P., Kozarekar, S. S., VanEpps, J. S., Kotov, N. A., Violi, A., & Hero, A. O. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04910-9 · Cited by 86
Mechanisms of DNA-binding specificity and functional gene regulation by transcription factors
Smith, N. C., & Matthews, J. M. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.05.006 · Cited by 81
MLP and CARP are linked to chronic PKCα signalling in dilated cardiomyopathy
Lange, S., Gehmlich, K., Lun, A. S., Blondelle, J., Hooper, C. L., Dalton, N. D., Alvarez, E. A., Zhang, X., Bang, M., Abassi, Y. A., et al. (2016) · Nature Communications · doi:10.1038/ncomms12120 · Cited by 79
Fluoxetine induces autophagic cell death via eEF 2K‐ AMPK ‐ mTOR ‐ ULK complex axis in triple negative breast cancer
Sun, D., Zhu, L., Zhao, Y., Jiang, Y., Chen, L., Yu, Y., & Ouyang, L. (2017) · Cell Proliferation · doi:10.1111/cpr.12402 · Cited by 77
Accounting for Protein Subcellular Localization: A Compartmental Map of the Rat Liver Proteome
Jadot, M., Boonen, M., Thirion, J., Wang, N., Xing, J., Zhao, C., Tannous, A., Qian, M., Zheng, H., Everett, J. K., et al. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.064527 · Cited by 77
Towards Structural Systems Pharmacology to Study Complex Diseases and Personalized Medicine
Xie, L., Ge, X., Tan, H., Xie, L., Zhang, Y., Hart, T. D., Yang, X., & Bourne, P. E. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003554 · Cited by 75
Computational Methods for Predicting Protein‐Protein Interactions Using Various Protein Features
Ding, Z., & Kihara, D. (2018) · Current Protocols in Protein Science · doi:10.1002/cpps.62 · Cited by 77
Critical assessment and performance improvement of plant–pathogen protein–protein interaction prediction methods
Yang, S., Li, H., He, H., Zhou, Y., & Zhang, Z. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx123 · Cited by 74
Mapping, modeling, and characterization of protein–protein interactions on a proteomic scale
Cafarelli, T. M., Desbuleux, A., Wang, Y., Choi, S. G., Ridder, D. D., & Vidal, M. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.05.003 · Cited by 75
Network Biology in Medicine and Beyond
Zhang, B., Tian, Y., & Zhang, Z. (2014) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.113.000123 · Cited by 69
An overview of recent advances in structural bioinformatics of protein–protein interactions and a guide to their principles
Sudha, G., Nussinov, R., & Srinivasan, N. (2014) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2014.07.004 · Cited by 70
A computational interactome and functional annotation for the human proteome
Garzón, J. I., Deng, L., Murray, D., Shapira, S. D., Petrey, D. S., & Honig, B. H. (2016) · eLife · doi:10.7554/elife.18715 · Cited by 70
Self-citation
Beyond Genomics: Studying Evolution with Gene Coexpression Networks
Ruprecht, C., Vaid, N., Proost, S., Persson, S., & Mutwil, M. (2017) · Trends in Plant Science · doi:10.1016/j.tplants.2016.12.011 · Cited by 67
Protein-protein interactions: Methods, databases, and applications in virus-host study
Farooq, Q. U. A., Shaukat, Z., Aiman, S., & Li, C. (2021) · World Journal of Virology · doi:10.5501/wjv.v10.i6.288 · Cited by 71
Study of combining virtual screening and antiviral treatments of the Sars-CoV-2 (Covid-19)
Khodadadi, E., Maroufi, P., Khodadadi, E., Esposito, I., Ganbarov, K., Espsoito, S., Yousefi, M., Zeinalzadeh, E., & Kafil, H. S. (2020) · Microbial Pathogenesis · doi:10.1016/j.micpath.2020.104241 · Cited by 67
iATTRACT: Simultaneous global and local interface optimization for protein–protein docking refinement
Schindler, C., de Vries, S. J., & Zacharias, M. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24728 · Cited by 65
Structure-Guided Design of a Small-Molecule Activator of Sirtuin-3 that Modulates Autophagy in Triple Negative Breast Cancer
Zhang, J. C., Zou, L., Shi, D., Liu, J., Zhang, J., Zhao, R., Wang, G., Zhang, L., Ouyang, L., & Liu, B. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c02268 · Cited by 65
Oral_voting_transfer: classification of oral microorganisms’ function proteins with voting transfer model
Bao, W., Liu, Y., & Chen, B. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1277121 · Cited by 65
Challenges in structural approaches to cell modeling
Im, W., Liang, J., Olson, A. J., Zhou, H., Vajda, S., & Vakser, I. A. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.05.024 · Cited by 70
Computing the functional proteome: recent progress and future prospects for genome-scale models
O’Brien, E. J., & Palsson, B. Ø. (2015) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.12.017 · Cited by 62
Evolution of protein interactions: From interactomes to interfaces
Andréani, J., & Guérois, R. (2014) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2014.05.010 · Cited by 70
Intrinsic disorder associated with 14-3-3 proteins and their partners
Sluchanko, N. N., & Bustos, D. M. (2019) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2019.03.007 · Cited by 67
Low-resolution structural modeling of protein interactome
Vakser, I. A. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.12.003 · Cited by 62
An improved sequence-based prediction protocol for protein-protein interactions using amino acids substitution matrix and rotation forest ensemble classifiers
You, Z., Li, X., & Chan, K. C. C. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.10.042 · Cited by 62
Machine-Learning-Based Predictor of Human–Bacteria Protein–Protein Interactions by Incorporating Comprehensive Host-Network Properties
Lian, X., Yang, S., Li, H., Fu, C., & Zhang, Z. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00074 · Cited by 61
Integration of New Genes into Cellular Networks, and Their Structural Maturation
Abrusán, G. (2013) · Genetics · doi:10.1534/genetics.113.152256 · Cited by 60
Small‐Molecule Stabilization of the 14‐3‐3/Gab2 Protein–Protein Interaction (PPI) Interface
Bier, D., Bartel, M., Sies, K., Halbach, S., Higuchi, Y., Haranosono, Y., Brummer, T., Kato, N., & Ottmann, C. (2015) · ChemMedChem · doi:10.1002/cmdc.201500484 · Cited by 61
LocFuse: Human protein–protein interaction prediction via classifier fusion using protein localization information
Zahiri, J., Mohammad-Noori, M., Ebrahimpour, R., Saadat, S., Bozorgmehr, J. H., Goldberg, T., & Masoudi‐Nejad, A. (2014) · Genomics · doi:10.1016/j.ygeno.2014.10.006 · Cited by 58
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
Structural Bioinformatics of the Interactome
Petrey, D. S., & Honig, B. H. (2014) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-051013-022726 · Cited by 61
Self-citation
Identifying potential cancer driver genes by genomic data integration
Chen, Y., Hao, J., Jiang, W., He, T., Zhang, X., Jiang, T., & Jiang, R. (2013) · Scientific Reports · doi:10.1038/srep03538 · Cited by 59
Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors
Wong, L., You, Z., Ming, Z., Li, J., Chen, X., & Huang, Y. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms17010021 · Cited by 59
Pathogens Use Structural Mimicry of Native Host Ligands as a Mechanism for Host Receptor Engagement
Drayman, N., Glick, Y., Ben-nun-Shaul, O., Zer, H., Zlotnick, A., Gerber, D., Schueler‐Furman, O., & Oppenheim, A. (2013) · Cell Host & Microbe · doi:10.1016/j.chom.2013.05.005 · Cited by 58
Triple-Stage Mass Spectrometry Unravels the Heterogeneity of an Endogenous Protein Complex
Ben-Nissan, G., Belov, M. E., Morgenstern, D., Levin, Y., Dym, O., Arkind, G., Lipson, C., Makarov, A. A., & Sharon, M. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b00518 · Cited by 54
Exploring protein-protein interactions at the proteome level
Elhabashy, H., Merino, F., Alva, V., Kohlbacher, O., & Lupas, A. N. (2022) · Structure · doi:10.1016/j.str.2022.02.004 · Cited by 57
Electrostatic complementarity at the interface drives transient protein-protein interactions
Grassmann, G., Rienzo, L. D., Gosti, G., Leonetti, M., Ruocco, G., Miotto, M., & Milanetti, E. (2023) · Scientific Reports · doi:10.1038/s41598-023-37130-z · Cited by 57
Systems biology of the structural proteome
Brunk, E., Mih, N., Monk, J. M., Zhang, Z., O’Brien, E. J., Bliven, S. E., Chen, K., Chang, R. L., Bourne, P. E., & Palsson, B. Ø. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0271-6 · Cited by 61
Predicting protein–protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach
Tian, B., Wu, X., Chen, C., Qiu, W., Ma, Q., & Yu, B. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.11.011 · Cited by 55
Computational close up on protein–protein interactions: how to unravel the invisible using molecular dynamics simulations?
Rakers, C., Bermúdez, M., Keller, B. G., Mortier, J., & Wolber, G. (2015) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1222 · Cited by 57
Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications
Wang, R., & Loscalzo, J. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.05.016 · Cited by 59
Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies
Zheng, M., Zhao, J., Cui, C., Fu, Z., Li, X., Liu, X., Ding, X., Tan, X., Li, F., Luo, X., et al. (2018) · Medicinal Research Reviews · doi:10.1002/med.21483 · Cited by 55
Protein structural bioinformatics: An overview
Paiva, V. D. A., Gomes, I. D. S., Monteiro, C. R., Mendonça, M. V., Martins, P., Santana, C. A., Gonçalves-Almeida, V. M., Izidoro, S. C., de Melo-Minardi, R. C., & Silveira, S. D. A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105695 · Cited by 54
Detecting Protein-Protein Interactions with a Novel Matrix-Based Protein Sequence Representation and Support Vector Machines
You, Z., Li, J., Gao, X., Zhou, H., Zhu, L., Lei, Y., & Ji, Z. (2015) · BioMed Research International · doi:10.1155/2015/867516 · Cited by 52
Extracting Insight from Noisy Cellular Networks
Landry, C. R., Levy, E. D., Abd-Rabbo, D., Tarassov, K., & Michnick, S. W. (2013) · Cell · doi:10.1016/j.cell.2013.11.003 · Cited by 51
Stabilization of Protein–Protein Interactions in chemical biology and drug discovery
Bier, D., Thiel, P., Briels, J. M., & Ottmann, C. (2015) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2015.05.002 · Cited by 56
Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature
Nagarajan, M., Wilkins, A. D., Bachman, B. J., Novikov, I. B., Bao, S., Haas, P. J., Terrón-Díaz, M. E., Bhatia, S., Adikesavan, A. K., Labrie, J. J., et al. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788609 · Cited by 52
The Scientific Quest for Lasting Youth: Prospects for Curing Aging
de Magalhães, J. P. (2014) · Rejuvenation Research · doi:10.1089/rej.2014.1580 · Cited by 54
Structure-based inhibition of protein–protein interactions
Watkins, A. M., & Arora, P. S. (2014) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2014.09.047 · Cited by 56
Computational modeling of protein assemblies
Soni, N., & Madhusudhan, M. S. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.04.006 · Cited by 50
Structural host-microbiota interaction networks
Guven‐Maiorov, E., Tsai, C., & Nussinov, R. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005579 · Cited by 55
Revealing protein-protein interactions at the transcriptome scale by sequencing
Johnson, K. L., Qi, Z., Yan, Z., Wen, X., Nguyen, T. C., Zaleta-Rivera, K., Chen, C.-J., Fan, X., Sriram, K., Wan, X., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.07.006 · Cited by 50
Discovery of Novel Dual-Target Inhibitor of Bromodomain-Containing Protein 4/Casein Kinase 2 Inducing Apoptosis and Autophagy-Associated Cell Death for Triple-Negative Breast Cancer Therapy
Zhang, J., Tang, P., Zou, L., Zhang, J., Chen, J., Yang, C., He, G., Liu, B., Liu, J., Chiang, C., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01382 · Cited by 52
SGPPI: structure-aware prediction of protein–protein interactions in rigorous conditions with graph convolutional network
Huang, Y., Wuchty, S., Zhou, Y., & Zhang, Z. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad020 · Cited by 51
Degraders upgraded: the rise of PROTACs in hematological malignancies
Casan, J. M. L., & Seymour, J. F. (2024) · Blood · doi:10.1182/blood.2023022993 · Cited by 53
A Massively Parallel Pipeline to Clone DNA Variants and Examine Molecular Phenotypes of Human Disease Mutations
Wei, X., Das, J., Fragoza, R., Liang, J. H., de Oliveira, F. M. B., Lee, H. R., Wang, X., Mort, M. E., Stenson, P. D., Cooper, D. N., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004819 · Cited by 51
An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge
Perricone, U., Gulotta, M. R., Lombino, J., Parrino, B., Cascioferro, S., Diana, P., Cirrincione, G., & Padova, A. (2018) · MedChemComm · doi:10.1039/c8md00166a · Cited by 54
Systematic elucidation of neuron-astrocyte interaction in models of amyotrophic lateral sclerosis using multi-modal integrated bioinformatics workflow
Mishra, V. R., Ré, D. B., Verche, V. L., Alvarez, M. J., Vasciaveo, A., Jacquier, A., Doulias, P.-T., Greco, T. M., Nizzardo, M., Papadimitriou, D. A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19177-y · Cited by 49
Self-citation
New insights into the gene expression associated to amyotrophic lateral sclerosis
Recabarren-Leiva, D., & Alarcón, M. A. (2017) · Life Sciences · doi:10.1016/j.lfs.2017.12.016 · Cited by 49
PrePPI: A Structure Informed Proteome-wide Database of Protein–Protein Interactions
(2023) · doi:10.1016/j.jmb.2023.168052 · Cited by 48
Self-citation
Protein–Protein Interactions as Drug Targets
Skwarczynska, M., & Ottmann, C. (2015) · Future Medicinal Chemistry · doi:10.4155/fmc.15.138 · Cited by 52
Proteomic Approaches and Identification of Novel Therapeutic Targets for Alcoholism
Gorini, G., Harris, R. A., & Mayfield, R. D. (2013) · Neuropsychopharmacology · doi:10.1038/npp.2013.182 · Cited by 50
Protein interaction networks as starting points to identify novel antimicrobial drug targets
Zoraghi, R., & Reiner, N. E. (2013) · Current Opinion in Microbiology · doi:10.1016/j.mib.2013.07.010 · Cited by 49
Survey of Similarity-Based Prediction of Drug-Protein Interactions
Wang, C., & Kurgan, L. (2019) · Current Medicinal Chemistry · doi:10.2174/0929867326666190808154841 · Cited by 51
Efficient prediction of human protein-protein interactions at a global scale
Schoenrock, A., Samanfar, B., Pitre, S., Hooshyar, M., Jin, K., Phillips, C., Wang, H., Phanse, S., Omidi, K., Gui, Y., et al. (2014) · BMC Bioinformatics · doi:10.1186/s12859-014-0383-1 · Cited by 49
Deorphanizing the human transmembrane genome: A landscape of uncharacterized membrane proteins
Babcock, J. J., & Li, M. (2013) · Acta Pharmacologica Sinica · doi:10.1038/aps.2013.142 · Cited by 44
Getting to the edge: protein dynamical networks as a new frontier in plant–microbe interactions
Garbutt, C. C., Bangalore, P., Kannar, P., & Mukhtar, M. S. (2014) · Frontiers in Plant Science · doi:10.3389/fpls.2014.00312 · Cited by 49
A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa)
Liu, S., Liu, Y., Zhao, J., Cai, S., Qian, H., Zuo, K., Zhao, L., & Zhang, L. (2017) · The Plant Journal · doi:10.1111/tpj.13475 · Cited by 48
A Combination of Transcriptional and MicroRNA Regulation Improves the Stability of the Relative Concentrations of Target Genes
Riba, A., Bosia, C., Baroudi, M. E., Ollino, L., & Caselle, M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003490 · Cited by 47
Template-based prediction of protein function
Petrey, D. S., Chen, T. S., Deng, L., Garzón, J. I., Hwang, H., Lasso, G., Lee, H., Silkov, A., & Honig, B. H. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.01.007 · Cited by 48
Self-citation
Novel toll-like receptor 4 (TLR4) antagonists identified by structure- and ligand-based virtual screening
Švajger, U., Brus, B., Turk, S., Sova, M., Hodnik, V., Anderluh, G., & Gobec, S. (2013) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2013.10.019 · Cited by 46
Virtual screening and experimental validation reveal novel small-molecule inhibitors of 14-3-3 protein–protein interactions
Thiel, P., Röglin, L., Meissner, N., Hennig, S., Kohlbacher, O., & Ottmann, C. (2013) · Chemical Communications · doi:10.1039/c3cc44612c · Cited by 47
Drafting the CLN3 Protein Interactome in SH-SY5Y Human Neuroblastoma Cells: A Label-free Quantitative Proteomics Approach
Scifo, E., Szwajda, A., Dȩbski, J., Uusi-Rauva, K., Kesti, T., Dadlez, M., Gingras, A., Tyynelä, J., Baumann, M., Jalanko, A., et al. (2013) · Journal of Proteome Research · doi:10.1021/pr301125k · Cited by 49
Studying protein–protein interactions: progress, pitfalls and solutions
Hayes, S. L., Malacrida, B., Bailey‐Whyte, M., & Kiely, P. A. (2016) · Biochemical Society Transactions · doi:10.1042/bst20160092 · 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
Personalized genomic disease risk of volunteers
Gonzalez‐Garay, M. L., McGuire, A. L., Pereira, S., & Caskey, C. T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1315934110 · Cited by 47
The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein–Protein Interactions (PPIs)
Yakubu, R., Nieves, E., & Weiss, L. M. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-15950-4_10 · Cited by 46
SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles
Rosenberger, G., Heusel, M., Bludau, I., Collins, B. C., Martelli, C., Williams, E. G., Xue, P., Liu, Y., Aebersold, R. H., & Califano, A. (2020) · Cell Systems · doi:10.1016/j.cels.2020.11.006 · Cited by 40
Self-citation
Structural Systems Pharmacology: The Role of 3D Structures in Next-Generation Drug Development
Duran‐Frigola, M., Mosca, R., & Aloy, P. (2013) · Chemistry & Biology · doi:10.1016/j.chembiol.2013.03.004 · Cited by 44
Data-Driven Prediction and Design of bZIP Coiled-Coil Interactions
Potapov, V., Kaplan, J. B., & Keating, A. E. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004046 · Cited by 45
HOMCOS: an updated server to search and model complex 3D structures
Kawabata, T. (2016) · Journal of Structural and Functional Genomics · doi:10.1007/s10969-016-9208-y · Cited by 45

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