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 501–600 of 726

Collaborative data analytics towards prediction on pathogen-host protein-protein interactions
Chen, H., Shen, J., Wang, L., & Song, J. (2017) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2017.8066706 · Cited by 5
A computational framework for modeling functional protein‐protein interactions
Pal, A., Pal, D., & Mitra, P. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26156 · Cited by 5
cpxDeepMSA: A Deep Cascade Algorithm for Constructing Multiple Sequence Alignments of Protein–Protein Interactions
Liu, Z., & Yu, D. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23158459 · Cited by 5
PeSA 2.0: A software tool for peptide specificity analysis implementing positive and negative motifs and motif-based peptide scoring
Topcu, E., Ridgeway, N. H., & Biggar, K. K. (2022) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2022.107753 · Cited by 5
African Swine Fever Virus Protein–Protein Interaction Prediction
Fenster, J. A., Azzinaro, P. A., Dinhobl, M., Borca, M. V., Spinard, E. J., & Gladue, D. P. (2024) · Viruses · doi:10.3390/v16071170 · Cited by 6
Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues
Perišić, O. (2018) · Pharmaceuticals · doi:10.3390/ph11010029 · Cited by 5
Comprehensive Analyses of Tissue-Specific Networks with Implications to Psychiatric Diseases
Lin, G. N., Corominas, R., Nam, H.-J., Urresti, J., & Iakoucheva, L. M. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7027-8_15 · Cited by 5
When theory meets experiment: the PD-1 challenge
Ahmed, M., & Barakat, K. (2017) · Journal of Molecular Modeling · doi:10.1007/s00894-017-3482-x · Cited by 5
Inhibition of protein interactions: co-crystalized protein–protein interfaces are nearly as good as holo proteins in rigid-body ligand docking
Belkin, S., Kundrotas, P. J., & Vakser, I. A. (2018) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-018-0124-z · Cited by 5
Predicting the Functions of Unknown Protein by Analyzing Known Protein Interaction: A Survey
Mugur, R., Smitha, P. S., & Pallavi, M. S. (2018) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/1540 · Cited by 5
Prediction of Protein-Protein Interactions from Protein Sequences by Combining MatPCA Feature Extraction Algorithms and Weighted Sparse Representation Models
Wang, Z., Li, Y., You, Z., Li, L., Zhan, X., & Pan, J. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/5764060 · Cited by 5
Primer on Protein–Protein Interaction Maps
la Rosa, N. M., & Falter‐Braun, P. (2015) · Wiley eBooks · doi:10.1002/9780470015902.a0006205.pub2 · Cited by 5
In-silico prediction of domain involved in chilli protein interaction with chilli leaf curl virus and associated betasatellite encoded protein
Pandey, V., Srivastava, A., Singh, N., Gupta, R., Ali, A., & Gaur, R. K. (2024) · Discover Plants. · doi:10.1007/s44372-024-00016-z · Cited by 4
EXCESP: A Structure-Based Online Database for Extracellular Interactome of Cell Surface Proteins in Humans
Dhusia, K., Madrid, C., Su, Z., & Wu, Y. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00612 · Cited by 4
Boosting Deep Learning-based Docking with Cross-attention and Centrality Embedding
Wang, J., Qiu, Z., Zhang, X., Yang, Z., Zhao, W., & Cui, X. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995214 · Cited by 4
MAGPIE: An interactive tool for visualizing and analyzing protein–ligand interactions
Rodríguez, D., Weber, K. C., Sundberg, B., & Glasgow, A. A. (2024) · Protein Science · doi:10.1002/pro.5027 · Cited by 5
Structural disorder: a tool for housekeeping proteins performing tissue-specific interactions
Banerjee, S., & De, R. K. (2015) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2015.1095115 · Cited by 4
The modeled structures of Deg5 and Deg8 proteases in Arabidopsis thaliana
Liangbing, C. H. E. N., Li, Q., & Li, L. (2014) · TURKISH JOURNAL OF BIOLOGY · doi:10.3906/biy-1306-74 · Cited by 4
Understanding the role of protein interaction motifs in transcriptional regulators: implications for crop improvement
Vaschetto, L. M. (2016) · Briefings in Functional Genomics · doi:10.1093/bfgp/elw022 · Cited by 4
MaSIF - Deciphering interaction fingerprints from protein molecular surfaces
Gaínza, P., Sverrisson, F., Monti, F., Rodolà, E., Bronstein, M. M., & Correia, B. E. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.2625419 · Cited by 6
Protein-Protein Interactions Modeling: From Dry to Wet Lab
Khare, E., & Singh, D. B. (2020) · Springer eBooks · doi:10.1007/978-981-15-5530-5_6 · Cited by 4
A structural database of chain–chain and domain–domain interfaces of proteins
Sen, N., & Madhusudhan, M. S. (2022) · Protein Science · doi:10.1002/pro.4406 · Cited by 4
Network-centric analysis of co-fractionated protein complex profiles using SECAT
Bokor, B. J., Gorhe, D., Jovanović, M., & Rosenberger, G. (2023) · STAR Protocols · doi:10.1016/j.xpro.2023.102293 · Cited by 3
A convolutional network and attention mechanism-based approach to predict protein–RNA binding residues
Li, K., Wu, H., Yue, Z., Sun, Y., & Xia, C. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107901 · Cited by 4
Molecular determinants within the C-termini of L-HDAg that regulate hepatitis D virus replication and assembly
Guo, H., Li, Q., Li, C., Hou, Y., Ding, Y., Liu, D., Ni, Y., Tang, R., Zheng, K., Urban, S., et al. (2023) · JHEP Reports · doi:10.1016/j.jhepr.2023.100961 · Cited by 4
EPDRNA: A Model for Identifying DNA–RNA Binding Sites in Disease-Related Proteins
Sun, C., & Feng, Y. (2024) · The Protein Journal · doi:10.1007/s10930-024-10183-3 · Cited by 4
A Protein–Protein Interaction Assay FlimPIA Based on the Functional Complementation of Mutant Firefly Luciferases
Ohmuro‐Matsuyama, Y., & Ueda, H. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3813-1_10 · Cited by 4
Protein–Protein Interaction Inhibitors in Cancer
Hardcastle, I. R. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.12392-3 · Cited by 5
Toward Characterising the Cellular 3D-Proteome
Elofsson, A. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.598878 · Cited by 4
Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces
Raimondi, F., Burkhart, J. G., Betts, M. J., Russell, R. B., & Wu, G. (2021) · F1000Research · doi:10.12688/f1000research.74395.1 · Cited by 4
A topology approach towards modeling activities and properties on a biomolecular surface
Upadhyay, A., Tran, T., & Ekenna, C. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669313 · Cited by 4
Bourgeoning Cancer Targets
Kriplani, P. (2022) · Recent Patents on Anti-Cancer Drug Discovery · doi:10.2174/1574892817666220804142633 · Cited by 4
Development of an in silico method for the identification of subcomplexes involved in the biogenesis of multiprotein complexes in Saccharomyces cerevisiae
Glatigny, A., Gambette, P., Bourand-Plantefol, A., Dujardin, G., & Mucchielli-Giorgi, M.-H. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0442-0 · Cited by 6
Interactomes – Scaffolds of Cellular Systems
Luck, K., Jailkhani, N., Cusick, M. E., Rolland, T., Calderwood, M. A., Charloteaux, B., & Vidal, M. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-394447-4.40037-4 · Cited by 4
Studying Protein–Protein Interactions by Combining Native Mass Spectrometry and Chemical Cross‐Linking
Sharon, M., & Sinz, A. (2015) · Wiley eBooks · doi:10.1002/9783527673391.ch2 · Cited by 4
Pitfalls of machine learning models for protein-protein interactionsPreprint
Lannelongue, L., & Inouye, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.07.479382 · Cited by 4
ProteinAligner: A Multi-modal Pretraining Framework for Protein Foundation ModelsPreprint
Zhang, L., Guo, H., Schaffer, L. V., Ko, Y. S., Singh, D., Rahmani, H., Grotjahn, D., Villa, E., Gilson, M., Wang, W., et al. (2024) · bioRxiv · doi:10.1101/2024.10.06.616870 · Cited by 6
A tri-modal contrastive learning framework for protein representation learning
Zhang, L., Guo, H., Schaffer, L. V., Ko, Y. S., Singh, D., Rahmani, H., Grotjahn, D. A., Villa, E., Gilson, M., Wang, W., et al. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101407 · Cited by 6
Influence of Amino Acid Mutations and Small Molecules on Targeted Inhibition of Proteins Involved in Cancer
Kanakaveti, V., Anoosha, P., Sakthivel, R., Rayala, S. K., & Gromiha, M. M. (2019) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026619666190304143354 · Cited by 3
Mapping the protein–protein interactome in the tumor immune microenvironment
Peng, R., & Deng, M. (2023) · Antibody Therapeutics · doi:10.1093/abt/tbad026 · Cited by 3
A Novel Confocal Scanning Protein–Protein Interaction Assay (PPI-CONA) Reveals Exceptional Selectivity and Specificity of CC0651, a Small Molecule Binding Enhancer of the Weak Interaction between the E2 Ubiquitin-Conjugating Enzyme CDC34A and Ubiquitin
Koszela, J., Pham, N. T., Shave, S., St‐Cyr, D. J., Ceccarelli, D. F. J., Orlicky, S. M., Marinier, A., Sicheri, F., Tyers, M., & Auer, M. (2024) · Bioconjugate Chemistry · doi:10.1021/acs.bioconjchem.4c00345 · Cited by 3
Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks
Zhao, H., Velez, C., Naravane, A., Saha, A., Feldman, J. D., Skolnick, J., Murray, D., & Honig, B. H. (2026) · Nature Communications · doi:10.1038/s41467-026-71166-9 · Cited by 6
Self-citation
Fragment hopping protocol for the design of small-molecule protein–protein interaction inhibitors
Kell, S. R., Wang, Z., & Ji, H. (2022) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2022.116879 · Cited by 3
Structural Determinants of Yeast Protein-Protein Interaction Interface Evolution at the Residue Level
Pollet, L., Lambourne, L., & Xia, Y. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167750 · Cited by 3
Structural signatures: a web server for exploring a database of and generating protein structural features from human cell lines and tissues
Zatorski, N., Stein, D. F., Rahman, R., Iyengar, R., & Schlessinger, A. (2022) · Database · doi:10.1093/database/baac053 · Cited by 3
Structural and functional analysis of protein
Yadav, N. S., Kumar, P., & Singh, I. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89775-4.00026-2 · Cited by 3
Encoding the space of protein-protein binding interfaces by artificial intelligence
Su, Z., Dhusia, K., & Wu, Y. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108080 · Cited by 3
Protein-protein interaction prediction using enhanced features with spaced conjoint triad and amino acid pairwise distance
Göktepe, Y. E. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2748 · Cited by 3
Protein-Protein Interaction Prediction by Integrating Sequence Information and Heterogeneous Network Representation
Su, X., You, Z., Chen, Z., Yi, H.-C., & Guo, Z.-H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84532-2_55 · Cited by 3
Detection of Drug-Drug Interactions Through Knowledge Graph Integrating Multi-attention with Capsule Network
Su, X., You, Z., Yi, H.-C., & Zhao, B.-W. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84532-2_38 · Cited by 3
Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor
Kara, A., Vickers, M. J., Swain, M., Whitworth, D. E., & Fernández‐Fuentes, N. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0741-7 · Cited by 3
Deep Learning Solutions for Protein: Recent Development and Future Directions
Chiroma, H., Usman, A. M., Jauro, F., Gabralla, L. A., Adewole, K. S., Dada, E. G., Shittu, F., Umar, A. Y., Okesola, J. O., & Awodele, O. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80216-5_19 · Cited by 2
Computational Methods and Deep Learning for Elucidating Protein Interaction Networks
Vora, D. S., Kalakoti, Y., & Sundar, D. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2617-7_15 · Cited by 3
Realizing the Potential of Protein-Protein Interaction Prediction for Studying Single and Evolutionarily Similar Organisms and Engineering Inhibitory Proteins with InSiPS: The In Silico Protein Synthesizer
Schoenrock, A. (2016) · Carleton University's Institutional Repository (MacOdrum Library, Carleton University) · doi:10.22215/etd/2016-11283 · Cited by 3
Protein–Protein Interactions and Genetic Disease
Brender, J., & Zhang, Y. (2017) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0026856 · Cited by 3
Impact Analysis of SARS-CoV2 on Signaling Pathways during COVID19 Pathogenesis using Codon Usage Assisted Host-Viral Protein InteractionsPreprint
Das, J. K., Chakraborty, S., & Roy, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.29.226217 · Cited by 3
What NLR you recognizing? Predicted binding affinities- and energies may be used to identify novel NLR-effector interactionsPreprint
Fick, A., Fick, J. L. M., Swart, V., & van den Berg, N. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.26.605369 · Cited by 3
Integrating structural homology with deep learning to achieve highly accurate protein-protein interface prediction for the human interactomePreprint
Xiong, D., Torres, M., Murray, D., Zhang, Z., Li, L., Naravane, A. C., Fragoza, R., Honig, B. H., & Yu, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.09.658393 · Cited by 3
Self-citation
Virus targeting as a dominant driver of interfacial evolution in the structurally resolved human-virus protein-protein interaction network
Su, W.-C., & Xia, Y. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101202 · Cited by 2
PrePPI – Structure-based Prediction of Protein–protein Interactomes and Networks
Velez, C., Naravane, A. C., Robila, V. I., Saha, A., Murray, D., & Honig, B. H. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169735 · Cited by 3
Self-citation
Fifteen challenges for generative AI applications to cell biology
Dupire, L., Khan, A. A., Karaletsos, T., Kelley, S., Lundberg, E. K., Ma, J., Paull, E., Quake, S. R., Rabadán, R., Cassius, R., et al. (2026) · Cell · doi:10.1016/j.cell.2026.07.004 · Cited by 3
Self-citation
Prediction of interactions between cell surface proteins by machine learning
Su, Z., Griffin, B., Emmons, S. W., & Wu, Y. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26648 · Cited by 2
The Search for Cancer Drivers
Laganà, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-55248-9_8 · Cited by 2
Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock—A Practical Manual
Biswas, G., Mukherjee, D., & Basu, S. (2024) · Journal of Computational Biology · doi:10.1089/cmb.2024.0554 · Cited by 2
Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer
Zatorski, N., Sun, Y., Elmas, A., Dallago, C., Karl, T., Stein, D., Rost, B., Huang, K.-L., Walsh, M., & Schlessinger, A. (2024) · iScience · doi:10.1016/j.isci.2024.110640 · Cited by 2
GABAB Receptors: Molecular Organization, Function, and Alternative Drug Development by Targeting Protein-Protein Interactions
Benke, D., Bhat, M. A., & Hleihil, M. (2024) · The Receptors · doi:10.1007/978-3-031-67148-7_1 · Cited by 3
Representing and Quantifying Conformational Changes of Kinases and Phosphatases Using the TSR-Based Algorithm
Milon, T. I., Rauniyar, K., Furman, S., Orthi, K. H., Wang, Y., Raghavan, V. V., & Xu, W. (2024) · Kinases and Phosphatases · doi:10.3390/kinasesphosphatases2040021 · Cited by 2
Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues
Grassmann, G., Rienzo, L. D., Ruocco, G., Miotto, M., & Milanetti, E. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02286 · Cited by 2
Proteomics and posttranslational protein modifications in Toxoplasma gondii
Weiss, L. M., Wastling, J. M., Jeffers, V., Sullivan, W. J., & Kim, K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-815041-2.00022-0 · Cited by 2
Novel domain identification approach for protein-protein interaction prediction
Shatnawi, M., & Zaki, N. (2015) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2015.7300340 · Cited by 2
Prediction of Biological Protein-protein Interaction Types Using Short-Linear Motifs
Pandit, M., Rueda, L. G., & Ngom, A. (2013) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2506583.2512372 · Cited by 2
Proteomics and Bioinformatics Soon to Resolve the Human Structural Interactome
Nebel, J. (2012) · Journal of Proteomics & Bioinformatics · doi:10.4172/jpb.10000e15 · Cited by 2
Predicting PPIs
Doerr, A. J. (2012) · Nature Methods · doi:10.1038/nmeth.2272 · Cited by 2
Efficient Prediction of Progesterone Receptor Interactome Using a Support Vector Machine Model
Liu, J., Peng, Y., & Fu, Y.-S. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms16034774 · Cited by 2
Considerations for Vaccine Design in the Postgenomic Era
Maritz-Olivier, C., & Richards, S. A. (2013) · Springer eBooks · doi:10.1007/978-3-319-00978-0_16 · Cited by 2
Protein folding prediction
Ma, B. (2016) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/n972016-00658 · Cited by 1
Intensification: A Resource for Amplifying Population-Genetic Signals with Protein Repeats
Chen, J., Wang, B., Regan, L., & Gerstein, M. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.12.003 · Cited by 2
Bioinformatics in Diagnosis of COVID-19
Sharma, S., Aroura, S., Gupta, A. A., & Priyadarshini, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-79753-9_12 · Cited by 2
Analysis of Brownian and molecular dynamics trajectories of to reveal the mechanisms of protein-protein interactions
Fedorov, V. A., Khruschev, S., & Kovalenko, I. B. (2023) · Computer Research and Modeling · doi:10.20537/2076-7633-2023-15-3-723-738 · Cited by 2
Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources
Zhang, M., & Duan, G. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1625-3_15 · Cited by 2
Using the Multi-instance Learning Method to Predict Protein-Protein Interactions with Domain Information
Zhang, Y., Zha, Y., Li, X., Zhao, S. G., & Du, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11740-9_24 · Cited by 2
Combination of novel and public RNA-seq datasets to generate an mRNA expression atlas for the domestic chickenPreprint
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) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/295535 · Cited by 2
Micropapillary Carcinoma of the Bladder: Recent Advances
Hsu, J. W.-C., & Ro, J. Y. (2019) · annals of urologic oncology · doi:10.32948/auo.2019.07.01 · Cited by 1
Prediction of Protein-Protein Interaction Using Support Vector Machine Based on Spatial Distribution of Amino Acids
Khandelwal, M., Rout, R. K., & Umer, S. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-5974-7_3 · Cited by 1
Structure-guided Evolutionary Analysis of Interactome Network Rewiring at Single Residue Resolution in Yeasts
Pollet, L., & Xia, Y. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168641 · Cited by 1
SE3Graph-PPI:Multiscale Protein-Protein Interaction Prediction Model based on Graph Neural Networks
Fang, Y., Zhou, Y., & Huang, J. (2024) · Chinese Control Conference (CCC) · doi:10.23919/ccc63176.2024.10662662 · Cited by 1
Exploring neural networks to uncover information-richer features for protein interaction prediction
Grassmann, G., Rienzo, L. D., Ruocco, G., Milanetti, E., & Miotto, M. (2025) · European Biophysics Journal · doi:10.1007/s00249-025-01742-2 · Cited by 1
A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration
Huang, Y., Xuan, J., Liang, J., Liu, X., Luo, Y., Gao, X., & Liu, W.-T. (2025) · Biology · doi:10.3390/biology14070742 · Cited by 1
Large Context, Deeper Insights: Harnessing Large Language Models for Advancing Protein–Protein Interaction Analysis
Kaicheng, U., Zhang, S. M., Pokharel, S., Pratyush, P., Qaderi, F., Liu, D., Zhao, J., KC, D. B., & Chen, S. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4623-6_15 · Cited by 1
CanGRNNA: Ensembling Message Passing and Temporal Dynamics with Attention for Prediction of Structural Cancer Protein–Protein Interactions
Jan, R., Hussain, A. B., Assad, A., & Bhat, B. A. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10498-y · Cited by 1
DensePPI-2: a bio-inspired update for sequence-based PPI prediction leveraging mutation rates
Chakraborty, T., Paul, D. E., Halsana, A. A., Halder, A. K., Basu, S., & Chakraborti, T. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf394 · Cited by 1
CPL: Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network
Dai, Q., Guo, M., Liu, X., Teng, Z., & Wang, C. (2014) · Journal of Computer Science and Technology · doi:10.1007/s11390-014-1492-z · Cited by 1
Bioinformatics Based Approaches to Study Virus–Host Interactions During Chikungunya Virus Infection
Rajasekharan, S., & Gupta, S. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3618-2_17 · Cited by 1
Genome-Wide Structural Modeling of Protein-Protein Interactions
Anishchenko, I. V., Badal, V. D., Dauzhenka, T. A., Das, M., Tuzikov, A. V., Kundrotas, P. J., & Vakser, I. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-38782-6_8 · Cited by 1
Coarse-Grained Contact Potential Helps Improve Fold Recognition Sensitivity in Template-Based Protein Structure Modeling
Abdelrasoul, M., & Li, Y. (2016) · doi:10.1109/bdcloud-socialcom-sustaincom.2016.95 · Cited by 1
A new two-stage method for revealing missing parts of edges in protein-protein interaction networks
Zhang, W., Xu, J., Li, Y., & Zou, X. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177029 · Cited by 1
A Bayesian approach for estimating protein–protein interactions by integrating structural and non-structural biological data
Rehman, H. U., Bari, I., Ali, A., & Mahmood, H. (2017) · Molecular BioSystems · doi:10.1039/c7mb00484b · Cited by 1
Analysis the Topological Structure of Reconstructed High-confidence Yeast PPI Network
Dong, Y., & Li, X. (2019) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai48974.2019.9010166 · Cited by 1
The protease web
Esser‐Skala, W., & Fortelny, N. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85696-6.00001-9 · Cited by 1
Towards an Ideal In Cell Hybridization-Based Strategy to Discover Protein Interactomes of Selected RNA Molecules
Spiniello, M., Scalf, M., Casamassimi, A., Abbondanza, C., & Smith, L. M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23020942 · Cited by 1

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