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

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

Advances in template-based protein docking by utilizing interfaces towards completing structural interactome

Muratcioğlu, S., Guven‐Maiorov, E., Keskin, Ö., & Gürsoy, A. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.10.001 · Cited by 26

Protein–Protein Interaction Interfaces and their Functional Implications

Schreiber, G. (2020) · Royal Society of Chemistry eBooks · doi:10.1039/9781788016544-00001 · Cited by 29

Protein–protein interaction networks studies and importance of 3D structure knowledge

Lu, H., Fornili, A., & Fraternali, F. (2013) · Expert Review of Proteomics · doi:10.1586/14789450.2013.856764 · Cited by 25

ACT-SVM: Prediction of Protein-Protein Interactions Based on Support Vector Basis Model

Ma, W., Cao, Y., Bao, W., Yang, B., & Chen, Y. (2020) · Scientific Programming · doi:10.1155/2020/8866557 · Cited by 25

DSSGNN-PPI: A Protein–Protein Interactions prediction model based on Double Structure and Sequence graph neural networks

Zhang, F., Chang, S., Wang, B., & Zhang, X. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108669 · Cited by 25

Dual-site molecular glues for enhancing protein-protein interactions of the CDK12-DDB1 complex

Zhang, Z., Li, Y., Yang, J., Li, J., Lin, X., Liu, T., Yang, S., Lin, J., Xue, S., Yu, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50642-0 · Cited by 27

Protein-Protein Docking with Dynamic Residue Protonation States

Kilambi, K. P., Reddy, K., & Gray, J. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004018 · Cited by 24

MADOKA: an ultra-fast approach for large-scale protein structure similarity searching

Deng, L., Zhong, G., Liu, C., Luo, J., & Liu, H. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3235-1 · Cited by 24

Predicting Protein–Protein Interactions Between Rice and Blast Fungus Using Structure-Based Approaches

Zheng, C., Liu, Y., Sun, F., Zhao, L., & Zhang, L. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.690124 · Cited by 24

Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward

Omranian, S., Nikoloski, Z., & Grimm, D. G. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.049 · Cited by 28

Design and biological testing of peptidic dimerization inhibitors of human Hsp90 that target the C-terminal domain

Bopp, B., Ciglia, E., Chaib, A. O., Groth, G., Gohlke, H., & Jose, J. (2016) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2016.01.005 · Cited by 22

Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type

Wang, K., Hu, G., Wu, Z., Su, H. L., Yang, J., & Kurgan, L. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21186879 · Cited by 24

Machine learning to predict de novo protein–protein interactions

Gaínza, P., Bunker, R. D., Townson, S. A., & Castle, J. C. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.04.013 · Cited by 26

Integrating Structural Information to Study the Dynamics of Protein-Protein Interactions in Cells

Wang, B., Xie, Z., Chen, J. C., & Wu, Y. (2018) · Structure · doi:10.1016/j.str.2018.07.010 · Cited by 23

Advances in Computational Methods for Protein–Protein Interaction Prediction

Xian, L., & Wang, Y. (2024) · Electronics · doi:10.3390/electronics13061059 · Cited by 24

Structure and Stability Insights into Tumour Suppressor p53 Evolutionary Related Proteins

Pagano, B., Jama, A., Martinez, P., Akanho, E., Bui, T. T. T., Drake, A. F., Fraternali, F., & Nikolova, P. V. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076014 · Cited by 23

Dissecting the Calcium-Induced Differentiation of Human Primary Keratinocytes Stem Cells by Integrative and Structural Network Analyses

Toufighi, K., Yang, J., Luis, N. M., Benitah, S. A., Lehner, B., Serrano, L. G., & Kiel, C. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004256 · Cited by 23

Exploring the shared pathogenic strategies of independently evolved effectors across distinct plant viruses

Li, L., Chen, J., & Sun, Z. (2024) · Trends in Microbiology · doi:10.1016/j.tim.2024.03.001 · Cited by 22

Prediction of protein–protein interaction sites from weakly homologous template structures using meta‐threading and machine learning

Maheshwari, S., & Bryliński, M. (2015) · Journal of Molecular Recognition · doi:10.1002/jmr.2410 · Cited by 22

Prediction of Protein‐Protein Interactions

Kotlyar, M., Rossos, A. E. M., & Jurišica, I. (2017) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.38 · Cited by 22

Analysis of physicochemical properties of protein–protein interaction modulators suggests stronger alignment with the “rule of five”

Truong, J. Q., George, A., & Holien, J. K. (2021) · RSC Medicinal Chemistry · doi:10.1039/d1md00213a · Cited by 24

Converging roles of PSENEN/PEN2 and CLN3 in the autophagy-lysosome system

Klein, M., Kaleem, A., Oetjen, S., Wünkhaus, D., Binkle-Ladisch, L., Schilling, S., Gjorgjieva, M., Scholz, R., Gruber-Schoffnegger, D., Storch, S., et al. (2021) · Autophagy · doi:10.1080/15548627.2021.2016232 · Cited by 22

Use of Mechanistic Models to Integrate and Analyze Multiple Proteomic Datasets

Stites, E. C., Aziz, M., Creamer, M. S., Hoff, D. D. V., Posner, R. G., & Hlavacek, W. S. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2015.02.030 · Cited by 22

Genome-wide inference of protein interaction network and its application to the study of crosstalk in Arabidopsis abscisic acid signaling

Zhang, F., Liu, S., Li, L., Zuo, K., Zhao, L., & Zhang, L. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.16.00057 · Cited by 22

Analysis of pharmacological mechanisms and targets mining of Wuzi-Yanzong-Wan for treating non-obstructive oligoasthenospermia

Zou, D., Meng, X.-D., Wang, B., Dai, Y., Yang, R., Suo, Y., Wu, Y., Yang, W., & Lin, R. (2019) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2019.108898 · Cited by 22

A Novel Network-Based Algorithm for Predicting Protein-Protein Interactions Using Gene Ontology

Hu, L., Wang, X., Huang, Y., Hu, P., & You, Z. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.735329 · Cited by 23

Using Neo4j for Mining Protein Graphs: A Case Study

Hoksza, D., & Jelı́nek, J. (2015) · International Workshop on Database and Expert Systems Applications (DEXA) · doi:10.1109/dexa.2015.59 · Cited by 22

Assessment of a Single Decoupling Alchemical Approach for the Calculation of the Absolute Binding Free Energies of Protein-Peptide Complexes

Kilburg, D., & Gallicchio, E. (2018) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2018.00022 · Cited by 22

Completing sparse and disconnected protein-protein network by deep learning

Huang, L., Liao, L., & Wu, C. H. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2112-7 · Cited by 21

DNN-PPI: A LARGE-SCALE PREDICTION OF PROTEIN–PROTEIN INTERACTIONS BASED ON DEEP NEURAL NETWORKS

Gui, Y., Wang, R., Wei, Y., & Wang, X. (2019) · Journal of Biological Systems · doi:10.1142/s0218339019500013 · Cited by 20

In Silico Prediction and Validation of Novel RNA Binding Proteins and Residues in the Human Proteome

Chowdhury, S., Zhang, J., & Kurgan, L. (2018) · PROTEOMICS · doi:10.1002/pmic.201800064 · Cited by 22

Interface-Based Structural Prediction of Novel Host-Pathogen Interactions

Guven‐Maiorov, E., Tsai, C., Ma, B., & Nussinov, R. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8736-8_18 · Cited by 21

Building protein-protein interaction networks for Leishmania species through protein structural information

Vasconcelos, C. R. D. S., Campos, T. D. L., & Rezende, A. M. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2105-6 · Cited by 22

Developmental and MAPK-responsive transcription factors drive distinct malignant subtypes and genetic dependencies in pancreatic cancerPreprint

Laise, P., Turunen, M. M., Garcia, A. C., Tomassoni, L., Maurer, H. C., Elyada, E., Schmierer, B., Worley, J., Kesner, J. S., Tan, X., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.27.357269 · Cited by 21

CyToStruct: augmenting the network visualization of cytoscape with the power of molecular viewers

Nepomnyachiy, S., Ben-Tal, N., & Kolodny, R. (2015) · Structure · doi:10.1016/j.str.2015.02.013 · Cited by 20

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20–27

Kilambi, K. P., Pacella, M. S., Xu, J., Labonte, J. W., Porter, J. R., Muthu, P., Drew, K., Kuroda, D., Schueler‐Furman, O., Bonneau, R., et al. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24425 · Cited by 20

Identifying protein complexes from heterogeneous biological data

Wu, M., Xie, Z., Li, X., Kwoh, C. K., & Zheng, J. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24365 · Cited by 20

Integrating 3D structural information into systems biology

Murray, D., Petrey, D. S., & Honig, B. H. (2021) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.100562 · Cited by 20

Genome‐wide identification studies – A primer to explore new genes in plant species

Safder, I., Shao, G., Sheng, Z., Hu, P., & Shengxiang, T. (2021) · Plant Biology · doi:10.1111/plb.13340 · Cited by 20

Developing Small-Molecule Inhibitors of Protein-Protein Interactions Involved in Viral Entry as Potential Antivirals for COVID-19

Buchwald, P. (2022) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2022.898035 · Cited by 20

An overview of bioinformatics methods for modeling biological pathways in yeast

Hou, J., Acharya, L. R., Zhu, D., & Cheng, J. (2015) · Briefings in Functional Genomics · doi:10.1093/bfgp/elv040 · Cited by 23

Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology: Algorithms and Software Tools

Hamid, A. R., & Tran, Q. N. (2015) · Cited by 20

Non-interacting proteins may resemble interacting proteins: prevalence and implications

Launay, G., Ceres, N., & Martin, J. (2017) · Scientific Reports · doi:10.1038/srep40419 · Cited by 20

Implicit Solvents for the Polarizable Atomic Multipole AMOEBA Force Field

Corrigan, R. A., Qi, G., Thiel, A. C., Lynn, J. R., Walker, B., Casavant, T. L., Lagardère, L., Piquemal, J., Ponder, J. W., Ren, P. Y., et al. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01286 · Cited by 23

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Fu, Y., Guo, Y., Wang, Y., Luo, J., Pu, X., Li, M., & Zhang, Z. (2015) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2015.03.003 · Cited by 19

Revealing the binding modes and the unbinding of 14-3-3σ proteins and inhibitors by computational methods

Hu, G., Cao, Z., Xu, S., Wang, W., & Wang, J. (2015) · Scientific Reports · doi:10.1038/srep16481 · Cited by 19

A critical review on the cellular and molecular interactions at the interface of zirconia-based biomaterials

Rahmati, M., & Mozafari, M. (2018) · Ceramics International · doi:10.1016/j.ceramint.2018.06.196 · Cited by 19

Machine-Learning Guided Discovery of Bioactive Inhibitors of PD1-PDL1 Interaction

Patil, S. P., Fattakhova, E., Hofer, J., Oravic, M., Bender, A., Brearey, J., Parker, D., Radnoff, M., & Smith, Z. (2022) · Pharmaceuticals · doi:10.3390/ph15050613 · Cited by 19

LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor

Asim, M. N., Ibrahim, M. A., Malik, M. I., Dengel, A. R., & Ahmed, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0270275 · Cited by 19

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

Kim, S., Mollaei, P., Antony, A., Magar, R., & Farimani, A. B. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01706 · Cited by 22

Characterizing of functional human coding RNA editing from evolutionary, structural, and dynamic perspectives

Solomon, O., Bazak, L., Levanon, E. Y., Amariglio, N., Unger, R., Rechavi, G., & Eyal, E. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24672 · Cited by 19

Scoring docking conformations using predicted protein interfaces

Esmaielbeiki, R., & Nebel, J. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-171 · Cited by 19

How do oncoprotein mutations rewire protein–protein interaction networks?

Bowler-Barnett, E., Wang, Z., & Ewing, R. M. (2015) · Expert Review of Proteomics · doi:10.1586/14789450.2015.1084875 · Cited by 19

ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds

Xie, T., Zhang, L., Zhang, S., Ouyang, L., Cai, H., & Liu, B. (2016) · Oncotarget · doi:10.18632/oncotarget.7015 · Cited by 19

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation

Armean, I. M., Lilley, K. S., Trotter, M. W. B., Pilkington, N. C. V., & Holden, S. B. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx803 · Cited by 19

Protein—protein binding supersites

Viswanathan, R., Fajardo, E. J., Steinberg, G., Haller, M. D., & Fiser, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006704 · Cited by 18

Network-based association of hypoxia-responsive genes with cardiovascular diseases

Wang, R., Oldham, W. M., & Loscalzo, J. (2014) · New Journal of Physics · doi:10.1088/1367-2630/16/10/105014 · Cited by 19

Protease-Inhibitor Interaction Predictions: Lessons on the Complexity of Protein–Protein Interactions

Fortelny, N., Butler, G. S., Overall, C. M., & Pavlidis, P. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.065706 · Cited by 19

How Klebsiella pneumoniae controls its virulence

Nguyen, T. N. T., Howells, G., & Short, F. L. (2025) · PLoS Pathogens · doi:10.1371/journal.ppat.1013499 · Cited by 19

A novel method for data fusion over entity-relation graphs and its application to protein–protein interaction prediction

Raimondi, D., Simm, J., Arany, Á., & Moreau, Y. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab092 · Cited by 19

Influence of Silica Exposure for Lung Silicosis Rat

Jiao, J., Li, L., Yao, W., Qin, W., Hao, C., & Lu, L. (2021) · Disease Markers · doi:10.1155/2021/6268091 · Cited by 18

Elucidating Common Structural Features of Human Pathogenic Variations Using Large-Scale Atomic-Resolution Protein Networks

Das, J., Lee, H. R., Sagar, A., Fragoza, R., Liang, J. H., Wei, X., Wang, X., Mort, M. E., Stenson, P. D., Cooper, D. N., et al. (2014) · Human Mutation · doi:10.1002/humu.22534 · Cited by 21

A novel conjoint triad auto covariance (CTAC) coding method for predicting protein-protein interaction based on amino acid sequence

Wang, X., Wang, R., Wei, Y., & Gui, Y. (2019) · Mathematical Biosciences · doi:10.1016/j.mbs.2019.04.002 · Cited by 17

An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model

Li, Y., Li, L., Wang, L., Yu, C.-Q., Wang, Z., & You, Z. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20143511 · Cited by 18

Evolutionary Model Selection and Parameter Estimation for Protein-Protein Interaction Network Based on Differential Evolution Algorithm

Huang, L., Liao, L., & Wu, C. H. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2366748 · Cited by 18

Predicting Peptide-Mediated Interactions on a Genome-Wide Scale

Chen, T. S., Petrey, D. S., Garzón, J. I., & Honig, B. H. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004248 · Cited by 18

End-to-end multitask learning, from protein language to protein features without alignmentsPreprint

Elnaggar, A., Heinzinger, M., Dallago, C., & Rost, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/864405 · Cited by 17

Computational Strategies to Combat COVID-19: Useful Tools to Accelerate SARS-CoV-2 and Coronavirus ResearchPreprint

Hufsky, F., Lamkiewicz, K., Almeida, A., Aouacheria, A., Arighi, C. N., Bateman, A. G., Baumbach, J., Beerenwinkel, N., Brandt, C., Cacciabue, M., et al. (2020) · Preprints.org · doi:10.20944/preprints202005.0376.v1 · Cited by 18

Recent progress and future challenges in structure-based protein-protein interaction prediction

Yuan, R., Zhang, J., Zhou, J., & Cong, Q. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.04.003 · Cited by 25

PIIMS Server: A Web Server for Mutation Hotspot Scanning at the Protein–Protein Interface

Wu, F., Yang, J., Mei, L., Wang, F., Hao, G., & Yang, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00966 · Cited by 17

Identifying binary protein-protein interactions from affinity purification mass spectrometry data

Zhang, X.-F., Ou-Yang, L., Hu, X. T., & Dai, D. (2015) · BMC Genomics · doi:10.1186/s12864-015-1944-z · Cited by 17

Essential role of Wnt5a‐Ror1/Ror2 signaling in metanephric mesenchyme and ureteric bud formation

Qi, X., Okinaka, Y., Nishita, M., & Minami, Y. (2016) · Genes to Cells · doi:10.1111/gtc.12342 · Cited by 17

Inference of protein-protein interaction networks from multiple heterogeneous data

Huang, L., Liao, L., & Wu, C. H. (2016) · EURASIP Journal on Bioinformatics and Systems Biology · doi:10.1186/s13637-016-0040-2 · Cited by 17

IAS: Interaction Specific GO Term Associations for Predicting Protein-Protein Interaction Networks

Yerneni, S., Khan, I., Wei, Q., & Kihara, D. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2476809 · Cited by 17

Rigid-Docking Approaches to Explore Protein–Protein Interaction Space

Matsuzaki, Y., Uchikoga, N., Ohue, M., & Akiyama, Y. (2016) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2016_41 · Cited by 18

Computational Prediction of Protein Complexes from Protein Interaction Networks

Srihari, S., Yong, C. H., & Wong, L. (2017) · doi:10.1145/3064650 · Cited by 17

AraPPINet: An Updated Interactome for the Analysis of Hormone Signaling Crosstalk in Arabidopsis thaliana

Zhao, J., Lei, Y., Hong, J., Zheng, C., & Zhang, L. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.00870 · Cited by 17

In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences

Burnside, D. J., Schoenrock, A., Moteshareie, H., Hooshyar, M., Basra, P., Hajikarimlou, M., Dick, K., Barnes, B., Kazmirchuk, T. D. D., Jessulat, M., et al. (2018) · iScience · doi:10.1016/j.isci.2018.11.038 · Cited by 19

Recent developments of sequence-based prediction of protein–protein interactions

Murakami, Y., & Mizuguchi, K. (2022) · Biophysical Reviews · doi:10.1007/s12551-022-01038-1 · Cited by 16

A data-driven interpretation of the stability of organic molecular crystals

Cersonsky, R. K., Pakhnova, M., Engel, E. A., & Ceriotti, M. (2023) · Chemical Science · doi:10.1039/d2sc06198h · Cited by 16

Pitfalls of machine learning models for protein–protein interaction networks

Lannelongue, L., & Inouye, M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae012 · Cited by 20

Deciphering the host-pathogen protein interface in chikungunya virus-mediated sickness

Rana, J., Sreejith, R., Gulati, S., Bharti, I., Jain, S., & Gupta, S. (2013) · Archives of Virology · doi:10.1007/s00705-013-1602-1 · Cited by 16

Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources

Taghipour, S., Zarrineh, P., Ganjtabesh, ‎., & Nowzari-Dalini, A. (2017) · BMC Bioinformatics · doi:10.1186/s12859-016-1422-x · Cited by 17

A scheme for inferring viral-host associations based on codon usage patterns identifies the most affected signaling pathways during COVID-19

Das, J. K., Chakraborty, S., & Roy, S. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103801 · Cited by 19

Proteins from Thermophilic Thermus thermophilus Often Do Not Fold Correctly in a Mesophilic Expression System Such as Escherichia coli

Kruglikov, A., Wei, Y., & Xia, X. (2022) · ACS Omega · doi:10.1021/acsomega.2c04786 · Cited by 16

Systems understanding of plant–pathogen interactions through genome-wide protein–protein interaction networks

Li, H., & Zhang, Z. (2016) · Frontiers of Agricultural Science and Engineering · doi:10.15302/j-fase-2016100 · Cited by 15

Functional, structural, and phylogenetic analysis of mitochondrial cytochrome b (cytb) in insects

Seddigh, S., & Darabi, M. (2017) · Mitochondrial DNA Part A · doi:10.1080/24701394.2016.1275596 · Cited by 18

Integrating Multimeric Threading With High-throughput Experiments for Structural Interactome of Escherichia coli

Gong, W., Guerler, A., Zhang, C., Warner, E., Li, C., & Zhang, Y. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.166944 · Cited by 13

Computational probing protein–protein interactions targeting small molecules

Wang, Y., Chen, S., Deng, N.-Y., & Wang, Y. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv528 · Cited by 15

Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis

Mahajan, G., & Mande, S. C. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1550-y · Cited by 16

Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis

Bansal, M., He, J., Peyton, M., Kustagi, M., Iyer, A., Comb, M. J., White, M. A., Minna, J. D., & Califano, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0208646 · Cited by 14

Biclustering‐based association rule mining approach for predicting cancer‐associated protein interactions

Dey, L., & Mukhopadhyay, A. (2019) · IET Systems Biology · doi:10.1049/iet-syb.2019.0045 · Cited by 15

Challenges and opportunities of automated protein‐protein docking: HDOCK server vs human predictions in CAPRI Rounds 38‐46

Yan, Y., He, J., Feng, Y., Lin, P., Tao, H., & Huang, S. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25874 · Cited by 16

Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge

Randhawa, V., & Pathania, S. (2020) · Briefings in Functional Genomics · doi:10.1093/bfgp/elaa015 · Cited by 15

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

Gemović, B., Šumonja, N., Davidović, R., Perović, V., & Veljković, N. (2018) · Current Medicinal Chemistry · doi:10.2174/0929867325666180214113704 · Cited by 16

Structural genomics and interactomics of 2019 Wuhan novel coronavirus, 2019-nCoV, indicate evolutionary conserved functional regions of viral proteinsPreprint

Cui, H., Gao, Z., Liu, M., Lu, S., Sun, M., Mkandawire, W. W., Narykov, O., Srinivasan, S., & Korkin, D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.10.942136 · Cited by 15

Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey

Zhong, Z., Barkova, A., & Mottin, D. (2025) · ACM Computing Surveys · doi:10.1145/3744237 · Cited by 20

Ultra sensitive firefly luciferase‐based protein‐protein interaction assay (FlimPIA) attained by hinge region engineering and optimized reaction conditions

Kurihara, M., Ohmuro‐Matsuyama, Y., Ayabe, K., Yamashita, T., Yamaji, H., & Ueda, H. (2015) · Biotechnology Journal · doi:10.1002/biot.201500189 · Cited by 15

A knowledge-based scoring function to assess quaternary associations of proteins

Dhawanjewar, A. S., Roy, A., & Madhusudhan, M. S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa207 · Cited by 18

Better Link Prediction for Protein-Protein Interaction Networks

Yuen, H. Y., & Jansson, J. (2020) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe50027.2020.00017 · Cited by 14

180 Integrating structural and systems biology: structure-based prediction of protein–protein interactions on a genome-wide scale

Zhang, Q. C., Petrey, D. S., & Honig, B. H. (2013) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2013.786422

A PrePPI way to make predictions

Montoya, M. (2012) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2445 · Cited by 4

Structure-based Prediction of Protein-protein Interaction Networks across Proteomes

Maheshwari, S. (2017) · doi:10.31390/gradschool_dissertations.4361

Predicting PPIs

Doerr, A. J. (2012) · Nature Methods · doi:10.1038/nmeth.2272 · Cited by 2

Computational Methods for the Prediction of Protein-Protein Interactions

Xia, J., Wang, S., & Lei, Y. (2010) · Protein and Peptide Letters · doi:10.2174/092986610791760405 · Cited by 26