ZDOCK server: interactive docking prediction of protein–protein complexes and symmetric multimers

Macromolecular modeling and design in Rosetta: recent methods and frameworks

Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0848-2 · Cited by 888

Irisin Mediates Effects on Bone and Fat via αV Integrin Receptors

Kim, H., Wrann, C. D., Jedrychowski, M., Vidoni, S., Kitase, Y., Nagano, K., Zhou, C., Chou, J., Parkman, V.-J. A., Novick, S. J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.025 · Cited by 638

Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2

Vreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jiménez-García, B., Bates, P. A., Fernandez-Recio, J., et al. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.016 · Cited by 424

Predicting Protein–Protein Interactions from the Molecular to the Proteome Level

Keskin, O., Tuncbag, N., & Gursoy, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00683 · Cited by 369

Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2022) · Protein Science · doi:10.1002/pro.4379 · Cited by 335

Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2021) · doi:10.1101/2021.10.23.465575 · Cited by 335

Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition

Lensink, M. F., Velankar, S., & Wodak, S. J. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25215 · Cited by 222

Improving peptide-protein docking with AlphaFold-Multimer using forced sampling

Johansson-Åkhe, I., & Wallner, B. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.959160 · Cited by 162

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Liu, X., Luo, Y., Li, P., Song, S., & Peng, J. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009284 · Cited by 140

Biogenic manganese oxide nanoparticle formation by a multimeric multicopper oxidase Mnx

Romano, C. A., Zhou, M., Song, Y., Wysocki, V. H., Dohnalkova, A. C., Kovarik, L., Paša-Tolić, L., & Tebo, B. M. (2017) · Nature Communications · doi:10.1038/s41467-017-00896-8 · Cited by 102

Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15

Liu, J., Guo, Z., Wu, T., Roy, R. S., Quadir, F., Chen, C., & Cheng, J. (2023) · Communications Biology · doi:10.1038/s42003-023-05525-3 · Cited by 86

Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling

Drake, Z. C., Seffernick, J. T., & Lindert, S. (2022) · Nature Communications · doi:10.1038/s41467-022-35593-8 · Cited by 85

CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2

Shor, B., & Schneidman-Duhovny, D. (2024) · Nature Methods · doi:10.1038/s41592-024-02174-0 · Cited by 85

TGF-β receptor I/II trafficking and signaling at primary cilia are inhibited by ceramide to attenuate cell migration and tumor metastasis

Gencer, S., Oleinik, N., Kim, J., Panneer Selvam, S., De Palma, R., Dany, M., Nganga, R., Thomas, R. J., Senkal, C. E., Howe, P. H., et al. (2017) · Science Signaling · doi:10.1126/scisignal.aam7464 · Cited by 83

Determining crystal structures through crowdsourcing and coursework

Horowitz, S., Koepnick, B., Martin, R., Tymieniecki, A., Winburn, A. A., Cooper, S., Flatten, J., Rogawski, D. S., Koropatkin, N. M., Hailu, T. T., et al. (2016) · Nature Communications · doi:10.1038/ncomms12549 · Cited by 75

Mitochondrial protein import is regulated by p17/PERMIT to mediate lipid metabolism and cellular stress

Oleinik, N., Kim, J., Roth, B. M., Selvam, S. P., Gooz, M., Johnson, R. H., Lemasters, J. J., & Ogretmen, B. (2019) · Science Advances · doi:10.1126/sciadv.aax1978 · Cited by 68

Advancing Peptide-Based Biorecognition Elements for Biosensors Using in-Silico Evolution

Xiao, X., Kuang, Z., Slocik, J. M., Tadepalli, S., Brothers, M., Kim, S., Mirau, P. A., Butkus, C., Farmer, B. L., Singamaneni, S., et al. (2018) · ACS Sensors · doi:10.1021/acssensors.8b00159 · Cited by 65

Natural ACE inhibitory peptides discovery from Spirulina (Arthrospira platensis) strain C1

Anekthanakul, K., Senachak, J., Hongsthong, A., Charoonratana, T., & Ruengjitchatchawalya, M. (2019) · Peptides · doi:10.1016/j.peptides.2019.170107 · Cited by 63

Synergistic effects of microbial transglutaminase and apple pectin on the gelation properties of pea protein isolate and its application to probiotic encapsulation

Wang, K., Sun, H., Cui, Z., Wang, J., Hou, J., Lu, F., & Liu, Y. (2024) · Food Chemistry · doi:10.1016/j.foodchem.2023.138232 · Cited by 55

Bioinformatics tools and benchmarks for computational docking and 3D structure prediction of RNA-protein complexes

Nithin, C., Ghosh, P., & Bujnicki, J. M. (2018) · Genes · doi:10.3390/genes9090432 · Cited by 48

A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers

Roy, R. S., Quadir, F., Soltanikazemi, E., & Cheng, J. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac063 · Cited by 45

Recent advances in predicting protein–protein interactions with the aid of artificial intelligence algorithms

Li, S., Wu, S., Wang, L., Li, F., Jiang, H., & Bai, F. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102344 · Cited by 42

Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes

Polonsky, K., Pupko, T., & Freund, N. T. (2023) · The Journal of Immunology · doi:10.4049/jimmunol.2300150 · Cited by 41

Improved Antibody‐Specific Epitope Prediction Using AlphaFold and AbAdapt**

(2022) · doi:10.1002/cbic.202200303 · Cited by 36

Are protein-protein interfaces special regions on a protein’s surface?

Tonddast-Navaei, S., & Skolnick, J. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4937428 · Cited by 31

Deep learning methods for 3D structural proteome and interactome modeling

Lee, D., Xiong, D., Wierbowski, S., Li, L., Liang, S., & Yu, H. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102329 · Cited by 26

Elucidation of genome-wide understudied proteins targeted by PROTAC-induced degradation using interpretable machine learning

Xie, L., & Xie, L. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010974 · Cited by 26

Protein oligomer modeling guided by predicted interchain contacts in CASP14

Baek, M., Anishchenko, I., Park, H., Humphreys, I. R., & Baker, D. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26197 · Cited by 18

Induced fit with replica exchange improves protein complex structure prediction

Harmalkar, A., Mahajan, S. P., & Gray, J. J. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010124 · Cited by 16

Rational Design by Structural Biology of Industrializable, Long-Acting Antihyperglycemic GLP-1 Receptor Agonists

Sun, L., Zheng, Z.-M., Shao, C.-S., Zhang, Z.-Y., Li, M.-W., Wang, L., Wang, H., Zhao, G.-H., & Wang, P. (2022) · Pharmaceuticals · doi:10.3390/ph15060740 · Cited by 17

Caveolin-1 knockout mitigates breast cancer metastasis to the lungs via integrin α3 dysregulation in 4T1-induced syngeneic breast cancer model

Singh, D. P., Pathak, R., Chintalaramulu, N., Pandit, A., Kumar, A., Ebenezer, P. J., Kumar, S., Duplooy, A., White, M. E., Jambunathan, N., et al. (2024) · Cancer Gene Therapy · doi:10.1038/s41417-024-00821-4 · Cited by 12

Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations

Wang, B., Lei, X., Tian, W., Perez-Rathke, A., Tseng, Y.-Y., & Liang, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad206 · Cited by 11

Molecular Interaction of Protein-Pigment C-Phycocyanin with Bovine Serum Albumin in a Gomphosis Structure Inhibiting Amyloid Formation

Luo, Y.-C., & Jing, P. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21218207 · Cited by 9

An improved structural model of the human iron exporter ferroportin. Insight into the role of pathogenic mutations in hereditary hemochromatosis type 4

Tortosa, V., Bonaccorsi di Patti, M. C., Brandi, V., Musci, G., & Polticelli, F. (2017) · Bio-Algorithms and Med-Systems · doi:10.1515/bams-2017-0029 · Cited by 9

Itraconazole Reversing Acquired Resistance to Osimertinib in NSCLC by Inhibiting the SHH/DUSP13B/p‐STAT3 Axis

Zheng, H., Tang, Y., Zang, H., Luo, J., Zhou, H., Zhan, Y., Zou, Y., Wen, Q., Ma, J., & Fan, S. (2025) · Advanced Science · doi:10.1002/advs.202409416 · Cited by 7

Applying Computational Protein Design to Engineer Affibodies for Affinity-controlled Delivery of Vascular Endothelial Growth Factor and Platelet-Derived Growth Factor

(2025) · doi:10.1021/acs.biomac.5c00097 · Cited by 5

PURA and GLUT1: Sweet partners for brain health

Colombo, R. B., Maxit, C., Martinelli, D., Anderson, M., Masone, D., & Mayorga, L. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2024.167261 · Cited by 5

Improved antibody-specific epitope prediction using AlphaFold and AbAdapt

Xu, Z., Davila, A., Wiamowski, J., Teraguchi, S., & Standley, D. M. (2022) · doi:10.1101/2022.05.21.492907 · Cited by 4

Structure-Based Deep Learning Framework for Modeling Human–Gut Bacterial Protein Interactions

Kiouri, D. P., Batsis, G. C., & Chasapis, C. T. (2025) · Proteomes · doi:10.3390/proteomes13010010 · Cited by 3

The Oncogenic EGFR–SHC1 Fusion Confers Insensitivity to EGFR-TKIs via Dual Activation of N-EGFR Kinase Domain and C-SHC1 Phosphorylation Sites in Lung Cancer

Zheng, J., Zhao, S., Zhan, J., Zhuang, W., Chen, M., Jiang, W., Huang, Y., He, J., Hu, L., Pang, L., et al. (2026) · Cancer Discovery · doi:10.1158/2159-8290.cd-25-1936 · Cited by 2

SHR-2005, an FcγR-Dependent CD40 Agonistic Antibody with Potent Antitumor Activities and Favorable Safety Profile

Wang, L.-M., Yang, C., Sun, X., Tian, Y., Zhang, J., Wang, X., Zhou, L., Wang, Z., Lin, Y., Wu, T., et al. (2025) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-24-0999 · Cited by 2

In silico design and validation of high-affinity RNA aptamers for SARS-CoV-2 comparable to neutralizing antibodies

Yang, Y., Qiao, L., Jiang, Y., Wang, Z., Zhang, D., Buratto, D., Huang, L., & Zhou, R. (2026) · eLife · doi:10.7554/elife.107785 · Cited by 2

HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses

Kravchenko, A., de Vries, S. J., Smaïl-Tabbone, M., & Chauvot de Beauchene, I. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05733-6 · Cited by 1

In silico design and validation of high-affinity RNA aptamers for SARS-CoV-2 comparable to neutralizing antibodies

Yang, Y., Qiao, L., Jiang, Y., Wang, Z., Zhang, D., Buratto, D., Huang, L., & Zhou, R. (2026) · eLife · doi:10.7554/elife.107785.4 · Cited by 1

Accurate protein-protein interactions modeling through physics-informed geometric invariant learning

(2026) · doi:10.1038/s42003-026-09809-2 · Cited by 1

Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partner

Choi, J. (2023) · iScience · doi:10.1016/j.isci.2023.106911 · Cited by 1

CurvePotGCN – A graph neural network to predict protein–protein interactions using surface curvature and electrostatic potential as node-features

Kadam, V., Bhargav, P., & Mukherjee, A. (2026) · Journal of Chemical Sciences · doi:10.1007/s12039-025-02459-7