Towards a structurally resolved human protein interaction network
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Abstract
Cellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology.
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AlphaFold-Multimer is being used to uncover protein-protein interactions on the scale of pathways and organisms.
“AF-M is being used to uncover PPIs on the scale of pathways and organisms15–24.”
From Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions (2025) · cites this paper as [17]
The Main section reports modeling 65,484 human protein interactions, supporting the organism-scale aspect. But Methods > Protein complex prediction says the study used FoldDock based on AlphaFold2, not AlphaFold-Multimer; the latter appears only in a reference. Thus this paper supports large-scale interaction modeling, but not the claim that AF-M was the method used. (LLM, conf=0.88)
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Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1131 · Cited by 17,749 · see citing works
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · see citing works
PhosphoSitePlus, 2014: mutations, PTMs and recalibrations
Hornbeck, P. V., Zhang, B., Murray, B., Kornhauser, J. M., Latham, V. M., & Skrzypek, E. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1267 · Cited by 3,243 · see citing works
Scoring function for automated assessment of protein structure template quality
Zhang, Y., & Skolnick, J. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20264 · Cited by 2,392 · see citing works
UniProt: the universal protein knowledgebase
The UniProt Consortium (2018) · Nucleic Acids Research · doi:10.1093/nar/gky092 · Cited by 2,043 · see citing works
The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases
Orchard, S. E., Ammari, M. G., Aranda, B., Breuza, L., Briganti, L., Broackes-Carter, F. C., Campbell, N. H., Chavali, G. B., Chen, C. C. L., del‐Toro, N., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1115 · Cited by 1,835 · see citing works
Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Christie, C. H., Dalenberg, K., Di Costanzo, L., Duarte, J. M., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1038 · Cited by 1,581 · see citing works
CORUM: the comprehensive resource of mammalian protein complexes—2009
Ruepp, A., Wägele, B., Lechner, M., Brauner, B., Dunger-Kaltenbach, I., Fobo, G., Frishman, G., Montrone, C., & Mewes, H. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp914 · Cited by 1,320 · see citing works
A reference map of the human binary protein interactome
Luck, K., Kim, D.-K., Lambourne, L., Spirohn, K., Begg, B. E., Bian, W., Brignall, R., Cafarelli, T., Campos-Laborie, F. J., Charloteaux, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2188-x · Cited by 1,197 · see citing works
Improved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works
Self-citationStructure-based prediction of protein–protein interactions on a genome-wide scale
Zhang, Q. C., Petrey, D., Deng, L., Qiang, L., Shi, Y., Thu, C. A., Bisikirska, B., Lefebvre, C., Accili, D., Hunter, T., et al. (2012) · Nature · doi:10.1038/nature11503 · Cited by 723 · see citing works
A structural biology community assessment of AlphaFold2 applications
Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675 · see citing works
Self-citationCORUM: the comprehensive resource of mammalian protein complexes—2019
Giurgiu, M., Reinhard, J., Brauner, B., Dunger-Kaltenbach, I., Fobo, G., Frishman, G., Montrone, C., & Ruepp, A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky973 · Cited by 595 · see citing works
Computed structures of core eukaryotic protein complexes
Humphreys, I. R., Pei, J., Baek, M., Krishnakumar, A., Anishchenko, I., Ovchinnikov, S., Zhang, J., Ness, T. J., Banjade, S., Bagde, S. R., et al. (2021) · Science · doi:10.1126/science.abm4805 · Cited by 542 · see citing works
FoldX 5.0: working with RNA, small molecules and a new graphical interface
Delgado, J., Radusky, L., Cianferoni, D., & Serrano, L. G. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz184 · Cited by 523 · see citing works
The functional landscape of the human phosphoproteome
Ochoa, D., Jarnuczak, A. F., Viéitez, C., Gehre, M., Soucheray, M., Mateus, A., Kleefeldt, A. A., Hill, A., Garcia-Alonso, L., Stein, F., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0344-3 · Cited by 491 · see citing works
Interactome3D: adding structural details to protein networks
Mosca, R., Céol, A., & Aloy, P. (2013) · Nature Methods · doi:10.1038/nmeth.2289 · Cited by 479 · see citing works
Systematic Functional Prioritization of Protein Posttranslational Modifications
Beltrão, P., Albanèse, V., Kenner, L., Swaney, D. L., Burlingame, A. L., Villén, J., Lim, W. A., Fraser, J. S., Frydman, J., & Krogan, N. J. (2012) · Cell · doi:10.1016/j.cell.2012.05.036 · Cited by 425 · see citing works
Three-dimensional reconstruction of protein networks provides insight into human genetic disease
Wang, X., Wei, X., Thijssen, B., Das, J., Lipkin, S. M., & Yu, H. (2012) · Nature Biotechnology · doi:10.1038/nbt.2106 · Cited by 385 · see citing works
The GTEx Consortium atlas of genetic regulatory effects across human tissuesPreprint
Aguet, F., Barbeira, A., Bonazzola, R., Brown, A. A., Castel, S. E., Jo, B., Kasela, S., Kim-Hellmuth, S., Liang, Y., Oliva, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/787903 · Cited by 379 · see citing works
Phosphorylation in protein-protein binding: effect on stability and function
Nishi, H., Hashimoto, K., & Panchenko, A. R. (2011) · Structure · doi:10.1016/j.str.2011.09.021 · Cited by 322 · see citing works
3did: a catalog of domain-based interactions of known three-dimensional structure
Mosca, R., Ceol, A. G. M., Stein, A., Olivella, R., & Aloy, P. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt887 · Cited by 307 · see citing works
Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set
Khadake, J., Meldal, B., Panni, S., Thorneycroft, D., Roey, K. V., Abbani, S., Salwiński, Ł., Pellegrini, M. A., Iannuccelli, M., Licata, L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07709-6 · Cited by 247 · see citing works
Protein interaction networks revealed by proteome coevolution
Cong, Q., Anishchenko, I., Ovchinnikov, S., & Baker, D. (2019) · Science · doi:10.1126/science.aaw6718 · Cited by 241 · see citing works
Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search
(2022) · doi:10.1038/s41467-022-33729-4 · Cited by 226 · see citing works
Self-citationComprehensive assessment of cancer missense mutation clustering in protein structures
Kamburov, A., Lawrence, M. S., Polak, P. P., Leshchiner, I., Lage, K., Golub, T. R., Lander, E. S., & Getz, G. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516373112 · Cited by 218 · see citing works
Efficient and robust proteome-wide approaches for cross-linking mass spectrometry
Klykov, O. V., Steigenberger, B. A., Pektaş, S., Fasci, D., Heck, A. J. R., & Scheltema, R. A. (2018) · Nature Protocols · doi:10.1038/s41596-018-0074-x · Cited by 163 · see citing works
Self-citationPhoX: An IMAC-Enrichable Cross-Linking Reagent
Steigenberger, B. A., Pieters, R. J., Heck, A. J. R., & Scheltema, R. A. (2019) · ACS Central Science · doi:10.1021/acscentsci.9b00416 · Cited by 150 · see citing works
Self-citationDrew, K., Wallingford, J. B., & Marcotte, E. M. (2021) · Molecular Systems Biology · doi:10.15252/msb.202010016 · Cited by 137 · see citing works
Gestaut, D. R., Roh, S., Ma, B., Pintilie, G. D., Joachimiak, Ł. A., Leitner, A., Walzthoeni, T., Aebersold, R. H., Chiu, W., & Frydman, J. (2019) · Cell · doi:10.1016/j.cell.2019.03.012 · Cited by 133 · see citing works
Self-citationHistone Interaction Landscapes Visualized by Crosslinking Mass Spectrometry in Intact Cell Nuclei
Fasci, D., van Ingen, H., Scheltema, R. A., & Heck, A. J. R. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.000924 · Cited by 128 · see citing works
Self-citationA Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces
Porta‐Pardo, E., García‐Alonso, L., Hrabe, T., Dopazo, J., & Godzik, A. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004518 · Cited by 126 · see citing works
A structural biology community assessment of AlphaFold 2 applications
Akdel, M., Pires, D. E. V., Porta Pardo, E., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2021) · doi:10.1101/2021.09.26.461876 · Cited by 122 · see citing works
Self-citationPlug-and-play analysis of the human phosphoproteome by targeted high-resolution mass spectrometry
Lawrence, R., Searle, B. C., Llovet, A., & Villén, J. (2016) · Nature Methods · doi:10.1038/nmeth.3811 · Cited by 115 · see citing works
Green, A. G., Elhabashy, H., Brock, K. P., Maddamsetti, R., Kohlbacher, O., & Marks, D. S. (2021) · Nature Communications · doi:10.1038/s41467-021-21636-z · Cited by 114 · see citing works
Prediction of protein assemblies, the next frontier: The CASP14‐CAPRI experiment
Lensink, M. F., Brysbaert, G., Mauri, T., Nadzirin, N., Velankar, S. S., Chaleil, R. A. G., Clarence, T., Bates, P. A., Kong, R., Liu, B., et al. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26222 · Cited by 105 · see citing works
Self-citationAn atlas of human kinase regulation
Ochoa, D., Jonikas, M., Lawrence, R., Debs, B. E., Selkrig, J., Typas, A., Villén, J., Santos, S. D. M., & Beltrão, P. (2016) · Molecular Systems Biology · doi:10.15252/msb.20167295 · Cited by 101 · see citing works
Structure of a human cap-dependent 48S translation pre-initiation complex
Eliseev, B., Yeramala, L., Leitner, A., Karuppasamy, M., Raimondeau, E., Huard, K., Alkalaeva, E. Z., Aebersold, R. H., & Schaffitzel, C. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky054 · Cited by 91 · see citing works
Self-citationBlind predictions of protein interfaces by docking calculations in CAPRI
Lensink, M. F., & Wodak, S. J. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22850 · Cited by 80 · see citing works
A precisely positioned MED12 activation helix stimulates CDK8 kinase activity
Klatt, F., Leitner, A., Kim, I. V., Ho-Xuan, H., Schneider, E. V., Langhammer, F., Weinmann, R., Müller, M., Huber, R., Meister, G., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1917635117 · Cited by 75 · see citing works
Self-citationSabath, K., Stäubli, M. L., Marti, S., Leitner, A., Moes, M., & Jonas, S. (2020) · Nature Communications · doi:10.1038/s41467-020-17232-2 · Cited by 61 · see citing works
Self-citationXLinkDB 2.0: integrated, large-scale structural analysis of protein crosslinking data
Schweppe, D. K., Zheng, C., Chavez, J. D., Navare, A., Wu, X., Eng, J. K., & Bruce, J. E. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw232 · Cited by 59 · see citing works
Self-citationSystematic identification of phosphorylation-mediated protein interaction switches
Betts, M. J., Wichmann, O., Utz, M., André, T., Petsalaki, E., Mínguez, P., Parca, L., Roth, F. P., Gavin, A., Peer Bork, B., et al. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005462 · Cited by 56 · see citing works
MaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity
Yugandhar, K., Wang, T., Leung, A. K.-Y., Lanz, M. C., Motorykin, I., Liang, J. H., Shayhidin, E. E., Smolka, M. B., Zhang, S., & Yu, H. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir119.001847 · Cited by 47 · see citing works
The human GID complex engages two independent modules for substrate recruitment
Mohamed, W. I., Park, S., Rabl, J., Leitner, A., Boehringer, D., & Peter, M. (2021) · EMBO Reports · doi:10.15252/embr.202152981 · Cited by 45 · see citing works
Self-citationMarchant, A., Cisneros, A. F., Dubé, A. K., Gagnon‐Arsenault, I., Ascencio, D., Jain, H., Aubé, S., Eberlein, C., Evans-Yamamoto, D., Yachie, N., et al. (2019) · eLife · doi:10.7554/elife.46754 · Cited by 42 · see citing works
Klykov, O. V., van der Zwaan, C., Heck, A. J. R., Meijer, A. B., & Scheltema, R. A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1911785117 · Cited by 34 · see citing works
Self-citationLimits and potential of combined folding and docking
Pozzati, G., Zhu, W., Bassot, C., Lamb, J., Kundrotas, P., & Elofsson, A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab760 · Cited by 25 · see citing works
Self-citationSubramaniam, V., Vincent, I. R., & Jothy, S. (2005) · Experimental and Molecular Pathology · doi:10.1016/j.yexmp.2005.08.004 · Cited by 18 · see citing works
Šoštarić, N., O’Reilly, F. J., Giansanti, P., Heck, A. J. R., Gavin, A., & van Noort, V. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.000892 · Cited by 15 · see citing works
Limits and potential of combined folding and docking using PconsDockPreprint
Pozzati, G., Zhu, W., Bassot, C., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.04.446442 · Cited by 13 · see citing works
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Flagged first, then by citations
2 of the 57 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Improved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works
Self-citationA structural biology community assessment of AlphaFold2 applications
Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675 · see citing works
Self-citationThe HADDOCK2.4 web server for integrative modeling of biomolecular complexes
Honorato, R. V., Trellet, M. E., Jiménez‐García, B., Schaarschmidt, J., Giulini, M., Reys, V., Koukos, P. I., Rodrigues, J., Karaca, E., van Zundert, G. C. P., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01011-0 · Cited by 653 · see citing works
AlphaFold2 and its applications in the fields of biology and medicine
Yang, Z., Zeng, X., Zhao, Y. C., & Chen, R. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01381-z · Cited by 606 · see citing works
Clustering predicted structures at the scale of the known protein universe
Barrio-Hernandez, I., Yeo, J., Jänes, J., Mirdita, M., Gilchrist, C. L. M., Wein, T., Varadi, M., Velankar, S., Beltrao, P., & Steinegger, M. (2023) · Nature · doi:10.1038/s41586-023-06510-w · Cited by 362 · see citing works
Self-citationProtein structure predictions to atomic accuracy with AlphaFold
Jumper, J., & Hassabis, D. (2022) · Nature Methods · doi:10.1038/s41592-021-01362-6 · Cited by 265 · see citing works
Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2
Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304302120 · Cited by 240 · see citing works
Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search
(2022) · doi:10.1038/s41467-022-33729-4 · Cited by 226 · see citing works
Self-citationDeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces
Réau, M., Renaud, N., Xue, L. C., & Bonvin, A. M. J. J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac759 · Cited by 190 · see citing works
Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
Stahl, K., Graziadei, A., Dau, T., Brock, O., & Rappsilber, J. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01704-z · Cited by 145 · see citing works
Discovery and significance of protein-protein interactions in health and disease
Greenblatt, J. F., Alberts, B., & Krogan, N. J. (2024) · Cell · doi:10.1016/j.cell.2024.10.038 · Cited by 171 · see citing works
An atlas of protein homo-oligomerization across domains of life
Schweke, H., Pacesa, M., Levin, T., Goverde, C. A., Kumar, P., Duhoo, Y., Dornfeld, L. J., Dubreuil, B., Georgeon, S., Ovchinnikov, S., et al. (2024) · Cell · doi:10.1016/j.cell.2024.01.022 · Cited by 144 · see citing works
Protein complexes in cells by AI‐assisted structural proteomics
(2023) · doi:10.15252/msb.202311544 · Cited by 147 · see citing works
Deep learning and protein structure modeling
Baek, M., & Baker, D. A. (2022) · Nature Methods · doi:10.1038/s41592-021-01360-8 · Cited by 124 · see citing works
Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation
Lee, C. Y., Hubrich, D., Varga, J. K., Schäfer, C., Welzel, M., Schumbera, E., Djokic, M., Strom, J. M., Schönfeld, J., Geist, J. L., et al. (2024) · Molecular Systems Biology · doi:10.1038/s44320-023-00005-6 · Cited by 105 · see citing works
“While in-cell XL-MS is a very elegant approach to obtain experimental information on PPI interfaces in unperturbed settings, it is still a method that is only accessible to few experts in the fi eld.”
InfluentialSystematic discovery of protein interaction interfaces using AlphaFold and experimental validation
Lee, C. Y., Hubrich, D., Varga, J. K., Schäfer, C., Welzel, M., Schumbera, E., Đokić, M., Strom, J. M., Schönfeld, J., Geist, J. L., et al. (2023) · doi:10.1101/2023.08.07.552219 · Cited by 105 · see citing works
From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2
Bret, H., Gao, J., Zea, D. J., Andreani, J., & Guerois, R. (2024) · Nature Communications · doi:10.1038/s41467-023-44288-7 · Cited by 103 · see citing works
Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions
Schmid, E. W., & Walter, J. C. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.01.034 · Cited by 120 · see citing works
Democratizing protein language models with parameter-efficient fine-tuning
Sledzieski, S., Kshirsagar, M., Baek, M., Dodhia, R., Lavista Ferres, J., & Berger, B. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2405840121 · Cited by 86 · see citing works
Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review
Lee, M. (2023) · Molecules · doi:10.3390/molecules28135169 · Cited by 93 · see citing works
Predicting protein-protein interactions in the human proteome
Zhang, J., Humphreys, I. R., Pei, J., Kim, J., Choi, C., Yuan, R., Durham, J., Liu, S., Choi, H.-J., Baek, M., et al. (2025) · Science · doi:10.1126/science.adt1630 · Cited by 87 · see citing works
Rui, H., Ashton, K. S., Min, J., Wang, C. W., & Potts, P. R. (2023) · RSC Chemical Biology · doi:10.1039/d2cb00207h · Cited by 82 · see citing works
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 80 · see citing works
The evolution of post-translational modifications
Bradley, D. T. C. (2022) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2022.101956 · Cited by 77 · see citing works
AlphaFold two years on: Validation and impact
Kovalevskiy, O. V., Mateos-Garcia, J. C., & Tunyasuvunakool, K. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2315002121 · Cited by 81 · see citing works
Enhanced Protein-Protein Interaction Discovery via AlphaFold-MultimerPreprint
Kim, A., Hu, Y., Comjean, A., Rodiger, J., Mohr, S. E., & Perrimon, N. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.19.580970 · Cited by 76 · see citing works
Rapid and sensitive protein complex alignment with Foldseek-Multimer
Kim, W., Mirdita, M., Karin, E. L., Gilchrist, C. L. M., Schweke, H., Söding, J., Levy, E. D., & Steinegger, M. (2025) · Nature Methods · doi:10.1038/s41592-025-02593-7 · Cited by 75 · see citing works
Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes
Wodak, S. J., Vajda, S., Lensink, M. F., Kozakov, D., & Bates, P. A. (2023) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-102622-084607 · Cited by 71 · see citing works
Deep learning for protein structure prediction and design—progress and applications
Jänes, J., & Beltrão, P. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00016-x · Cited by 71 · see citing works
Computational drug development for membrane protein targets
Li, H., Sun, X., Cui, W., Xu, M., Dong, J., Ekundayo, B., Ni, D., Rao, Z., Guo, L., Stahlberg, H., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01987-2 · Cited by 62 · see citing works
A paradigm shift in structural biology
Subramaniam, S., & Kleywegt, G. J. (2022) · Nature Methods · doi:10.1038/s41592-021-01361-7 · Cited by 55 · see citing works
Revolutionizing protein–protein interaction prediction with deep learning
Zhang, J., Durham, J., & Cong, Q. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102775 · Cited by 53 · see citing works
Davey, N. E., Simonetti, L., & Ivarsson, Y. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102593 · Cited by 53 · see citing works
Overview of AlphaFold2 and breakthroughs in overcoming its limitations
Wang, L., Wen, Z., Liu, S.-W., Zhang, L., Finley, C., Lee, H., & Fan, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108620 · Cited by 53 · see citing works
Cross-linking mass spectrometry for mapping protein complex topologies in situ
Lee, K., & O’Reilly, F. J. (2023) · Essays in Biochemistry · doi:10.1042/ebc20220168 · Cited by 53 · see citing works
New advances in cross-linking mass spectrometry toward structural systems biology
Yu, C., & Huang, L. (2023) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2023.102357 · Cited by 53 · see citing works
Xlink Mapping and AnalySis (XMAS) - Smooth Integrative Modeling in ChimeraXPreprint
Lagerwaard, I. M., Albanese, P., Jankevics, A., & Scheltema, R. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.21.489026 · Cited by 53 · see citing works
Self-citationXiong, D., Qiu, Y., Zhao, J., Zhou, Y., Lee, D., Gupta, S., Torres, M., Lü, W., Liang, S., Kang, J. J., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02428-4 · Cited by 49 · see citing works
Illuminating the dark protein-protein interactome
Tabar, M. S., Parsania, C., Chen, H., Su, X., Bailey, C. G., & Rasko, J. E. J. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100275 · Cited by 43 · see citing works
Protein interactions in human pathogens revealed through deep learning
Humphreys, I. R., Zhang, J., Baek, M., Wang, Y., Krishnakumar, A., Pei, J., Anishchenko, I. V., Tower, C. A., Jackson, B. A., Warrier, T., et al. (2024) · Nature Microbiology · doi:10.1038/s41564-024-01791-x · Cited by 46 · see citing works
Protein–protein interaction prediction methods: from docking-based to AI-based approaches
Tsuchiya, Y., Yamamori, Y., & Tomii, K. (2022) · Biophysical Reviews · doi:10.1007/s12551-022-01032-7 · Cited by 44 · see citing works
Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile
Bryant, P., & Noé, F. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012253 · Cited by 53 · see citing works
Self-citationStructural determinants of co-translational protein complex assembly
Mallik, S., Venezian, J., Lobov, A., Heidenreich, M., Garcia-Seisdedos, H., Yeates, T. O., Shiber, A., & Levy, E. D. (2025) · Cell · doi:10.1016/j.cell.2024.11.013 · Cited by 42 · see citing works
McMaster, B., Thorpe, C. J., Ogg, G. S., Deane, C. M., & Koohy, H. (2024) · Nature Methods · doi:10.1038/s41592-024-02240-7 · Cited by 41 · see citing works
Label-free visual proteomics: Coupling MS- and EM-based approaches in structural biology
Klykov, O. V., Kopylov, M., Carragher, B. O., Heck, A. J. R., Noble, A. J., & Scheltema, R. A. (2022) · Molecular Cell · doi:10.1016/j.molcel.2021.12.027 · Cited by 36 · see citing works
Self-citationThrift, W. J., Perera, J. F., Cohen, S., Lounsbury, N., Gurung, H. R., Rose, C. M., Chen, J., Jhunjhunwala, S., & Liu, K. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae123 · Cited by 36 · see citing works
Challenges in bridging the gap between protein structure prediction and functional interpretation
Varadi, M., Tsenkov, M., & Velankar, S. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26614 · Cited by 36 · see citing works
Pei, J., Zhang, J., & Cong, Q. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac527 · Cited by 35 · see citing works
AlphaFold2 enables accurate deorphanization of ligands to single-pass receptors
Banhos Danneskiold-Samsøe, N., Kavi, D., Jude, K. M., Nissen, S. B., Wat, L. W., Coassolo, L., Zhao, M., Santana-Oikawa, G. A., Broido, B. B., Garcia, K. C., et al. (2024) · Cell Systems · doi:10.1016/j.cels.2024.10.004 · Cited by 35 · see citing works
A Perspective on the Prospective Use of AI in Protein Structure Prediction
Versini, R., Sritharan, S., Fas, B. A., Tubiana, T., Aimeur, S., Henri, J., Erard, M., Nüße, O., Andréani, J., Baaden, M., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01361 · Cited by 33 · see citing works
A combinatorial approach to uncover an additional Integrator subunit
Offley, S. R., Pfleiderer, M. M., Zucco, A. J., Fraudeau, A., Welsh, S. A., Rażew, M., Galej, W. P., & Gardini, A. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112244 · Cited by 35 · see citing works
Yang, J., Li, Y., Wang, G., Chen, Z., & Wu, D. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3486216 · Cited by 35 · see citing works
Weeratunga, S., Gormal, R. S., Liu, M., Eldershaw, D., Livingstone, E. K., Malapaka, A., Wallis, T. P., Bademosi, A. T., Jiang, A., Healy, M. D., et al. (2024) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2023.105541 · Cited by 32 · see citing works
Trip, D. S. L., van Oostrum, M., Memon, D., Frommelt, F., Baptista, D., Panneerselvam, K., Bradley, G., Licata, L., Hermjakob, H., Orchard, S. E., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02659-z · Cited by 39 · see citing works
Self-citationComputed cancer interactome explains the effects of somatic mutations in cancers
Zhang, J., Pei, J., Durham, J., Bos, T., & Cong, Q. (2022) · Protein Science · doi:10.1002/pro.4479 · Cited by 31 · see citing works
Varadi, M., Bordin, N., Orengo, C., & Velankar, S. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102543 · Cited by 29 · see citing works
AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor
Trepte, P., Secker, C., Olivet, J., Blavier, J., Kostova, S., Maseko, S. B., Minia, I., Silva Ramos, E., Cassonnet, P., Golusik, S., et al. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00019-8 · Cited by 35 · see citing works
Deep learning insights into the architecture of the mammalian egg-sperm fusion synapse
Elofsson, A., Han, L., Bianchi, E., Wright, G. J., & Jovine, L. (2024) · eLife · doi:10.7554/elife.93131 · Cited by 36 · see citing works
Self-citationTT3D: Leveraging precomputed protein 3D sequence models to predict protein–protein interactions
Sledzieski, S., Devkota, K., Singh, R., Cowen, L., & Berger, B. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad663 · Cited by 26 · see citing works
Predicted mechanistic impacts of human protein missense variantsPreprint
Jänes, J., Müller, M., Selvaraj, S., Manoel, D., Stephenson, J. D., Gonçalves, C. I., Lafita, A., Polacco, B. J., Obernier, K., Alasoo, K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.29.596373 · Cited by 26 · see citing works
Self-citationContext-dependent, fuzzy protein interactions: Towards sequence-based insights
Fuxreiter, M. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102834 · Cited by 31 · see citing works
GPCRome-wide analysis of G-protein-coupling diversity using a computational biology approach
Matic, M., Miglionico, P., Tatsumi, M., Inoue, A., & Raimondi, F. (2023) · Nature Communications · doi:10.1038/s41467-023-40045-y · Cited by 27 · see citing works
The Transformative Power of AI as Future GPTs in Propelling Society Into a New Era of Advancement
Sharma, R. (2023) · IEEE Engineering Management Review · doi:10.1109/emr.2023.3315191 · Cited by 27 · see citing works
Computing the Human InteractomePreprint
Zhang, J., Humphreys, I. R., Pei, J., Kim, J., Choi, C., Yuan, R., Durham, J., Liu, S., Choi, H., Baek, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.01.615885 · Cited by 27 · see citing works
Substrate recognition principles for the PP2A-B55 protein phosphatase
Kruse, T., Garvanska, D. H., Varga, J. K., Garland, W., McEwan, B. C., Hein, J. B., Weisser, M. B., Benavides-Puy, I., Chan, C. B., Sotelo-Parrilla, P., et al. (2024) · Science Advances · doi:10.1126/sciadv.adp5491 · Cited by 26 · see citing works
Targeted protein degradation in CNS disorders: a promising route to novel therapeutics?
Kuemper, S., Cairns, A. G., Birchall, K., Yao, Z., & Large, J. M. (2024) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2024.1370509 · Cited by 27 · see citing works
Insights Into Glycobiology and the Protein-Glycan Interactome Using Glycan Microarray Technologies
Heimburg‐Molinaro, J., Mehta, A. Y., Tilton, C. A., & Cummings, R. D. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100844 · Cited by 26 · see citing works
The prospects and opportunities of protein structure prediction with AI
Tunyasuvunakool, K. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-022-00488-5 · Cited by 24 · see citing works
AlphaFold and what is next: bridging functional, systems and structural biology
Szczepski, K., & Jaremko, Ł. (2025) · Expert Review of Proteomics · doi:10.1080/14789450.2025.2456046 · Cited by 23 · see citing works
Molecular complex detection in protein interaction networks through reinforcement learning
Palukuri, M. V., Patil, R. S., & Marcotte, E. M. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05425-7 · Cited by 25 · see citing works
Emerging frontiers in protein structure prediction following the AlphaFold revolution
Rennie, M. L., & Oliver, M. R. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2024.0886 · Cited by 27 · see citing works
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 25 · see citing works
AlphaFold3, a secret sauce for predicting mutational effects on protein-protein interactionsPreprint
Lu, W., Zhang, J., Rao, J., Zhang, Z., & Zheng, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.25.595871 · Cited by 27 · see citing works
Afonso, A. L., Cavaleiro, C. T., Castanho, M. A. R. B., Neves, V., & Cavaco, M. C. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26073117 · Cited by 23 · see citing works
MAVISp: A Modular Structure-Based Framework for Protein Variant Effects
Arnaudi, M., Utichi, M., Degn, K., Tiberti, M., Beltrame, L., Krzesińska, K., Sánchez-Izquierdo Besora, P., Kiachaki, E., Scrima, S., Bauer, L., et al. (2022) · doi:10.1101/2022.10.22.513328 · Cited by 20 · see citing works
Structural coverage of the human interactome
Kosoglu, K., Aydin, Z., Tuncbag, N., Gursoy, A., & Keskin, O. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad496 · Cited by 21 · see citing works
Bias in, bias out – AlphaFold-Multimer and the structural complexity of protein interfaces
Strom, J. M., & Luck, K. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103002 · Cited by 22 · see citing works
Multimeric protein interaction and complex prediction: Structure, dynamics and function
Lu, D., Yu, S., Huang, Y., & Gong, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.009 · Cited by 20 · see citing works
Robustness of microbiome function
Lee, K. K., Park, Y., & Kuehn, S. (2023) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2023.100479 · Cited by 20 · see citing works
The solute carrier superfamily interactome
Frommelt, F., Ladurner, R., Goldmann, U., Wolf, G., Inglés‐Prieto, Á., Liñeiro, E., Gelová, Z., Hopp, A.-K., Christodoulaki, E., Teoh, S. T., et al. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00109-1 · Cited by 20 · see citing works
hu.MAP3.0: atlas of human protein complexes by integration of >25,000 proteomic experiments
Fischer, S. N., Claussen, E. R., Kourtis, S., Sdelci, S., Orchard, S., Hermjakob, H., Kustatscher, G., & Drew, K. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00121-5 · Cited by 25 · see citing works
Tang, R., Yong, Z., Jiang, S., Chen, X., Liu, Y., Zhang, Y., Sun, G., & Wang, W. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.11.006 · Cited by 19 · see citing works
Badonyi, M., & Marsh, J. A. (2025) · Nature Communications · doi:10.1038/s41467-025-63234-3 · Cited by 19 · see citing works
New opportunities in integrative structural modeling
Ziemianowicz, D. S., & Kosiński, J. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102488 · Cited by 16 · see citing works
Chemical cross-linking and mass spectrometry enabled systems-level structural biology
Botticelli, L., Bakhtina, A. A., Kaiser, N. K., Keller, A., McNutt, S. W., Bruce, J. E., & Feixia Chu, C. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102872 · Cited by 18 · see citing works
Self-citationDecoding the functional impact of the cancer genome through protein–protein interactions
Fu, H., Mo, X., & Ivanov, A. A. (2025) · Nature reviews. Cancer · doi:10.1038/s41568-024-00784-6 · Cited by 21 · see citing works
CASP16 Protein Monomer Structure Prediction Assessment
Yuan, R., Zhang, J., Kryshtafovych, A., Schaeffer, R. D., Zhou, J., Cong, Q., & Grishin, N. V. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70031 · Cited by 19 · see citing works
Dörig, C., Marulli, C., Peskett, T. R., Volkmar, N., Pantolini, L., Studer, G., Paleari, C., Frommelt, F., Schwede, T. F., de Souza, N., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02432-8 · Cited by 18 · see citing works
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 20 · see citing works
Protein structure-based evaluation of missense variants: Resources, challenges and future directions
David, A., & Sternberg, M. J. E. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102600 · Cited by 18 · see citing works
AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility
Martin, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01805 · Cited by 19 · see citing works
Selected Approaches to Disrupting Protein–Protein Interactions within the MAPK/RAS Pathway
Harwood, S. J., Smith, C. R., Lawson, J. D., & Ketcham, J. M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24087373 · Cited by 16 · see citing works
AlphaPulldown2—a general pipeline for high-throughput structural modeling
Molodenskiy, D., Maurer, V. J., Yu, D., Chojnowski, G., Bienert, S., Tauriello, G., Gilep, K., Schwede, T., & Kosinski, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf115 · Cited by 15 · see citing works
Barghout, R. A., Xu, Z.-Q. J., Betala, S., & Mahadevan, R. (2023) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2023.103007 · Cited by 15 · see citing works
Translation efficiency covariation identifies conserved coordination patterns across cell types
Liu, Y., Rao, S., Hoskins, I., Geng, M., Zhao, Q., Chacko, J., Ghatpande, V., Qi, K., Persyn, L., Wang, J., et al. (2026) · Nature Biotechnology · doi:10.1038/s41587-025-02718-5 · Cited by 15 · see citing works
Using metagenomic data to boost protein structure prediction and discovery
Hou, Q., Pucci, F., Pan, F., Xue, F.-Z., Rooman, M., & Feng, Q. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.12.030 · Cited by 16 · see citing works
Integrating Large-Scale Protein Structure Prediction into Human Genetics Research
Marrero, M. C., Jänes, J., Baptista, D., & Beltrão, P. (2024) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120622-020615 · Cited by 13 · see citing works
Accelerating protein–protein interaction screens with reduced AlphaFold-Multimer sampling
Bellinzona, G., Sassera, D., & Bonvin, A. M. J. J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae153 · Cited by 13 · see citing works
Global profiling of arginine reactivity and ligandability in the human proteome
Wang, Y., Hu, T., Zhu, L., Xie, S., Yang, X., Xu, C., Zhai, Y., Li, Y., Huang, X., Yang, B., et al. (2026) · Nature Chemistry · doi:10.1038/s41557-025-02012-6 · Cited by 12 · see citing works
3 = 1 + 2: how the divide conquered de novo protein structure prediction and what is next?
Zhou, Y., Litfin, T., & Zhan, J. (2023) · National Science Review · doi:10.1093/nsr/nwad259 · Cited by 14 · see citing works
- Authors
- Burke, D. F., Bryant, P., Barrio-Hernandez, I., Memon, D., Pozzati, G., Shenoy, A., Zhu, W., Dunham, A. S., Albanese, P., Keller, A., Scheltema, R. A., Bruce, J. E., Leitner, A., Kundrotas, P., Beltrao, P., & Elofsson, A.
- Affiliations
- European Bioinformatics Institute (GB)
- Stockholm University (SE)
- Utrecht University (NL)
- University of Washington (US)
- ETH Zurich (CH)
- University of Kansas (US)
- Corresponding author
- Kundrotas, Petras
- Elofsson, Arne
- ORCID
- Venue
- Nature Structural & Molecular Biology
- Published
- 2023-01-23
- Volume
- 30
- Issue
- 2
- Pages
- 216-225
- ISSN
- 1545-9985 (ISSN-L)
- 1545-9993
- PMC
- PMC9935395
- OpenAlex
- W4317802023
- Cited by
- 269
- References
- 57
- Citations by year
- 2026: 50 · 2025: 60 · 2024: 80 · 2023: 58 · 2022: 21
- FWCI
- 22.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Structural & Molecular Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KU ScholarWorks (The University of Kansas) (submittedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- ETH Zürich Research Collection
- Funders
- European Commission (823839, HORIZON2020)
- Eidgenössische Technische Hochschule Zürich
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (741.018.201)
- Linköpings Universitet (SNIC 2021/5-297)
- Knut och Alice Wallenbergs Stiftelse
- Vetenskapsrådet (2021-03979, 2016-03798, Berzelius-2021-29)
- Stockholms Universitet
- ETH Zürich Foundation
- Helmut Horten Stiftung
- National Supercomputer Centre, Linköpings Universitet
- National Institute on Aging (R56AG070096, R56AG078279)
- National Heart, Lung, and Blood Institute (R01HL144778)
- National Institute of General Medical Sciences (R35GM136255)
- Topics
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein-protein interaction network, AlphaFold2, interface residues, pathogenic variants, phosphorylation sites, co-regulation, protein complexes, molecular machines
- MeSH terms
- Humans, Mutation, Signal Transduction, Computational Biology, Protein Interaction Maps
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