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‘The entire protein universe’: AI predicts shape of nearly every known protein
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“In fact, precalculated structures for 200 million protein sequences are already available online,282”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [282]
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Über die Statistik verketteter Vorgänge
Eggenberger, F., & Pólya, G. (1923) · ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik · doi:10.1002/zamm.19230030407 · Cited by 497
Harrer, S. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104512 · Cited by 480
“…of our times, DeepMind ’ s AlphaFold system capable of predicting structure and thus biological function of virtually any protein in existence, 26 we are potentially looking at one of the most transformative and powerful tools hu-mankind will ever have created to understand and navigate…”
BackgroundAhdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02272-z · Cited by 439
“The model has been made available to the public with DeepMind’s official open-source implementation, which has been used to predict the structures of hundreds of millions of proteins (Tunyasuvunakool et al. 2021, Varadi et al. 2021, Callaway 2022).”
MethodUsing evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf
Yariv, B., Yariv, E., Kessel, A., Masrati, G., Chorin, A. B., Martz, E., Mayrose, I., Pupko, T., & Ben‐Tal, N. (2023) · Protein Science · doi:10.1002/pro.4582 · Cited by 425
“In this respect, it is noteworthy that AlphaFold structures are now available for most UniProt proteins (Callaway 2022).”
BackgroundTerwilliger, T. C., Liebschner, D., Croll, T. I., Williams, C. J., McCoy, A. J., Poon, B. K., Afonine, P. V., Oeffner, R. D., Richardson, J. S., Read, R. J., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02087-4 · Cited by 411
“…using AlphaFold 1 , RoseTTAFold 2 and related methods 3 are far more accurate than previous generations of prediction algorithms 4 , bringing much closer to reality the biological understanding that could be derived from knowing the three-dimensional structures of all macromolecules 1,2,5–9 .”
Emerging opportunities and challenges for the future of reservoir computing
Yan, M., Huang, C., Bienstman, P., Peter Tiňo, B., Lin, W. J., & Sun, J. (2024) · Nature Communications · doi:10.1038/s41467-024-45187-1 · Cited by 414
Resnik, D. B., & Hosseini, M. (2025) · AI and Ethics · doi:10.1007/s43681-024-00493-8 · Cited by 228
“One of the most impressive feats accomplished by ML systems is their contribution to solving the protein folding problem [41].”
InfluentialSynthetic extracellular matrices with function-encoding peptides
Ligorio, C., & Mata, Á. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00055-3 · Cited by 184
“Indeed, in 2022, the artificial intelligence network AlphaFold predicted the structure of >200 million proteins from ~1 million species, covering almost every known protein 223 .”
The state of quantum computing applications in health and medicine
Flöther, F. F. (2023) · Research Directions Quantum Technologies · doi:10.1017/qut.2023.4 · Cited by 128
“…imperative to understand the quickly growing and increasingly heterogeneous health-relevant data that are becoming available, particularly real-world data (RWD) such as information from electronic health records (EHRs), claims, disease registries, and fitness trackers (Real-World Evidence, 2022).”
BackgroundArt in an age of artificial intelligence
Chatterjee, A. K. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1024449 · Cited by 151
“For example, in biology, AI recently solved the complex problem of three-dimensional protein folding from a two-dimensional code (Callaway, 2022).”
BackgroundEvaluation of AlphaFold2 structures as docking targets
Holcomb, M., Chang, Y., Goodsell, D. S., & Forli, S. (2022) · Protein Science · doi:10.1002/pro.4530 · Cited by 112
“Recently these predictions were expanded to cover virtually every known protein sequence (Callaway, 2022; Varadi et al., 2022).”
BackgroundJasmonate perception: Ligand–receptor interaction, regulation, and evolution
Hu, S., Yu, K., Yan, J., Shan, X., & Xie, D. (2022) · Molecular Plant · doi:10.1016/j.molp.2022.08.011 · Cited by 86
Bartolec, T. K., Vázquez-Campos, X., Norman, A. R., Luong, C., Johnson, M., Payne, R. J., Wilkins, M. R., Mackay, J. P., & Low, J. K. K. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2219418120 · Cited by 83
“In lieu of experimental structures, machine learning-based structural modelers such as AlphaFold2 (6) have been shown to be highly accurate under controlled tests (7) and have greatly expanded the coverage of structural proteomes across a range of species (8, 9).”
InfluentialDemeter, K., Linke, R. B., Ballesté, E., Reischer, G. H., Mayer, R., Vierheilig, J., Kolm, C., Stevenson, M. E., Derx, J., Kirschner, A. K. T., et al. (2023) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuad028 · Cited by 81
Using AlphaFold Predictions in Viral Research
Gutnik, D., Evseev, P. V., Анатольевич, М. К., & Shneider, M. M. (2023) · Current Issues in Molecular Biology · doi:10.3390/cimb45040240 · Cited by 65
“As reported by the EMBL’s European Bioinformatics Institute, 35% of the more than 214 million AF2 predictions have been found to be very accurate [60], which indicates that its predictions are often not inferior to those obtained experimentally.”
ResultAccelerating crystal structure determination with iterative AlphaFold prediction
Terwilliger, T. C., Afonine, P. V., Liebschner, D., Croll, T. I., McCoy, A. J., Oeffner, R. D., Williams, C. J., Poon, B. K., Richardson, J. S., Read, R. J., et al. (2023) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s205979832300102x · Cited by 62
Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50
Ozden, B., Kryshtafovych, A., & Karaca, E. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26598 · Cited by 51
“27,28 Since the release of AF2, scientists have sought to incorporate this framework into their modeling pipelines.”
BackgroundTerwilliger, T. C., Liebschner, D., Croll, T. I., Williams, C. J., McCoy, A. J., Poon, B. K., Afonine, P. V., Oeffner, R. D., Richardson, J. S., Read, R. J., et al. (2022) · doi:10.1101/2022.11.21.517405 · Cited by 411
Li, J., Kang, G., Wang, J., Yuan, H., Wu, Y., Meng, S., Wang, P., Zhang, M., Wang, Y., Feng, Y., et al. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.125733 · Cited by 50
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Jussupow, A., & Kaila, V. R. I. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01027 · Cited by 42
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Nielsen, J. R., Weusthuis, R. A., & Huang, W. E. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108102 · Cited by 48
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Gupta, Y. A., Savytskyi, O. V., Coban, M., Venugopal, A., Pleqi, V., Weber, C. A., Chitale, R. A., Durvasula, R. V., Hopkins, C. E., Kempaiah, P., et al. (2022) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2022.101151 · Cited by 40
“…have also shown a decrease in Spike-ACE2 interaction as their binding site is closer to the interface compare to antagonists e.g. Losartan/Valsartan that bind in the catalytic core and have no positive effect as reported in multiple trials (Geriak et al., 2021; Puskarich et al., 2021, 2022).”
BackgroundMethodQuantum computing research in medical sciences
Alrashed, S., & Min‐Allah, N. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101606 · Cited by 40
Ke, W., Crist, R. M., Clogston, J. D., Stern, S. T., Dobrovolskaia, M. A., Grodzinski, P. A., & Jensen, M. A. (2022) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2022.114591 · Cited by 33
Hu, Y., & Buehler, M. J. (2022) · ACS Nano · doi:10.1021/acsnano.2c07681 · Cited by 26
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Brinkhaus, H. O., Rajan, K., Schaub, J., Zielesny, A., & Steinbeck, C. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102542 · Cited by 29
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Wang, J., Chen, L., Qin, S., Xie, M., Luo, S., & Li, W. (2023) · Biotechnology Journal · doi:10.1002/biot.202300256 · Cited by 25
“[101] However, AlphaFold has some limitations, such as lack of partner proteins or multimer interactions in the database, low ability to predict only individual protein states, and imprecise capture of protein states by AI.”
BackgroundExperimental phasing opportunities for macromolecular crystallography at very long wavelengths
Omari, K. E., Duman, R., Mykhaylyk, V. B., Orr, C. M., Latimer-Smith, M., Winter, G., Grama, V., Qu, F., Bountra, K., Kwong, H.-S., et al. (2023) · Communications Chemistry · doi:10.1038/s42004-023-01014-0 · Cited by 26
“Of the current ~200 million predictions from AlphaFold2, ~35% are matching experimentally determined structures, and another ~45% are considered to be accurate enough for many applications 8 , resulting in ~20% of structures still requiring phasing.”
Structure-driven protein engineering for production of valuable natural products
Wang, Y., Yu, L., Shao, J., Zhu, Z., & Zhang, L. (2022) · Trends in Plant Science · doi:10.1016/j.tplants.2022.11.004 · Cited by 23
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Castro-Rodríguez, J. A., Rodríguez‐Sotres, R., & Farrés, A. (2023) · Catalysts · doi:10.3390/catal13030591 · Cited by 25
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Radoux, C. J., Vianello, F., McGreig, J. E., Desai, N., & Bradley, A. R. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.958378 · Cited by 26
“Recently, more than 200 million AF2 structures have been released across more than 1 million species (Callaway 2022), greatly increasing the scope for structure-based assessment; however, care must be taken.”
BackgroundResnik, D. B., & Hosseini, M. (2023) · doi:10.31235/osf.io/rbg9z · Cited by 20
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Cruz‐Aguilar, M. A. (2025) · AI & Society · doi:10.1007/s00146-025-02658-3 · Cited by 23
Μaniotis, Ν., Maragakis, M., & Vordos, N. (2025) · Nanoscale Advances · doi:10.1039/d5na00258c · Cited by 20
Parse, N., & Pinitsoontorn, S. (2023) · APL Materials · doi:10.1063/5.0160055 · Cited by 18
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Feng, K., Yi, Z., & Xu, B. (2025) · Cancer Innovation · doi:10.1002/cai2.159 · Cited by 19
Patterns Lead the Way to Far-from-Equilibrium Materials
Knoll, P., Ouyang, B., & Steinbock, O. (2023) · ACS Physical Chemistry Au · doi:10.1021/acsphyschemau.3c00050 · Cited by 9
Melo, M. C. R., & Bernardi, R. C. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2023.01.042 · Cited by 15
Pathfinder: Protein folding pathway prediction based on conformational sampling
Huang, Z., Cui, X., Xia, Y., Zhao, K., & Zhang, G. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011438 · Cited by 13
“The AlphaFold database [4] and recent predictions of more than 200 million protein structures provide reference structure information for nearly every known protein [5].”
BackgroundNedeljković, M., Kreutzberger, M. A. B., Postel, S., Bonsor, D., Xing, Y., Jacob, N., Schuler, W. J., Egelman, E. H., & Sundberg, E. J. (2023) · PLOS Pathogens · doi:10.1371/journal.ppat.1010979 · Cited by 13
“Recently, Alpha-Fold structure predictions for over 200 million proteins became available [35].”
Key Proteomics Tools for Fundamental and Applied Microalgal Research
Plouviez, M., & Dubreucq, É. (2024) · Proteomes · doi:10.3390/proteomes12020013 · Cited by 13
“Currently, more than 1 million computed protein structures have been added to the Protein Databank (PDB, https://www.rcsb.org/; accessed on 1 April 2024), the main reference deposit of protein structures, and almost all known proteins have had their structure calculated [138].”
BackgroundMethodOpportunities and Challenges for Nanomaterials as Vaccine Adjuvants
Li, Z., Chen, P., Qu, A., Sun, M., Xu, L., Xu, C., Hu, S., & Kuang, H. (2025) · Small Methods · doi:10.1002/smtd.202402059 · Cited by 14
N -Myristoyltransferase, a Potential Antifungal Candidate Drug-Target for Aspergillus flavus
Wang, Y., Lin, R., Liu, M., Wang, S., Chen, H., Zeng, W., Nie, X., & Wang, S. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.04212-22 · Cited by 12
“structures for plants, bacteria, animals, and other organisms (46).”
BackgroundTowards a General Intermolecular Binding Affinity CalculatorPreprint
Li, W. (2022) · Preprints.org · doi:10.20944/preprints202208.0213.v1 · Cited by 10
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Hiefinger, C., Mandl, S., Wieland, M., et al. (2023) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2022.12.003 · Cited by 10
Prediction Promises: Towards a Metaphorology of Artificial Intelligence
Möck, L. A. (2022) · Journal of Aesthetics and Phenomenology · doi:10.1080/20539320.2022.2143654 · Cited by 12
Molecular Dynamics Simulation Methods to Study Structural Dynamics of Proteins
Kumar, A., & Ojha, K. K. (2023) · Springer eBooks · doi:10.1007/978-981-99-2079-2_5 · Cited by 10
Just Keep Rolling?—An Encompassing Review towards Accelerated Vaccine Product Life Cycles
(2023) · doi:10.3390/vaccines11081287 · Cited by 11
“In collaboration with EMBL’s European Bioinformatics Institute, DeepMind made the predictions of over 200 million UniProt entries available, thereby revolutionizing basic and applied research [84].”
Outer membrane β‐barrel structure prediction through the lens of AlphaFold2
Topitsch, A., Schwede, T., & Pereira, J. (2024) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26552 · Cited by 9
“11,12 AF2 is a deep neural network with two attention-based transformation modules, where evolutionary, physical, and geometric information is used to perform end-to-end protein structure prediction.”
MethodKo, S., Kim, J., Lim, J., Lee, S., Park, J. Y., Woo, J., Scott-Nevros, Z. K., Kim, J. R., Yoon, H., & Kim, D. (2023) · mSystems · doi:10.1128/msystems.00943-23 · Cited by 8
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Parse, N., Recatala‐Gomez, J., Zhu, R., Low, A. K. Y., Hippalgaonkar, K., Tomoya, M. A. T. O., Katsura, Y., & Pinitsoontorn, S. (2024) · Advanced Theory and Simulations · doi:10.1002/adts.202400308 · Cited by 9
Shen, J., Wu, W., Wang, K., et al. (2024) · mBio · doi:10.1128/mbio.03414-23 · Cited by 8
Fungal carboxylate transporters: recent manipulations and applications
Wu, T., Li, J., & Tian, C. (2023) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-023-12720-z · Cited by 6
Kullik, H., Martin, U., Schaub, J., Loidl‐Stahlhofen, A., & Zielesny, A. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00797 · Cited by 8
Cryo-EM structure of native honey bee vitellogenin
Montserrat-Canals, M., Schnelle, K., Leipart, V., Halskau, Ø., Amdam, G. V., Moeller, A., Cunha, E. S., & Luecke, H. (2025) · Nature Communications · doi:10.1038/s41467-025-58575-y · Cited by 7
“The relevance of Vg as a target for AI-based technologies to predict protein structures is highlighted by an article where it was used to exemplify the upcoming revolution in structural biology 68 .”
Zhang, D., Xu, Z., Li, J., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c12083 · Cited by 7
Tentonin 3 is a pore-forming subunit of a slow inactivation mechanosensitive channel
Pak, S., Ryu, H., Lim, S., Nguyen, T.-L., Yang, S., Kang, S., Yu, Y. G., Woo, J., Kim, C., Fenollar-Ferrer, C., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114334 · Cited by 6
Stepwise order in protein complex assembly: approaches and emerging themes
Brown, M. T., & McMurray, M. A. (2025) · Open Biology · doi:10.1098/rsob.240283 · Cited by 5
Information, Coding, and Biological Function: The Dynamics of Life
Cartwright, J. H. E., Čejková, J., Fimmel, E., et al. (2024) · Artificial Life · doi:10.1162/artl_a_00432 · Cited by 5
“The availability of an almost complete proteome database (Callaway, 2022) that includes spatial structures opens immediate opportunities for applied research; many laboratories are already using these data, for instance, to develop life-saving cures for diseases that disproportionately affect the…”
BackgroundArtificial Intelligence Could Revolutionize Science—If We Can Trust It
Leslie, M. (2024) · Engineering · doi:10.1016/j.eng.2024.03.002 · Cited by 5
Analysis of proteins in the light of mutations
Vila, J. A. (2023) · European Biophysics Journal · doi:10.1007/s00249-024-01714-y · Cited by 5
“…to reveal not dozens, hundreds, or thousands, but millions of plausible tridimensional protein structures (Tunyasuvunakool et al. 2021; Varadi et al. 2022; Callaway 2022a), regardless of the roughly 2700 protein folds that exist—a number that is, as far as we know, fairly limited (Liu et al. 2004).”
NMR structure verifies the eponymous zinc finger domain of transcription factor ZNF750
Rua, A. J., Whitehead, R. D., & Alexandrescu, A. T. (2023) · Journal of Structural Biology X · doi:10.1016/j.yjsbx.2023.100093 · Cited by 5
RETRACTED ARTICLE: Decoding intelligence via symmetry and asymmetry
Fu, J., & Hsiao, C.-A. (2024) · Scientific Reports · doi:10.1038/s41598-024-62906-2 · Cited by 4
“However, since we demonstrated that intelligence was not a mysterious thing, current advances illustrate this point well—artificial intelligence is gradually catching up with humans in all aspects 35–39 .”
RetractedReservoir computing with memristors
(2022) · Nature Electronics · doi:10.1038/s41928-022-00867-y · Cited by 4
Bartolec, T. K., Vázquez-Campos, X., Norman, A., Luong, C., Payne, R., Wilkins, M., Mackay, J., & Low, J. (2022) · bioRxiv · doi:10.1101/2022.11.16.516813 · Cited by 1
“With the recent release of more than 200 million predicted structures by AlphaFold (76) and 600 million in the ESM Metagenomic Atlas (77), it is now more important than ever to have orthogonal data to corroborate these models.”
BackgroundMolecular storytelling: a conceptual framework for teaching and learning with molecular case studies
Trujillo, C., & Dutta, S. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1379515 · Cited by 3
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Flöther, F. F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-98808-7.00001-1 · Cited by 7
OpenProteinSet: Training data for structural biology at scale.Preprint
Ahdritz, G., Bouatta, N., Kadyan, S., Jarosch, L., Berenberg, D., Fisk, I., Watkins, A. M., Ra, S., Bonneau, R., & AlQuraishi, M. (2023) · PubMed · Cited by 3
Predicted peptide scaffolds for drug screening in endometrial cancer organoids
Zhang, M., Wan, Y., & Li, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-21282-1 · Cited by 2
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Parkinson, G., Patel, R. D., Bergstrom, C. P., Vadasz, B., Amgad, M., Cooper, L. A. D., Sparano, J. A., & Lu, J. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-520716 · Cited by 4
The Data Mechanisms of Diagnosis and Intelligence
Fu, J., & Hsiao, C.-A. (2023) · Symmetry · doi:10.3390/sym15020278 · Cited by 2
Artificial intelligence in early stages of structure-based drug discovery
Ravichandran, R., Park, S., Skorupan, S., Bessette, K., & Gentile, F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-23932-8.00001-7 · Cited by 2
What is hidden in the darkness? Characterization of AlphaFold structural spacePreprint
Durairaj, J., Pereira, J., Akdel, M., & Schwede, T. F. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.11.511548 · Cited by 2
“, 2000), 214 million protein structural models are publicly available since July 28 2022 through the AlphaFold database version three (AFDB v3) (Varadi et al., 2022; Callaway, 2022).”
BackgroundMethodYang, Y., Xie, Y., Zhao, L., Diala, C. S., Ali, M. A., Li, R., Xu, Y., Wu, A., Kim, P., Hosseini, S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.30.578053 · Cited by 2
Li, W. (2024) · Preprints.org · doi:10.20944/preprints202406.1032.v1 · Cited by 2
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Stovpets, O. V. (2024) · Мiждисциплiнарнi дослiдження складних систем · doi:10.31392/iscs.2024.24.057 · Cited by 1
Box of Lessons: An Open Educational Resource for Exploring Biomolecular Structure and Function
Pettit, A. S., Johnson, K. A., Gadd, B., et al. (2025) · The journal of college science teaching · doi:10.1080/0047231x.2025.2472144 · Cited by 1
“This includes experimentally determined structures (230 thousand) and computed structure models (a million from RCSB.org and another ∼ 200 million from AlphaFold (Burley et al., 2023; Callaway, 2022)).”
Tian, W., Hu, Y., Gao, X., Yang, J.-H., & Jiang, W. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262110744 · Cited by 2
“These include incomplete structural coverage, heterogeneous validation, limited availability of high-quality structural data for membrane proteins and multi-subunit complexes, imperfect toxicity and multi-target modeling, and dataset biases that constrain generalizability [25–27].”
Zielińska, A., Fornalik, M., Szczepaniak, M., Gimła, M., Lemanska, A., Cielecka‐Piontek, J., & Souto, E. B. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag203 · Cited by 3
Sicilia, C., Corral‐Lugo, A., Smialowski, P., McConnell, M. J., & Martín-Galiano, A. J. (2022) · ACS Omega · doi:10.1021/acsomega.2c04076 · Cited by 2
Research training in an AI world
Chen, A. (2023) · Journal of Clinical Investigation · doi:10.1172/jci173283 · Cited by 1
Wu, D., Wang, L., Li, W., et al. (2023) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.3c02594 · Cited by 1
Ma, L., Zhang, S., Liang, Q., Huang, W., Wang, H., Pan, E., Xu, P., Zhang, S., Tao, F., Tang, J., et al. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05477-9 · Cited by 1
“The program and its predicted structures for nearly all catalogued proteins with sequence information known to science are publicly available [33, 34].”
BackgroundBiology’s Dark Matter: From Galaxies to Microbes
Vanderstraeten, S., & Searle, A. (2025) · Theory Culture & Society · doi:10.1177/02632764241304719 · Cited by 1
Assessing Structural Classification Using AlphaFold2 Models Through ECOD ‐Based Comparative Analysis
Kawabata, T., & Kinoshita, K. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26828
“The database has recently expanded to additional models, including over 200 million protein structure predictions [29].”
Similarity analysis and phylogenetic trees from cellular automata imaging of lysozyme proteinsPreprint
de Souza, L. F., Pereira, H. B. D. B., Filho, T. M. R., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2313549/v1 · Cited by 1
Specialty grand challenge: Structure, spectroscopy, and imaging
Ferreira, A. M. D. C. (2023) · Frontiers in Chemical Biology · doi:10.3389/fchbi.2023.1146814 · Cited by 1
“It made it possible to understand the relationships between protein functions and its 3D structures, and the results are now available to scientists in a database (Callaway, 2022).”
BackgroundRegulação da inteligência artificial na saúde
Dourado, D. D. A. (2024) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.5.2024.tde-23042024-111255 · Cited by 2
A one-dimensional semaglutide-GLP-1R-based mini static GIBACPreprint
Li, W. (2024) · Preprints.org · doi:10.20944/preprints202406.0953.v1 · Cited by 1
Reflections on communication and leadership
Knappe, G. A. (2022) · MIT Science Policy Review · doi:10.38105/spr.dys98zdagv
NMR structure verifies the eponymous degenerate zinc finger domain of transcription factor ZNF750Preprint
Rua, A. J., Whitehead, R. D., & Alexandrescu, A. T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.10.544471
“If true, AlphaFold would not only predict protein structure but flexibility.”
BackgroundMethodResultInfluentialThe Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15Preprint
Karaca, E., Ozden, B., & Kryshtafovych, A. (2023) · doi:10.22541/au.168897262.26176460/v1
WITHDRAWN: NMR structure verifies the eponymous zinc finger domain of transcription factor ZNF750
Rua, A. J., Whitehead, R. D., & Alexandrescu, A. T. (2023) · Journal of Structural Biology · doi:10.1016/j.jsb.2023.108003
RetractedQuels enseignements de mathématiques pour la biologie ? / Which mathematics for teaching biology ?
Haiech, J. (2023) · Notes Académiques de l Académie d agriculture de France / Academic Notes of the French Academy of Agriculture · doi:10.58630/pubac.not.a244295
Towler, M. C. (2023) · Biomedical visualization · doi:10.1007/978-3-031-41652-1_5
Zhou, M., Wang, T., Xu, K., Wang, H., Li, Z., Zhang, W., & Wang, Y. (2024) · Frontiers of Environmental Science & Engineering · doi:10.1007/s11783-024-1851-0 · Cited by 1
Metadata
- Authors
- Callaway, E.
- Corresponding author
- Callaway, Ewen
- Venue
- Nature
- Published
- 2022-07-28
- Volume
- 608
- Issue
- 7921
- Pages
- 15-16
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4288428465
- Cited by
- 113
- References
- 0
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 26 · 2023: 44 · 2022: 18
- Type
- other
- Language
- en
- Open access
- closed
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- AlphaFold, protein structure prediction, protein structure database, DeepMind
- MeSH terms
- Artificial Intelligence, Protein Conformation, Proteins, Computational Biology
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