‘The entire protein universe’: AI predicts shape of nearly every known protein

Callaway, Ewen

doi:10.1038/d41586-022-02083-2

Cited by 114Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

‘The entire protein universe’: AI predicts shape of nearly every known protein

Callaway, Ewen·2022·Nature

DOI 10.1038/d41586-022-02083-2

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1038/d41586-022-02083-2

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Precalculated structures for 200 million protein sequences are already available online.
“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]

Sorted by citations

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 483 · see citing works
Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine
Harrer, S. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104512 · Cited by 480 · see citing works
“…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…”
Background
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
Ahdritz, 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 · see citing works
“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).”
Method
Using 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 387 · see citing works
“In this respect, it is noteworthy that AlphaFold structures are now available for most UniProt proteins (Callaway 2022).”
Background
AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination
Terwilliger, 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 · see citing works
“…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 381 · see citing works
The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool
Resnik, D. B., & Hosseini, M. (2025) · AI and Ethics · doi:10.1007/s43681-024-00493-8 · Cited by 228 · see citing works
“One of the most impressive feats accomplished by ML systems is their contribution to solving the protein folding problem [41].”
Influential
Synthetic extracellular matrices with function-encoding peptides
Ligorio, C., & Mata, Á. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00055-3 · Cited by 177 · see citing works
“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 · see citing works
“…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).”
Background
Art in an age of artificial intelligence
Chatterjee, A. K. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1024449 · Cited by 114 · see citing works
“For example, in biology, AI recently solved the complex problem of three-dimensional protein folding from a two-dimensional code (Callaway, 2022).”
Background
Evaluation 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 109 · see citing works
“Recently these predictions were expanded to cover virtually every known protein sequence (Callaway, 2022; Varadi et al., 2022).”
Background
Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures
(2023) · doi:10.1021/acs.jcim.2c01270 · Cited by 95 · see citing works
Jasmonate 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 85 · see citing works
Cross-linking mass spectrometry discovers, evaluates, and corroborates structures and protein–protein interactions in the human cell
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 81 · see citing works
“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).”
Influential
Have genetic targets for faecal pollution diagnostics and source tracking revolutionized water quality analysis yet?
Demeter, 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 73 · see citing works
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 · see citing works
“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.”
Result
Accelerating crystal structure determination with iterative <i>AlphaFold</i> 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 · see citing works
The impact of <scp>AI</scp> ‐based modeling on the accuracy of protein assembly prediction: Insights from <scp>CASP15</scp>
Ozden, B., Kryshtafovych, A., & Karaca, E. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26598 · Cited by 51 · see citing works
“27,28 Since the release of AF2, scientists have sought to incorporate this framework into their modeling pipelines.”
Background
AlphaFold predictions are valuable hypotheses, and accelerate but do not replace experimental structure determination
Terwilliger, 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 · see citing works
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 · see citing works
Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization
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 48 · see citing works
Effective Molecular Dynamics from Neural Network-Based Structure Prediction Models
Jussupow, A., & Kaila, V. R. I. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01027 · Cited by 42 · see citing works
Growth-coupled enzyme engineering through manipulation of redox cofactor regeneration
Nielsen, J. R., Weusthuis, R. A., & Huang, W. E. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108102 · Cited by 48 · see citing works
Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics
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 · see citing works
“…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).”
BackgroundMethod
Quantum 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 · see citing works
Trends and patterns in cancer nanotechnology research: A survey of NCI's caNanoLab and nanotechnology characterization laboratory
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 32 · see citing works
End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification
Hu, Y., & Buehler, M. J. (2022) · ACS Nano · doi:10.1021/acsnano.2c07681 · Cited by 28 · see citing works
Open data and algorithms for open science in AI-driven molecular informatics
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 · see citing works
Advances in biosynthesis of peptide drugs: Technology and industrialization
Wang, J., Chen, L., Qin, S., Xie, M., Luo, S., & Li, W. (2023) · Biotechnology Journal · doi:10.1002/biot.202300256 · Cited by 25 · see citing works
“[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.”
Background
Experimental 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 24 · see citing works
“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., et al. (2022) · Trends in Plant Science · doi:10.1016/j.tplants.2022.11.004 · Cited by 22 · see citing works
Determinants for an Efficient Enzymatic Catalysis in Poly(Ethylene Terephthalate) Degradation
Castro-Rodríguez, J. A., Rodríguez‐Sotres, R., & Farrés, A. (2023) · Catalysts · doi:10.3390/catal13030591 · Cited by 23 · see citing works
The druggable genome: Twenty years later
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 22 · see citing works
“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.”
Background
The Ethics of Using Artificial Intelligence in Scientific Research: New Guidance Needed for a New ToolPreprint
Resnik, D. B., & Hosseini, M. (2023) · doi:10.31235/osf.io/rbg9z · Cited by 20 · see citing works
Functionally annotating cysteine disulfides and metal binding sites in the plant kingdom using AlphaFold2 predicted structures
(2022) · doi:10.1016/j.freeradbiomed.2022.12.001 · Cited by 16 · see citing works
The epistemic revolution of AI: reconfiguring the foundations of scientific knowledge
Cruz‐Aguilar, M. A. (2025) · AI & Society · doi:10.1007/s00146-025-02658-3 · Cited by 23 · see citing works
A comprehensive analysis of nanomagnetism models for the evaluation of particle energy in magnetic hyperthermia
Μaniotis, Ν., Maragakis, M., & Vordos, N. (2025) · Nanoscale Advances · doi:10.1039/d5na00258c · Cited by 20 · see citing works
Machine learning for predicting ZT values of high-performance thermoelectric materials in mid-temperature range
Parse, N., & Pinitsoontorn, S. (2023) · APL Materials · doi:10.1063/5.0160055 · Cited by 18 · see citing works
Artificial Intelligence and Breast Cancer Management: From Data to the Clinic
Feng, K., Yi, Z., & Xu, B. (2025) · Cancer Innovation · doi:10.1002/cai2.159 · Cited by 18 · see citing works
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 17 · see citing works
Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike
Melo, M. C. R., & Bernardi, R. C. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2023.01.042 · Cited by 15 · see citing works
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 · see citing works
“The AlphaFold database [4] and recent predictions of more than 200 million protein structures provide reference structure information for nearly every known protein [5].”
Background
An unbroken network of interactions connecting flagellin domains is required for motility in viscous environments
Nedeljković, 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 · see citing works
“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, E. (2024) · Proteomes · doi:10.3390/proteomes12020013 · Cited by 9 · see citing works
“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].”
BackgroundMethod
Opportunities 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 · see citing works
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 · see citing works
“structures for plants, bacteria, animals, and other organisms (46).”
Background
Towards a General Intermolecular Binding Affinity CalculatorPreprint
Li, W. (2022) · Preprints.org · doi:10.20944/preprints202208.0213.v1 · Cited by 10 · see citing works
Rational design, production and in vitro analysis of photoxenoproteins
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 · see citing works
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 11 · see citing works
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 · see citing works
Just Keep Rolling?—An Encompassing Review towards Accelerated Vaccine Product Life Cycles
(2023) · doi:10.3390/vaccines11081287 · Cited by 11 · see citing works
“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 <scp>AlphaFold2</scp>
Topitsch, A., Schwede, T., & Pereira, J. (2024) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26552 · Cited by 9 · see citing works
“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.”
Method
Blanket antimicrobial resistance gene database with structural information, BOARDS, provides insights on historical landscape of resistance prevalence and effects of mutations in enzyme structure
Ko, 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 · see citing works
Predicting High‐Performance Thermoelectric Materials With StarryData2
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 · see citing works
Chloroflexus aurantiacus acetyl-CoA carboxylase evolves fused biotin carboxylase and biotin carboxyl carrier protein to complete carboxylation activity
Shen, J., Wu, W., Wang, K., et al. (2024) · mBio · doi:10.1128/mbio.03414-23 · Cited by 8 · see citing works
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 · see citing works
PySSA for Windows: End-User Protein Structure Prediction and Visual Analysis with ColabFold and PyMOL
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 · see citing works
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 · see citing works
“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 .”
Graphene-Assisted Electron-Based Imaging of Individual Organic and Biological Macromolecules: Structure and Transient Dynamics
Zhang, D., Xu, Z., Li, J., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c12083 · Cited by 7 · see citing works
Accelerating crystal structure determination with iterative AlphaFold prediction
(2022) · doi:10.1101/2022.11.18.517112 · Cited by 62 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
“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…”
Background
Artificial Intelligence Could Revolutionize Science—If We Can Trust It
Leslie, M. (2024) · Engineering · doi:10.1016/j.eng.2024.03.002 · Cited by 5 · see citing works
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 · see citing works
“…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 4 · see citing works
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 · see citing works
“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 .”
Retracted
Reservoir computing with memristors
(2022) · Nature Electronics · doi:10.1038/s41928-022-00867-y · Cited by 4 · see citing works
Cross-linking mass spectrometry discovers, evaluates, and validates the experimental and predicted structural proteomePreprint
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 · see citing works
“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.”
Background
Molecular 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 · see citing works
Early quantum computing applications on the path towards precision medicine
Flöther, F. F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-98808-7.00001-1 · Cited by 7 · see citing works
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 · see citing works
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 · see citing works
Artificial Intelligence in Oncology: Clinical Applications, Challenges, and Opportunities
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 2 · see citing works
The Data Mechanisms of Diagnosis and Intelligence
Fu, J., & Hsiao, C.-A. (2023) · Symmetry · doi:10.3390/sym15020278 · Cited by 2 · see citing works
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 · see citing works
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 · see citing works
“, 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).”
BackgroundMethod
Systematic characterization of protein structural features of alternative splicing isoforms using AlphaFold 2Preprint
Yang, 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 · see citing works
Optimizing GLP-1R Agonist: A Computational Semaglutide Analogue with 112-fold Enhanced Binding Affinity to GLP-1RPreprint
Li, W. (2024) · Preprints.org · doi:10.20944/preprints202406.1032.v1 · Cited by 2 · see citing works
PHILOSOPHIC-ANTHROPOLOGICAL ASPECTS OF THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE: A VIEW FROM 2024
Stovpets, O. V. (2024) · Мiждисциплiнарнi дослiдження складних систем · doi:10.31392/iscs.2024.24.057 · Cited by 1 · see citing works
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 · see citing works
“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)).”
Computer-Aided Drug Design Across Breast Cancer Subtypes: Methods, Applications and Translational Outlook
Tian, W. X., Hu, Y., Gao, X., Yang, J., & Jiang, W. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262110744 · Cited by 2 · see citing works
“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].”
Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing
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 · see citing works
Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative “Superbugs”: Six Protein Clusters Amenable for Distinct Scientific Applications
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 · see citing works
Research training in an AI world
Chen, A. (2023) · Journal of Clinical Investigation · doi:10.1172/jci173283 · Cited by 1 · see citing works
Identifying a New Target for BtOBP8: Discovery of a Small Amino Ketone Molecule Containing Benzothiazole Fragments
Wu, D., Wang, L., Li, W., et al. (2023) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.3c02594 · Cited by 1 · see citing works
CrMP-Sol database: classification, bioinformatic analyses and comparison of cancer-related membrane proteins and their water-soluble variant designs
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 · see citing works
“The program and its predicted structures for nearly all catalogued proteins with sequence information known to science are publicly available [33, 34].”
Background
Biology’s Dark Matter: From Galaxies to Microbes
Vanderstraeten, S., & Searle, A. (2025) · Theory Culture & Society · doi:10.1177/02632764241304719 · Cited by 1 · see citing works
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 · Cited by 1 · see citing works
“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 · see citing works
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 · see citing works
“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).”
Background
Regulação 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 · see citing works
A one-dimensional semaglutide-GLP-1R-based mini static GIBACPreprint
Li, W. (2024) · Preprints.org · doi:10.20944/preprints202406.0953.v1 · Cited by 1 · see citing works
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.”
BackgroundMethodResultInfluential
The 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
Retracted
Which mathematics should be learned for 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
Animation for Science Communication: A Description of the Production Process and Contexts for Using Science Animation
Towler, M. C. (2023) · Biomedical visualization · doi:10.1007/978-3-031-41652-1_5
Broadening environmental research in the era of accurate protein structure determination and predictions
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

Authors: Callaway, E.
Corresponding author: Callaway, Ewen
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2022-07-28
Volume: 608
Issue: 7921
Pages: 15-16
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/d41586-022-02083-2
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
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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
Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
Keywords: AlphaFold, protein structure prediction, protein structure database, DeepMind
MeSH terms: Artificial Intelligence, Protein Conformation, Proteins, Computational Biology

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

‘The entire protein universe’: AI predicts shape of nearly every known protein

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cited by

Related

Record

About these checks