‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures

Callaway, Ewen

doi:10.1038/d41586-020-03348-4

Cited by 506Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures

Callaway, Ewen·2020·Nature

DOI 10.1038/d41586-020-03348-4

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AlphaFold combines deep learning innovations to predict three-dimensional protein structures at or near experimental scale resolution.
“AlphaFold has combined numerous deep learning innovations to predict the three-dimensional (3D) structures of proteins at or near experimental scale resolution”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [3]

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“The work of AlphaFold2 is regarded as one of the milestones in protein structure prediction and is anticipated to ‘ change everything ’ 10 .”
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Background
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Jain, S., Bakolitsa, C., Brenner, S. E., Predrag Radivojac, A., Moult, J., Repo, S., Hoskins, R. A., Andreoletti, G., Barsky, D., Chellapan, A., et al. (2024) · Genome biology · doi:10.1186/s13059-023-03113-6 · Cited by 68 · see citing works
“This latter perspective is particularly important because of the recent successes of artificial intelligence approaches in related fields [30, 31].”
Background
Machine learning differentiates enzymatic and non-enzymatic metals in proteins
Feehan, R., Franklin, M. W., & Slusky, J. S. G. (2021) · Nature Communications · doi:10.1038/s41467-021-24070-3 · Cited by 57 · see citing works
“Machine learning-based methods have won the last two critical assessments of methods of protein structure predictions 11 – 13 , a competition for de novo structure prediction, demonstrating machine learning effectiveness of predicting protein characteristics on previously unstudied proteins.”
Method
Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging
Shad, R., Cunningham, J. P., Ashley, E. A., Langlotz, C. P., & Hiesinger, W. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00399-8 · Cited by 58 · see citing works
Targeted in situ cross-linking mass spectrometry and integrative modeling reveal the architectures of three proteins from SARS-CoV-2
Slavin, M., Zamel, J., Zohar, K., Eliyahu, T., Braitbard, M., Brielle, E. S., Baraz, L., Stolovich-Rain, M., Friedman, A., Wolf, D. G., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2103554118 · Cited by 56 · see citing works
“We have therefore referred to the Nsp2 model generated by AlphaFold2 from DeepMind (29).”
Crystallographic molecular replacement using an in silico‐generated search model of SARS‐CoV‐2 ORF8
Flower, T. G., & Hurley, J. H. (2021) · Protein Science · doi:10.1002/pro.4050 · Cited by 49 · see citing works
“4 out of 100.(21,22) Our group recently determined the crystal structure of ORF8 at 2.”
Background
Classification Model for the Second Extracellular Loop of Class A GPCRs
Nicoli, A., Dunkel, A., Toni Giorgino, A., de Graaf, C., & Pizio, A. D. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01056 · Cited by 57 · see citing works
Efficient and easible biocatalysts: Strategies for enzyme improvement. A review
Albayati, S. H., Nezhad, N. G., Taki, A. G., & Rahman, R. N. Z. R. A. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.133978 · Cited by 57 · see citing works
Chemistry 2030: A Roadmap for a New Decade
García‐Martínez, J. (2021) · Angewandte Chemie International Edition · doi:10.1002/anie.202014779 · Cited by 56 · see citing works
Artificial intelligence guided conformational mining of intrinsically disordered proteins
Gupta, A., Dey, S., Hicks, A., & Zhou, H.-X. (2022) · Communications Biology · doi:10.1038/s42003-022-03562-y · Cited by 56 · see citing works
Human-AI collaboration by design
Song, B., Zhu, Q., & Luo, J. (2024) · Proceedings of the Design Society · doi:10.1017/pds.2024.227 · Cited by 78 · 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
Protein–Protein Docking: Past, Present, and Future
Sunny, S., & Jayaraj, P. B. (2021) · The Protein Journal · doi:10.1007/s10930-021-10031-8 · Cited by 50 · see citing works
“The groundbreaking solution to this hard problem was proposed by DeepMind [114].”
Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations
Souza, P. C. T., Limongelli, V., Wu, S., Marrink, S., & Monticelli, L. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.657222 · Cited by 37 · see citing works
“…applications have similar pitfalls, with the current approaches reasonably successful in predicting protein structures (Hutson, 2019; Callaway, 2020) and rigid-body protein-protein interactions (Siebenmorgen and Zacharias, 2020) but with limitations in the design of conformational…”
Method
AlphaFold predicts the most complex protein knot and composite protein knots
Brems, M. A., Runkel, R., Yeates, T. O., & Virnau, P. (2022) · Protein Science · doi:10.1002/pro.4380 · Cited by 50 · see citing works
“Recently, the artificial intelligence (AI) system AlphaFold developed by Google’s DeepMind dominated the Critical Assessment of Techniques for Protein Structure Prediction (CASP) twice.(1) AlphaFold 2, the version under consideration here, is a deep learning system that incorporates training procedures based on the ev…”
Background
Students’ preferences with university teaching practices: analysis of testimonials with artificial intelligence
Álvarez, C. Á., & Falcón, S. (2023) · Educational Technology Research and Development · doi:10.1007/s11423-023-10239-8 · Cited by 57 · see citing works
“This is especially notable in biology, where the development of AlphaFold 2, an AI-based tool capable of predicting the three-dimensional structure of proteins, has been a milestone (Callaway, 2020; Jumper et al., 2021).”
Background
Can We AlphaFold Our Way Out of the Next Pandemic?
Higgins, M. K. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167093 · Cited by 46 · see citing works
“Nevertheless, the CASP competitions are stringent, rigorous and robust peer review processes in their own right(10) and AlphaFold 2 was by some distance the best prediction method yet to test its arm in the competition.(9) In particular, its performance was strongest in the challenging ‘free model’ category in CASP.”
Method
From Lock-In to Transformation: A Path-Centric Theory of Emerging Technology and Organizing
Pentland, B. T., Yoo, Y., Recker, J., & Kim, I. (2021) · Organization Science · doi:10.1287/orsc.2021.1543 · Cited by 47 · see citing works
“The new technology is not just radically shortening the vaccine development process; fundamentally new patterns of activity for drug discovery are emerging (Cui et al. 2020, Chaudhari et al. 2020, Lin, Li, and Lin 2020, Callaway 2020).”
Background
Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine
Ambreen, S., Umar, M., Noor, A., Jain, H., & Ali, R. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.117164 · Cited by 50 · see citing works
Could AlphaFold revolutionize chemical therapeutics?
Tong, A., Burch, J. D., McKay, D. B., Bustamante, C., Crackower, M. A., & Wu, H. (2021) · Nature Structural & Molecular Biology · doi:10.1038/s41594-021-00670-x · Cited by 44 · see citing works
Grand Challenges in Bioinformatics Data Visualization
O’Donoghue, S. I. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.669186 · Cited by 46 · see citing works
Revealing RNA virus diversity and evolution in unicellular algae transcriptomes
Charon, J., Murray, S. A., & Holmes, E. C. (2021) · Virus Evolution · doi:10.1093/ve/veab070 · Cited by 43 · see citing works
Overexpression and kinetic analysis of Ideonella sakaiensis PETase for polyethylene terephthalate (PET) degradation
Aer, L., Jiang, Q., Gul, I., Qi, Z., Feng, J., & Tang, L. (2022) · Environmental Research · doi:10.1016/j.envres.2022.113472 · Cited by 44 · see citing works
Recent advances and challenges in experiment-oriented polymer informatics
Hatakeyama‐Sato, K. (2022) · Polymer Journal · doi:10.1038/s41428-022-00734-9 · Cited by 44 · see citing works

Authors: Callaway, E.
Corresponding author: Callaway, Ewen
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2020-11-30
Volume: 588
Issue: 7837
Pages: 203-204
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/d41586-020-03348-4
OpenAlex: W3108943181
Cited by: 499
References: 0
Citations by year: 2026: 15 · 2025: 39 · 2024: 77 · 2023: 87 · 2022: 97 · 2021: 181 · 2020: 2 · 2012: 1
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
Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords: DeepMind, AlphaFold, protein structure prediction, protein 3D structure, deep learning, artificial intelligence in biology, protein folding
MeSH terms: Deep Learning, Models, Molecular, Protein Conformation, Proteins, Time Factors, Crystallography, X-Ray, Caspases

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