Cited by 505Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

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‘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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“It was used by Google’s “AlphaFold”, which can accurately predict protein structure based on protein sequence within a few days (78, 79).”
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“DeepMind uses deep and reinforcement learning to predict protein structure (Callaway 2020).”
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“As an example, DeepMind’s artificial intelligence system called AlphaFold outperformed its competitors in predicting protein structures and achieved results that are very close to the experimentally determined structures in the Critical Assessment of Structure Prediction in 2020 [66].”
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“X ‐ ray crystallography, cryogenic electron microscopy, and nuclear magnetic resonance spectroscopy have all been suggested as traditional experimental methods to determine protein structures, although they are still time ‐ consuming and expensive (Callaway, 2020).”
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“…as Phyre2 (Kelley et al., 2015) and more Frontiers in Cell and Developmental Biology | www.frontiersin.org November 2021 | Volume 9 | Article 7843678 recently AlphaFold (Callaway, 2020; Jumper et al., 2021) (https:// alphafold.ebi.ac.uk/entry/P53083) validate the presence of the SMP fold in Mdm34.”
Background
Quorum quenching by a type IVA secretion system effector
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“Callaway indicated in his Nature synopsis of CASP14 (77) that purely computational structure determination is unlikely, but rather that sparse experimental data will soon be sufficient for unambiguous structure elucidation in combination with the new wave of artificial intelligence technologies.”
Background
Deep learning methods for protein structure prediction
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“AlphaFold2 is artificial intelligence for protein structure prediction, which is a new neural network-based model that can predict protein structures with atomic-level accuracy [29, 30].”
Background
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“The model developed by DeepMind has created high 38 expectations in the community [13] because of its outstanding results on the 13th edition 39 of Critical Assessment of Structure Prediction (CASP) [14] using the first version of the 40 model [15], and more recently in CASP 14 [16] with the new AlphaFold 2 [12].”
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“The core of this discipline lies in demystifying the intricate relationships between amino acid sequences and the resulting 3D structures of proteins [96].”
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“…have made great strides in the last decade (Kuhlman and Bradley 2019) —best exemplified by the transformative results achieved by the AlphaFold2 team in the CASP14 competition (Callaway 2020)—and predicted structures are now often good enough to act as substitutes for structural homology purposes.”
Background
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“Recently, deep learning algorithms have been developed that have achieved signi ﬁ cant superiority over traditional learning algorithms, especially in many dif ﬁ cult cases such as image classi ﬁ cation (Krizhevsky et al., 2012; He et al., 2016) and protein structure prediction (Callaway, 2020).”
Method
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“The model was highly rated in CASP14 in 2020 [30 31 32].”
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“In 2018, the first version of AlphaFold, a protein structure prediction algorithm based on deep convolutional residual neural networks, was developed by Google AI’s DeepMind [18,19] and outperformed all other programs for the thirteenth edition of the critical assessment of protein structure prediction (CASP).”
Method
Membrane fluidity, composition, and charge affect the activity and selectivity of the AMP ascaphin-8
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Background
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“28 In heterogeneous catalysis, like in many other communities, there is a need for AI algorithms that ensure their interpretability.”
Background
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Background
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Hönig, S. M. N., Lemmen, C., & Rarey, M. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1640 · Cited by 19
The SWISS‐MODEL Repository of 3D Protein Structures and Models
Robin, X., Waterhouse, A. M., Bienert, S., Studer, G., Alexander, L. T., Tauriello, G., Schwede, T. F., & Pereira, J. (2023) · Methods and principles in medicinal chemistry · doi:10.1002/9783527830497.ch6 · Cited by 20
A review and outlook on expression of animal proteins in plants
Tusé, D., McNulty, M. J., McDonald, K. A., & Buchman, L. W. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1426239 · Cited by 15
“Nevertheless, more than 20 computational tools have been used to identify T-and B-cell allergenic epitopes on proteins, with associated tools to predict the impact on the protein ’ s structure and functionality (reviewed in Hayes et al., 2015; Callaway, 2020; Zinsli et al., 2021).”
Method
Modular optimization in metabolic engineering
Wong, M. S., Badri, A., Gasparis, C., et al. (2021) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.1080/10409238.2021.1937928 · Cited by 15
“On top of that, strains that are useful for lab scale analysis can prove to be insufficient for industrial production and vice versa (Crater and Lievense 2018).”
Background
A Trust Paradox May Limit the Application of Al-Generated Knowledge
Nylund, P. A., Ferràs, X., & Brem, A. (2023) · Research-Technology Management · doi:10.1080/08956308.2023.2236475 · Cited by 22
“Scientists consider this achievement a game-changer in biotechnology (Callaway 2020).”
Background
Equivariant Line Graph Neural Network for Protein-Ligand Binding Affinity Prediction
Yi, Y., Wan, X., Zhao, K., Ou-Yang, L., & Zhao, P. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3383245 · Cited by 18
“The advances in protein structure prediction technologies promote the accumulation of three-dimensional (3D) structure data of proteins [4].”
Background
Evolution-aided engineering of plant specialized metabolism
Irfan, M., Chavez, B. G., Rizzo, P., D’Auria, J. C., & Moghe, G. D. (2021) · aBIOTECH · doi:10.1007/s42994-021-00052-3 · Cited by 15
“For example, ML is now increasingly likely to accurately predict protein structure from a sequence (Callaway 2020), and ML is already finding applications in the directed evolution of proteins, to build sequence-function models and select variant libraries for screening (Yang et al. 2019; Shamsi et…”
Background
Virtual screening of potential anticancer drugs based on microbial products
Pinto, G., Hendrikse, N. M., Štourač, J., Damborský, J., & Bednář, D. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2021.07.012 · Cited by 16
MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction
Zheng, P., Zhang, G., Liu, Y., & Huang, G. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05135-0 · Cited by 16
“Hundreds of computational tools have been developed over the past thirty years for predicting post-translational modification [17, 21–23], RNA modification [24–26], single-cell analysis [27], protein functions [28, 29], as well as protein structure [30], gene selection [31], cancer diagnosis [32], and even food recomm…”
Computational protein modeling and the next viral pandemic
Narykov, O., Srinivasan, S., & Korkin, D. (2021) · Nature Methods · doi:10.1038/s41592-021-01144-0 · Cited by 14
Viral informatics: bioinformatics-based solution for managing viral infections
Kumar, S., Kumar, G. S., Maitra, S. S., Malý, P., Bharadwaj, S., Sharma, P., & Dwivedi, V. D. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac326 · Cited by 15
A Comprehensive Analysis of the Structural Recognition between KCTD Proteins and Cullin 3
Balasco, N., Esposito, L., Smaldone, G., Salvatore, M., & Vitagliano, L. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25031881 · Cited by 13
Foamy Viruses, Bet, and APOBEC3 Restriction
Vasudevan, A. A. J., Becker, D., Luedde, T., Gohlke, H., & Münk, C. (2021) · Viruses · doi:10.3390/v13030504 · Cited by 14
“Despite recent advances in ab initio protein structure prediction [205], template-based structure modeling is still considered to yield high-quality structural models if appropriate template structures can be identified [206].”
Background
RBCeq: A robust and scalable algorithm for accurate genetic blood typing
Jadhao, S., Davison, C. L., Roulis, E. V., Schoeman, E. M., Divate, M. D., Haring, M., Williams, C., Shankar, A. J., Lee, S., Pecheniuk, N. M., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2021.103759 · Cited by 15
The Builders of the Junction: Roles of Junctophilin1 and Junctophilin2 in the Assembly of the Sarcoplasmic Reticulum–Plasma Membrane Junctions in Striated Muscle
Perni, S. (2022) · Biomolecules · doi:10.3390/biom12010109 · Cited by 14
“Alphafold2 is a giant leap forward in the reliability of protein folding prediction compared to similar existing software [74], and it has already been used to predict the structure of nearly the entire human proteome.”
Method
Computing the Volume, Surface Area, Mean, and Gaussian Curvatures of Molecules and Their Derivatives
Koehl, P., Akopyan, A. V., & Edelsbrunner, H. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01346 · Cited by 14
Quantitative Systems Pharmacology and Machine Learning: A Match Made in Heaven or Hell?
Tindall, M. J., Cucurull-Sanchez, L., Mistry, H. B., & Yates, J. W. T. (2023) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.122.001551 · Cited by 14
Beyond history and “on a roll”: The list of the most well‐studied human protein structures and overall trends in the protein data bank
Li, Z., & Buck, M. (2021) · Protein Science · doi:10.1002/pro.4038 · Cited by 16
Biological evolution requires an emergent, self-organizing principle
Brown, O. R., & Hullender, D. A. (2023) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2023.06.001 · Cited by 13

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

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