AlphaFold heralds a data-driven revolution in biology and medicine

Thornton, Janet M.; Laskowski, Roman A.; Borkakoti, Neera

doi:10.1038/s41591-021-01533-0

Cited by 143 publications

(107 citation statements)

References 15 publications

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“…Like many other structural biologists (Cramer, 2021;Perrakis & Sixma, 2021;Thornton et al, 2021;McCoy et al, 2022;Subramaniam & Kleywegt, 2022), we are convinced that this achievement in protein structure-prediction accuracy will revolutionize structural biology. In the future, methods such as MIR/SIR or SAD/MAD will become increasingly marginal or applied to specific cases such as low-resolution data, for instance.…”

Section: Discussionmentioning

confidence: 87%

The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models: a case-study report

Barbarin-Bocahu¹,

Graille²

2022

Acta Crystallogr D Struct Biol

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The breakthrough recently made in protein structure prediction by deep-learning programs such as AlphaFold and RoseTTAFold will certainly revolutionize biology over the coming decades. The scientific community is only starting to appreciate the various applications, benefits and limitations of these protein models. Yet, after the first thrills due to this revolution, it is important to evaluate the impact of the proposed models and their overall quality to avoid the misinterpretation or overinterpretation of these models by biologists. One of the first applications of these models is in solving the `phase problem' encountered in X-ray crystallography in calculating electron-density maps from diffraction data. Indeed, the most frequently used technique to derive electron-density maps is molecular replacement. As this technique relies on knowledge of the structure of a protein that shares strong structural similarity with the studied protein, the availability of high-accuracy models is then definitely critical for successful structure solution. After the collection of a 2.45 Å resolution data set, we struggled for two years in trying to solve the crystal structure of a protein involved in the nonsense-mediated mRNA decay pathway, an mRNA quality-control pathway dedicated to the elimination of eukaryotic mRNAs harboring premature stop codons. We used different methods (isomorphous replacement, anomalous diffraction and molecular replacement) to determine this structure, but all failed until we straightforwardly succeeded thanks to both AlphaFold and RoseTTAFold models. Here, we describe how these new models helped us to solve this structure and conclude that in our case the AlphaFold model largely outcompetes the other models. We also discuss the importance of search-model generation for successful molecular replacement.

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Section: Discussionmentioning

confidence: 87%

The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models: a case-study report

Barbarin-Bocahu¹,

Graille²

2022

Acta Crystallogr D Struct Biol

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“… 13 High‐confidence predictions (pLDDT > 70, threshold resulting from benchmark on a test set 13 ) cover roughly 62% of the human proteome and 92% of sequences in E. coli . 33 Despite progress, there are many proteins and portions of proteins whose AF2 structural models may not be accurate. Models of single‐pass transmembrane proteins are especially problematic, as they include multiple extracellular (EC) and intracellular (IC) domains that can change their relative positions due to the flexibility of connecting loops.…”

Section: New Content Of Membranome 30mentioning

confidence: 99%

“…A quality assessment score, the predicted local distance difference test (pLDDT) is natively produced by the AF2 system 13 . High‐confidence predictions (pLDDT > 70, threshold resulting from benchmark on a test set 13 ) cover roughly 62% of the human proteome and 92% of sequences in E. coli 33 . Despite progress, there are many proteins and portions of proteins whose AF2 structural models may not be accurate.…”

Section: New Content Of Membranome 30mentioning

confidence: 99%

Membranome 3.0: Database of single‐pass membrane proteins with AlphaFold models

et al. 2022

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The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org/.

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“…In conclusion, the AlphaFold algorithm has rightly been called a “game changer” in the field of structural biology and has demonstrated one of the many applications of deep learning algorithms in biomedicine. 22 , 23 However, AlphaFold has not completely solved the “protein folding problem” and many challenges remain, such as predicting the relative position of domains within a chain, how domains shift their relative conformation in response to stimuli, and how domains transition from disorder to order.…”

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confidence: 99%