Crystallographic molecular replacement using an in silico‐generated search model of SARS‐CoV‐2 ORF8

Flower, Thomas G; Hurley, James H.

doi:10.1002/pro.4050

Cited by 49 publications

(35 citation statements)

References 35 publications

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“…We managed to solve the structure of a small protein and to obtain 2F o À F c electron-density maps of excellent quality in a straightforward way, whereas we had previously struggled with solving the structure for almost two years following the acquisition of good-quality diffraction data. Several other structural biologists have also succeeded in solving reluctant crystal structures thanks to AlphaFold models (Flower & Hurley, 2021;Kryshtafovych, Moult et al, 2021;Milla ´n et al, 2021;Moi et al, 2021). Many other examples will undoubtedly be described in the future.…”

Section: Discussionmentioning

confidence: 99%

“…However, it is now obvious that beyond the sequence identity, the structural similarity between the search model and the crystal structure is most important (McCoy et al, 2022). Thanks to their high accuracy and also to the significantly improved estimation of the error in the coordinates, both AlphaFold and RoseTTAFold 3D structure models have already influenced the process of 3D protein structure determination by the molecular-replacement technique (Flower & Hurley, 2021;Kryshtafovych, Schwede et al, 2021;Milla ´n et al, 2021;Moi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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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: 99%

Section: Introductionmentioning

confidence: 99%

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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“…For T1100, several of the models submitted to CASP, including the AlphaFold2 models, gave a molecular-replacement solution where the NMR structures of individual domains failed. The AlphaFold2 model for T1064 has also been used to solve the SARS-CoV-2 ORF8 structure retrospectively by molecular replacement (Flower & Hurley, 2021).…”

Section: Casp14mentioning

confidence: 99%

Implications of AlphaFold2 for crystallographic phasing by molecular replacement

McCoy

Sammito

Read

2022

Acta Crystallogr D Struct Biol

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The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high-coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. Here, the data from CASP14 are used to explore the prospects for changes in phasing methods, and in particular to explore the prospects for molecular-replacement phasing using in silico models.

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“…These problems include the prediction of various protein interactions, such as protein-protein, proteinligand and protein-DNA/RNA, and the prediction of the impact of mutations on protein stability. AlphaFold proved to be useful for experimental determination of protein structures with molecular replacement phasing [4,5] and already facilitated elucidation of SARS-Cov2 protein structures [6,7]. Furthermore, AlphaFold in collaboration with EMBL-EBI launched a global initiative on constructing the structure models for the whole protein sequence space [8].…”

Section: Introductionmentioning

confidence: 99%

Using AlphaFold to predict the impact of single mutations on protein stability and function

Pak

Markhieva

Novikova³

et al. 2021

Preprint

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AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the AlphaFold predictions on the impact of a single mutation on structure with a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold cannot be immediately applied to other problems or applications in protein folding.

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Crystallographic molecular replacement using an in silico‐generated search model of SARS‐CoV‐2 ORF8

Cited by 49 publications

References 35 publications

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

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

Implications of AlphaFold2 for crystallographic phasing by molecular replacement

Using AlphaFold to predict the impact of single mutations on protein stability and function

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