Next Generation Protein Structure Predictions and Genetic Variant Interpretation

Diwan, Gaurav D.; González-Sánchez, Juan Carlos; Apic, Gordana; Russell, Robert B.

doi:10.1016/j.jmb.2021.167180

Cited by 25 publications

(17 citation statements)

References 68 publications

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“…Another interesting aspect to be considered for the future is to predict the effect of posttranslational modifications on the protein structure as, for instance, phosphorylation can induce very important conformational changes in the global protein structure and not only in loops (Graille et al, 2005;Bah et al, 2015). In the future, it will be important to be able to accurately model variants or post-translationally modified forms of any protein of interest (Diwan et al, 2021).…”

Section: Discussionmentioning

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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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%

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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“…In general, these papers assess prediction accuracy for particular types of protein domains (eg transmembrane regions) or specific families of proteins; some consider specifically the regions which are not predicted with any accuracy -attempting to use this as a marker of potential disorder 20 . Attempts to use AlphaFold2 to predict the impact of variants on function have not produced convincing results [21][22] , and probably more work will need to be done for it to have a bearing on this important area. One other interesting application of AlphaFold2 has been inverse protein folding, or protein design [23][24] .…”

Section: New Work Inspired By Alphafoldmentioning

confidence: 99%

The impact of AlphaFold2 one year on

2022

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The greatly improved prediction of protein 3-D structure from sequence, achieved by the second version of AlphaFold in 2020, has already started to have a huge impact on biological research; however, in our view a number of challenges remain, and the protein folding problem cannot be considered solved yet. We expect fierce competition to improve the method even further and new applications of machine learning to help illuminate proteomes and their many interactions.

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“…It also points to research projects that should be reconsidered. The richness of high quality data that are being compiled in databases (e.g., refs and − ) is already strengthening studies that require protein structures, such as mapping binding sites and interactions in signaling pathways, and identification of hot spots, including latent and rare cancer driver mutations. − The most profound impact will likely be in accelerating and improving production of new medications (e.g., ref ), and in generating data that can be used toward this vital aim (e.g., refs , , , and − ). AI developments and applications may further help foretell whether the signal propagating downstream will be strong enough to reach its genomic target to activate (suppress) gene expression, and predict pathways. − Altogether, these powerful approaches and the databases that they create revamp and transform traditional and ongoing research involving the use of structures.…”

Section: Introductionmentioning

confidence: 99%

AlphaFold, Artificial Intelligence (AI), and Allostery

et al. 2022

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AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota–human protein–protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

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Next Generation Protein Structure Predictions and Genetic Variant Interpretation

Cited by 25 publications

References 68 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

The impact of AlphaFold2 one year on

AlphaFold, Artificial Intelligence (AI), and Allostery

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