Present Impact of AlphaFold2 Revolution on Structural Biology, and an Illustration With the Structure Prediction of the Bacteriophage J-1 Host Adhesion Device

Goulet, Adeline; Cambillau, Christian

doi:10.3389/fmolb.2022.907452

Cited by 23 publications

(17 citation statements)

References 30 publications

(48 reference statements)

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“…The Dit proteins analyzed in this study all form a core assembling the belt and galectin domains, similar to that observed for other phage Dits (Sciara et al, 2010;Veesler et al, 2010;Veesler et al, 2012) [or an OB-fold in phage T5 (Flayhan et al, 2014)]. Moreover, the Dits' CBMs exhibit similar folds for the five phages, whose the closest structure in the PDB is that of one of the two CBMs belonging to the Dit of L. casei phage J-1 (Dieterle et al, 2017;Goulet, 2022).…”

Section: Discussionsupporting

confidence: 82%

A structural discovery journey of streptococcal phages adhesion devices by AlphaFold2

Goulet

Joos

Lavelle

et al. 2022

Front. Mol. Biosci.

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Successful bacteriophage infection starts with specific recognition and adhesion to the host cell surface. Adhesion devices of siphophages infecting Gram-positive bacteria are very diverse and remain, for the majority, poorly understood. These assemblies often comprise long, flexible, and multi-domain proteins, which limits their structural analyses by experimental approaches such as X-ray crystallography and electron microscopy. However, the protein structure prediction program AlphaFold2 is exquisitely adapted to unveil structural and functional details of such molecular machineries. Here, we present structure predictions of whole adhesion devices of five representative siphophages infecting Streptococcus thermophilus, one of the main lactic acid bacteria used in dairy fermentations. The predictions highlight the mosaic nature of these devices that share functional domains for which active sites and residues could be unambiguously identified. Such AlphaFold2 analyses of phage-encoded host adhesion devices should become a standard method to characterize phage-host interaction machineries and to reliably annotate phage genomes.

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Section: Discussionsupporting

confidence: 82%

A structural discovery journey of streptococcal phages adhesion devices by AlphaFold2

Goulet

Joos

Lavelle

et al. 2022

Front. Mol. Biosci.

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“…AF2 structural predictions are a potent and fast tool to determine the structure–function relationships of viral replication/translation machineries. Precisely knowing domain boundaries is highly valuable to recombinantly produce stable and active proteins and perform structural and functional analyses [ 32 ]. In particular, predicted structures can be used as templates to design mutants and test their activity, or to design inhibitors of the replication machineries, which is a target of choice for efficient antiviral treatments as illustrated by the NS5B-RdRp inhibitor used to treat HCV-infected patients [ 53 ].…”

Section: Discussionmentioning

confidence: 99%

“…In particular, it is well adapted to predict structures of long, flexible and multi-domain proteins that cannot be analyzed as a whole using experimental approaches such as X-ray crystallography and electron microscopy. Recently, we applied this method to bacterial viruses (bacteriophages) to determine the structures of their whole host-binding machineries with several multi-domain proteins [ 31 , 32 ]. Moreover, as compared with homology modeling, AF2 produces structures that do not suffer from sequence bias, with an estimate of the prediction reliability for each protein residue given by a confidence score, the predicted local distance difference test (pLDDT, from 0 to 100).…”

Section: Introductionmentioning

confidence: 99%

Structure Prediction and Analysis of Hepatitis E Virus Non-Structural Proteins from the Replication and Transcription Machinery by AlphaFold2

Goulet

Cambillau

Roussel

et al. 2022

Viruses

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Hepatitis E virus (HEV) is a major cause of acute viral hepatitis in humans globally. Considered for a long while a public health issue only in developing countries, the HEV infection is now a global public health concern. Most human infections are caused by the HEV genotypes 1, 2, 3 and 4 (HEV-1 to HEV-4). Although HEV-3 and HEV-4 can evolve to chronicity in immunocompromised patients, HEV-1 and HEV-2 lead to self-limited infections. HEV has a positive-sense single-stranded RNA genome of ~7.2 kb that is translated into a large pORF1 replicative polyprotein, essential for the viral RNA genome replication and transcription. Unfortunately, the composition and structure of these replicases are still unknown. The recent release of the powerful machine-learning protein structure prediction software AlphaFold2 (AF2) allows us to accurately predict the structure of proteins and their complexes. Here, we used AF2 with the replicase encoded by the polyprotein pORF1 of the human-infecting HEV-3. The boundaries and structures reveal five domains or nonstructural proteins (nsPs): the methyltransferase, Zn-binding domain, macro, helicase, and RNA-dependent RNA polymerase, reliably predicted. Their substrate-binding sites are similar to those observed experimentally for other related viral proteins. Precisely knowing enzyme boundaries and structures is highly valuable to recombinantly produce stable and active proteins and perform structural, functional and inhibition studies.

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“…30,31 AF2 models therefore have a considerable impact on structure determination by X-ray crystallography and NMR. 23,29,32,33 Furthermore, the observation that AF2/RF models can be used for molecular replacement while other models cannot, suggests that we might question our definition of what a good model is. Indeed, some models that could have been defined as good by many metrics were not accurate enough for molecular replacement.…”

Section: Pipelinesmentioning

confidence: 99%

A Perspective on the Prospective Use of AI in Protein Structure Prediction

Versini,

Sritharan,

Aykac Fas

et al. 2023

J. Chem. Inf. Model.

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AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

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Present Impact of AlphaFold2 Revolution on Structural Biology, and an Illustration With the Structure Prediction of the Bacteriophage J-1 Host Adhesion Device

Cited by 23 publications

References 30 publications

A structural discovery journey of streptococcal phages adhesion devices by AlphaFold2

A structural discovery journey of streptococcal phages adhesion devices by AlphaFold2

Structure Prediction and Analysis of Hepatitis E Virus Non-Structural Proteins from the Replication and Transcription Machinery by AlphaFold2

A Perspective on the Prospective Use of AI in Protein Structure Prediction

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