Source 1primary paper2025MED
Toolkit/AlphaFold3
AlphaFold3
Also known as: AlphaFold 3, AlphaFold3
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
AlphaFold3 is a computational structure-prediction method used in the cited study to model the MagMboI–DNA complex. In that work, it was applied to infer interactions with the 5'-GATC-3' recognition sequence and to guide optimization of the photoactivatable endonuclease variant MagMboI-plus for top-down genome engineering.
Usefulness & Problems
Why this is useful
In the cited study, AlphaFold3 was useful for generating a structural model of a protein–DNA complex that informed interpretation of sequence recognition by MagMboI. This enabled structure-guided optimization of a photoactivatable nuclease in the absence of other evidence described here.
Problem solved
The specific problem addressed in the cited work was how to rationalize MagMboI recognition of the 5'-GATC-3' DNA sequence and use that information to improve a photoactivatable endonuclease variant. The available evidence does not provide broader benchmarking or general performance claims for AlphaFold3 beyond this application.
Published Workflows
Objective: Identify RNA conformational states consistent with experimental smFRET data from a heterogeneous and difficult-to-predict RNA system.
Why it works: The workflow first generates a collection of candidate RNA conformations, removes structurally implausible models, then predicts dye-pair FRET distributions for retained structures and compares them to experimental smFRET data to identify compatible conformational states.
matching predicted FRET distributions to experimental smFRET statesretaining models consistent with Watson-Crick base-pairing patternsmulti-tool RNA 3D structure predictionstructural validation and eRMSD filteringaccessible contact volume calculationcomparison and weighting against experimental smFRET data
Stages
- 1.Candidate RNA 3D structure generation(library_design)
This stage creates the initial collection of candidate RNA conformations needed for downstream structural validation and FRET-guided selection.
Selection: Generate a collection of candidate conformations using three RNA 3D modeling tools.
- 2.Structural validation and eRMSD filtering(in_silico_filter)
This stage removes models that fail structural validation before computational FRET prediction.
Selection: Watson-Crick base-pairing patterns and an eRMSD threshold
- 3.FRET distribution prediction for retained structures(secondary_characterization)
This stage converts retained structures into predicted observables that can be compared with experiment.
Selection: Compute accessible contact volume of the Cy3/Cy5 dye pair using FRETraj to predict FRET distributions.
- 4.Comparison and weighting against experimental smFRET data(confirmatory_validation)
This stage identifies conformational states compatible with the observed FRET states.
Selection: Agreement of predicted FRET distributions with experimental smFRET data
Steps
- 1.Predict candidate RNA 3D structures with three modeling toolscandidate structure generators
Generate a collection of candidate conformations for the target ribosomal RNA tertiary contact.
Candidate structures are required before any structural validation, filtering, or FRET back-calculation can occur.
- 2.Validate candidate structures by Watson-Crick base-pairing patterns and filter by eRMSD threshold
Retain structurally plausible models for downstream FRET prediction.
Structural filtering is performed before FRET prediction so only retained structures are subjected to the more specific comparison against experimental data.
- 3.Compute Cy3/Cy5 accessible contact volumes and predict FRET distributions with FRETrajFRET back-calculation method
Translate retained structures into predicted FRET distributions.
FRET prediction is done after structural filtering because the abstract states it is performed for each retained structure.
- 4.Compare and weight predicted FRET distributions against experimental smFRET data
Identify conformational states compatible with the observed FRET states.
This final comparison uses the predicted observables from the previous step to select experimentally compatible conformational states.
Objective: Integrate experimental cryo-EM data with AI-based structure prediction to accelerate protein structure-function analysis and modeling of challenging targets.
Why it works: The review describes cryo-EM and AI as complementary, with cryo-EM providing experimental structural information and AI providing accurate computational predictions from sequence.
near-atomic experimental visualizationsequence-to-structure predictionmap-guided model integrationcryo-EMadvanced image processingdeep learning structure prediction
AlphaFold3-guided optimization of a photoactivatable endonuclease for top-down genome engineering.
2025Objective: Optimize a photoactivatable restriction enzyme for light-controlled top-down genome engineering using AlphaFold3-guided structural redesign.
Why it works: The workflow uses structural modeling of the MagMboI-DNA complex to identify split-site changes expected to improve interface area, complex stability, and protein-DNA contacts, then tests whether those predicted improvements translate into in vivo activity under blue light control.
blue-light-induced heterodimerization restores nuclease activityincreased MagMboI-DNA interface areastrengthened protein-DNA contactspreserved alpha-helical integrityAlphaFold3 structural modelingcomparison of neighboring split-site variantsin vivo testing in Saccharomyces cerevisiae
Stages
- 1.AlphaFold3 modeling of the MagMboI-DNA complex(in_silico_filter)
This stage provides structure-based insight to guide redesign before experimental testing.
Selection: structural insights into interaction between MagMboI and its target DNA recognition sequence required for Mg2+-dependent DNA cleavage
- 2.Comparison of neighboring split-site variants(hit_picking)
This stage narrows candidate split designs to a redesigned variant predicted to improve structural and functional properties.
Selection: identification of an alternative split that increases interface area and enhances complex stability relative to the original construct
- 3.In vivo evaluation in Saccharomyces cerevisiae(confirmatory_validation)
This stage tests whether structure-guided redesign improves in vivo function and reveals tradeoffs not captured by structural prediction alone.
Selection: comparison of blue-light-activated DNA-cleavage activity and genomic rearrangements between MagMboI-plus and the original MagMboI construct
Steps
- 1.Model the MagMboI-DNA complex with AlphaFold3engineered nuclease modeled with a computation method
Obtain structural insight into how MagMboI interacts with its target DNA recognition sequence.
Structural modeling is performed first to guide redesign before selecting alternative split sites for experimental comparison.
- 2.Compare neighboring split-site variants to identify an alternative splitoriginal construct and redesigned variant
Identify a split-site redesign predicted to improve interface area, stability, and protein-DNA contacts.
Variant comparison follows structural modeling because the model provides the rationale for which split-site changes may improve the construct.
- 3.Test MagMboI-plus in Saccharomyces cerevisiae under blue light activationredesigned variant benchmarked against original construct
Determine whether the redesigned variant improves in vivo DNA-cleavage activity and assess genome rearrangement consequences.
In vivo testing is done after redesign selection to confirm whether predicted structural improvements translate into useful cellular performance and to detect liabilities.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
Photocleavagestructural modeling of protein–dna complexesstructure-guided rational redesignTarget processes
editingInput: Light
Implementation Constraints
The reported implementation involved modeling a MagMboI–DNA complex containing the 5'-GATC-3' recognition sequence to infer interactions relevant to Mg2+-dependent cleavage. The downstream biological validation was performed in Saccharomyces cerevisiae using a photoactivatable endonuclease system, but no additional practical details on software setup or input requirements are provided in the supplied evidence.
The supplied evidence is limited to a single 2025 study and one application context involving MagMboI optimization. No independent replication, quantitative accuracy metrics, runtime information, or comparisons to alternative structure-prediction methods are provided here.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Source 2primary paper2026MED
Source 3review2025MED
Ranked Claims
Next-generation countermeasures for Bt resistance include synergistic Cry/Vip pyramiding, CRISPR/Cas9-validated receptor knockouts revealing functional redundancy, Domain III chimerization, PACE, and AlphaFold3-guided rational redesign.
Countermeasures now integrate synergistic Cry/Vip pyramiding, CRISPR/Cas9-validated receptor knockouts revealing functional redundancy, Domain III chimerization (e.g., Cry1A.105), phage-assisted continuous evolution (PACE), and the emerging application of AlphaFold3 for structure-guided rational redesign of resistance-breaking variants.
Cryo-EM and AI-based structure prediction have revolutionized protein modeling by enabling near-atomic resolution visualization and highly accurate computational predictions from amino acid sequences.
MagMboI-plus induced more pronounced genomic rearrangements than the original MagMboI construct in Saccharomyces cerevisiae cells.
genomic rearrangements more pronounced
MagMboI-plus induced more pronounced genomic rearrangements than the original MagMboI construct in Saccharomyces cerevisiae cells.
genomic rearrangements more pronounced
MagMboI-plus induced more pronounced genomic rearrangements than the original MagMboI construct in Saccharomyces cerevisiae cells.
genomic rearrangements more pronounced
MagMboI-plus induced more pronounced genomic rearrangements than the original MagMboI construct in Saccharomyces cerevisiae cells.
genomic rearrangements more pronounced
MagMboI-plus induced more pronounced genomic rearrangements than the original MagMboI construct in Saccharomyces cerevisiae cells.
genomic rearrangements more pronounced
MagMboI-plus induced more pronounced genomic rearrangements than the original MagMboI construct in Saccharomyces cerevisiae cells.
genomic rearrangements more pronounced
MagMboI-plus induced more pronounced genomic rearrangements than the original MagMboI construct in Saccharomyces cerevisiae cells.
genomic rearrangements more pronounced
MagMboI-plus induced more pronounced genomic rearrangements than the original MagMboI construct in Saccharomyces cerevisiae cells.
genomic rearrangements more pronounced
In Saccharomyces cerevisiae cells, MagMboI-plus showed slightly increased DNA-cleavage activity in vivo upon blue light activation compared with the original MagMboI construct.
DNA-cleavage activity change slightly increased
In Saccharomyces cerevisiae cells, MagMboI-plus showed slightly increased DNA-cleavage activity in vivo upon blue light activation compared with the original MagMboI construct.
DNA-cleavage activity change slightly increased
In Saccharomyces cerevisiae cells, MagMboI-plus showed slightly increased DNA-cleavage activity in vivo upon blue light activation compared with the original MagMboI construct.
DNA-cleavage activity change slightly increased
In Saccharomyces cerevisiae cells, MagMboI-plus showed slightly increased DNA-cleavage activity in vivo upon blue light activation compared with the original MagMboI construct.
DNA-cleavage activity change slightly increased
In Saccharomyces cerevisiae cells, MagMboI-plus showed slightly increased DNA-cleavage activity in vivo upon blue light activation compared with the original MagMboI construct.
DNA-cleavage activity change slightly increased
In Saccharomyces cerevisiae cells, MagMboI-plus showed slightly increased DNA-cleavage activity in vivo upon blue light activation compared with the original MagMboI construct.
DNA-cleavage activity change slightly increased
In Saccharomyces cerevisiae cells, MagMboI-plus showed slightly increased DNA-cleavage activity in vivo upon blue light activation compared with the original MagMboI construct.
DNA-cleavage activity change slightly increased
In Saccharomyces cerevisiae cells, MagMboI-plus showed slightly increased DNA-cleavage activity in vivo upon blue light activation compared with the original MagMboI construct.
DNA-cleavage activity change slightly increased
Cryo-EM and AI have complementary roles in modern protein structural biology.
AlphaFold3-based prediction can accelerate functional improvements in engineered enzymes and provide a strategy for developing light-controlled genome engineering tools.
AlphaFold3-based prediction can accelerate functional improvements in engineered enzymes and provide a strategy for developing light-controlled genome engineering tools.
AlphaFold3-based prediction can accelerate functional improvements in engineered enzymes and provide a strategy for developing light-controlled genome engineering tools.
AlphaFold3-based prediction can accelerate functional improvements in engineered enzymes and provide a strategy for developing light-controlled genome engineering tools.
AlphaFold3-based prediction can accelerate functional improvements in engineered enzymes and provide a strategy for developing light-controlled genome engineering tools.
AlphaFold3-based prediction can accelerate functional improvements in engineered enzymes and provide a strategy for developing light-controlled genome engineering tools.
AlphaFold3-based prediction can accelerate functional improvements in engineered enzymes and provide a strategy for developing light-controlled genome engineering tools.
AlphaFold3-based prediction can accelerate functional improvements in engineered enzymes and provide a strategy for developing light-controlled genome engineering tools.
AlphaFold predictions have been combined with cryo-EM maps to explore conformational diversity in cytochrome P450 enzymes.
MagMboI functions through a split-protein strategy in which blue-light-induced heterodimerization restores nuclease activity.
MagMboI functions through a split-protein strategy in which blue-light-induced heterodimerization restores nuclease activity.
MagMboI functions through a split-protein strategy in which blue-light-induced heterodimerization restores nuclease activity.
MagMboI functions through a split-protein strategy in which blue-light-induced heterodimerization restores nuclease activity.
MagMboI functions through a split-protein strategy in which blue-light-induced heterodimerization restores nuclease activity.
MagMboI functions through a split-protein strategy in which blue-light-induced heterodimerization restores nuclease activity.
MagMboI functions through a split-protein strategy in which blue-light-induced heterodimerization restores nuclease activity.
MagMboI functions through a split-protein strategy in which blue-light-induced heterodimerization restores nuclease activity.
AlphaFold3 was used to model the MagMboI-DNA complex and provide structural insight into interaction with the 5'-GATC-3' recognition sequence required for Mg2+-dependent DNA cleavage.
AlphaFold3 was used to model the MagMboI-DNA complex and provide structural insight into interaction with the 5'-GATC-3' recognition sequence required for Mg2+-dependent DNA cleavage.
AlphaFold3 was used to model the MagMboI-DNA complex and provide structural insight into interaction with the 5'-GATC-3' recognition sequence required for Mg2+-dependent DNA cleavage.
AlphaFold3 was used to model the MagMboI-DNA complex and provide structural insight into interaction with the 5'-GATC-3' recognition sequence required for Mg2+-dependent DNA cleavage.
AlphaFold3 was used to model the MagMboI-DNA complex and provide structural insight into interaction with the 5'-GATC-3' recognition sequence required for Mg2+-dependent DNA cleavage.
AlphaFold3 was used to model the MagMboI-DNA complex and provide structural insight into interaction with the 5'-GATC-3' recognition sequence required for Mg2+-dependent DNA cleavage.
AlphaFold3 was used to model the MagMboI-DNA complex and provide structural insight into interaction with the 5'-GATC-3' recognition sequence required for Mg2+-dependent DNA cleavage.
AlphaFold3 was used to model the MagMboI-DNA complex and provide structural insight into interaction with the 5'-GATC-3' recognition sequence required for Mg2+-dependent DNA cleavage.
MagMboI is a photoactivatable restriction enzyme designed for light-controlled top-down genome engineering.
MagMboI is a photoactivatable restriction enzyme designed for light-controlled top-down genome engineering.
MagMboI is a photoactivatable restriction enzyme designed for light-controlled top-down genome engineering.
MagMboI is a photoactivatable restriction enzyme designed for light-controlled top-down genome engineering.
MagMboI is a photoactivatable restriction enzyme designed for light-controlled top-down genome engineering.
MagMboI is a photoactivatable restriction enzyme designed for light-controlled top-down genome engineering.
MagMboI is a photoactivatable restriction enzyme designed for light-controlled top-down genome engineering.
MagMboI is a photoactivatable restriction enzyme designed for light-controlled top-down genome engineering.
These technologies facilitate detailed insights into challenging protein targets including membrane proteins, flexible and intrinsically disordered proteins, and large macromolecular complexes.
An alternative split-site variant, MagMboI-plus, increases the MagMboI-DNA interface area and enhances complex stability relative to the original construct.
An alternative split-site variant, MagMboI-plus, increases the MagMboI-DNA interface area and enhances complex stability relative to the original construct.
An alternative split-site variant, MagMboI-plus, increases the MagMboI-DNA interface area and enhances complex stability relative to the original construct.
An alternative split-site variant, MagMboI-plus, increases the MagMboI-DNA interface area and enhances complex stability relative to the original construct.
An alternative split-site variant, MagMboI-plus, increases the MagMboI-DNA interface area and enhances complex stability relative to the original construct.
An alternative split-site variant, MagMboI-plus, increases the MagMboI-DNA interface area and enhances complex stability relative to the original construct.
An alternative split-site variant, MagMboI-plus, increases the MagMboI-DNA interface area and enhances complex stability relative to the original construct.
An alternative split-site variant, MagMboI-plus, increases the MagMboI-DNA interface area and enhances complex stability relative to the original construct.
MagMboI-plus preserves alpha-helical integrity while strengthening protein-DNA contacts.
MagMboI-plus preserves alpha-helical integrity while strengthening protein-DNA contacts.
MagMboI-plus preserves alpha-helical integrity while strengthening protein-DNA contacts.
MagMboI-plus preserves alpha-helical integrity while strengthening protein-DNA contacts.
MagMboI-plus preserves alpha-helical integrity while strengthening protein-DNA contacts.
MagMboI-plus preserves alpha-helical integrity while strengthening protein-DNA contacts.
MagMboI-plus preserves alpha-helical integrity while strengthening protein-DNA contacts.
MagMboI-plus preserves alpha-helical integrity while strengthening protein-DNA contacts.
Approval Evidence
4 sources4 linked approval claimsfirst-pass slugs alphafold-3, alphafold3, alphafold3-guided-rational-redesign
the emerging application of AlphaFold3 for structure-guided rational redesign of resistance-breaking variants
Source:
We predicted 3D structures ... using three popular RNA 3D modeling tools, namely RNAComposer, FARFAR2, and AlphaFold3
Source:
Using AlphaFold3, we modeled the structure of the MagMboI-DNA complex and gained structural insights into the interaction between MagMboI and its target DNA recognition sequence (5'-GATC-3') required for Mg2+-dependent DNA cleavage.
Source:
We discuss the complementary roles of cryo-EM and AI, including developments in direct electron detectors, advanced image processing, and deep learning algorithms exemplified by AlphaFold 2 and the emerging AlphaFold 3.
Source:
engineering strategysupports
Next-generation countermeasures for Bt resistance include synergistic Cry/Vip pyramiding, CRISPR/Cas9-validated receptor knockouts revealing functional redundancy, Domain III chimerization, PACE, and AlphaFold3-guided rational redesign.
Countermeasures now integrate synergistic Cry/Vip pyramiding, CRISPR/Cas9-validated receptor knockouts revealing functional redundancy, Domain III chimerization (e.g., Cry1A.105), phage-assisted continuous evolution (PACE), and the emerging application of AlphaFold3 for structure-guided rational redesign of resistance-breaking variants.
Source:
method compositionsupports
Candidate RNA 3D conformations were generated using RNAComposer, FARFAR2, and AlphaFold3.
Source:
general strategysupports
AlphaFold3-based prediction can accelerate functional improvements in engineered enzymes and provide a strategy for developing light-controlled genome engineering tools.
Source:
structure guided designsupports
AlphaFold3 was used to model the MagMboI-DNA complex and provide structural insight into interaction with the 5'-GATC-3' recognition sequence required for Mg2+-dependent DNA cleavage.
Source:
Comparisons
Source-backed strengths
The cited study reports that AlphaFold3 supported modeling of the MagMboI–DNA complex and inference of contacts relevant to Mg2+-dependent DNA cleavage. In the resulting application, the AlphaFold3-guided MagMboI-plus construct induced more pronounced genomic rearrangements than the original MagMboI construct in Saccharomyces cerevisiae cells.
Ranked Citations
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