Graphical abstract template · Drug discovery & proteins · Structure prediction
Inside AlphaFold 2 and AlphaFold 3
The two architectures side by side: AlphaFold 2 refines an MSA and a pair representation through 48 Evoformer blocks and builds the chain with a structure module; AlphaFold 3 keeps a lighter 4-block MSA module, 48 Pairformer blocks and generates atom coordinates with a diffusion module, for complexes of proteins, nucleic acids, small molecules and ions.
Updated 4 cited papers21 editable layers
At a glance
- Category
- Drug discovery & proteins
- Method
- Structure prediction
- Starting materials
- Sequences (and for AlphaFold 3, ligands, ions and modified residues)
- Product
- Atomic coordinates with confidence (pLDDT, PAE)
- Key result
- Block diagram drawn after the papers; schematic, not to scale
- Figure
- 1920 × 1080 px (16:9), 21 editable layers, 2 generated objects
Step by step
- InputsGenetic and structure database searches give an MSA and templates
- Evoformer48 blocks exchange information between the MSA and pair representations (Jumper 2021; block count from Abramson 2024)
- Structure moduleBuilds the chain from residue frames and side-chain angles (AlphaFold 2)
- AlphaFold 3 trunkA 4-block MSA module, then 48 Pairformer blocks on single and pair representations (Abramson 2024)
- DiffusionAtom coordinates generated from noise; a confidence head predicts pLDDT, PAE and PDE (Abramson 2024)
Strengths
- One framework from proteins to complexes
- Confidence heads alongside the structure
Limitations
- Diagrams simplify: many layers and inputs are left out
Uses
- Teaching slides
- Methods figures
- Reviews
Papers this template follows
- Jumper et al., “Highly accurate protein structure prediction with AlphaFold”, Nature 2021. doi:10.1038/s41586-021-03819-2primary
- Abramson et al., “Accurate structure prediction of biomolecular interactions with AlphaFold 3”, Nature 2024. doi:10.1038/s41586-024-07487-wprimary
- Baek et al., “Accurate prediction of protein structures and interactions using a three-track neural network”, Science 2021. doi:10.1126/science.abj8754further reading
- Krishna et al., “Generalized biomolecular modeling and design with RoseTTAFold All-Atom”, Science 2024. doi:10.1126/science.adl2528further reading
After Jumper et al., Nature 2021 (doi:10.1038/s41586-021-03819-2) and Abramson et al., Nature 2024 (doi:10.1038/s41586-024-07487-w). Schematic block diagrams; block counts as stated in the AlphaFold 3 paper.
How templates are written and checked: editorial guidelines.
Questions
What does the “Inside AlphaFold 2 and AlphaFold 3” template show?
The two architectures side by side: AlphaFold 2 refines an MSA and a pair representation through 48 Evoformer blocks and builds the chain with a structure module; AlphaFold 3 keeps a lighter 4-block MSA module, 48 Pairformer blocks and generates atom coordinates with a diffusion module, for complexes of proteins, nucleic acids, small molecules and ions.
What are the steps shown in “Inside AlphaFold 2 and AlphaFold 3”?
1. Inputs: Genetic and structure database searches give an MSA and templates. 2. Evoformer: 48 blocks exchange information between the MSA and pair representations (Jumper 2021; block count from Abramson 2024). 3. Structure module: Builds the chain from residue frames and side-chain angles (AlphaFold 2). 4. AlphaFold 3 trunk: A 4-block MSA module, then 48 Pairformer blocks on single and pair representations (Abramson 2024). 5. Diffusion: Atom coordinates generated from noise; a confidence head predicts pLDDT, PAE and PDE (Abramson 2024).
What are the starting materials and the product?
Starting materials: Sequences (and for AlphaFold 3, ligands, ions and modified residues). Product: Atomic coordinates with confidence (pLDDT, PAE).
Which papers is “Inside AlphaFold 2 and AlphaFold 3” based on?
Jumper et al., “Highly accurate protein structure prediction with AlphaFold”, Nature 2021 (doi:10.1038/s41586-021-03819-2); Abramson et al., “Accurate structure prediction of biomolecular interactions with AlphaFold 3”, Nature 2024 (doi:10.1038/s41586-024-07487-w); Baek et al., “Accurate prediction of protein structures and interactions using a three-track neural network”, Science 2021 (doi:10.1126/science.abj8754); Krishna et al., “Generalized biomolecular modeling and design with RoseTTAFold All-Atom”, Science 2024 (doi:10.1126/science.adl2528).
Can I edit the “Inside AlphaFold 2 and AlphaFold 3” template?
Yes. Open it in the LabCanvas editor: all 21 layers are editable, including 2 generated objects (structures, plots, equipment) with their own settings. Change the text, colours and layout, then export PNG, TIFF, SVG or PDF.
Do I need an account to use this template?
Not to view it here. To open and edit it, sign in to LabCanvas or create a free account (the free plan keeps up to 3 designs). Downloading the figure is part of LabCanvas Pro (US$19 a month or US$199 a year).
