# AlphaFold: a predicted structure and how sure it is

> From a sequence and its alignment to a 3D model coloured by confidence: per-residue pLDDT, the predicted aligned error between domains, and AlphaFold 3 complexes with ligands. The AlphaFold Database opened with over 360,000 predicted structures across 21 proteomes.

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- Open in the LabCanvas editor (LabCanvas account): https://www.labcanvas.com/editor.html?template=alphafold-predicted-structure-confidence
- Updated: 2026-10-14
- Category: Drug discovery & proteins
- Method: Structure prediction
- Starting materials: An amino acid sequence and its multiple sequence alignment
- Product: A 3D model with per-residue confidence (pLDDT) and pairwise error (PAE)
- Key result: Confidence bands as in the AlphaFold Database (Varadi 2022). Alignment, structure, PAE and pLDDT traces are synthetic examples
- Figure: 1920 × 1080 px, 63 editable layers (5 generated objects with settings)

## Steps

1. **Alignment**: Homologous sequences found in genetic databases; coevolving columns carry contact information
2. **Prediction**: AlphaFold 2 predicts structures with atomic accuracy competitive with experiment in most CASP14 cases (Jumper 2021)
3. **Confidence**: pLDDT per residue: above 90 very high, below 50 very low (often disordered)
4. **Domains**: The PAE map shows which parts are placed confidently relative to each other
5. **Complexes**: AlphaFold 3 predicts complexes with nucleic acids, small molecules, ions and modified residues (Abramson 2024)

## Strengths

- Structures for almost any sequence, in minutes
- Confidence scores say which parts to trust
- Open database of predictions

## Limitations

- Low pLDDT regions are often disordered, not wrong folds
- One static state per prediction; dynamics and alternative states are missed

## Uses

- Drug target structures
- Experimental model building (cryo-EM, crystallography)
- Protein design checks

## References

1. Jumper et al., “Highly accurate protein structure prediction with AlphaFold”, Nature 2021. https://doi.org/10.1038/s41586-021-03819-2 (primary)
2. Varadi et al., “AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models”, Nucleic Acids Res. 2022. https://doi.org/10.1093/nar/gkab1061 (primary)
3. Abramson et al., “Accurate structure prediction of biomolecular interactions with AlphaFold 3”, Nature 2024. https://doi.org/10.1038/s41586-024-07487-w (primary)
4. Mariani et al., “lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests”, Bioinformatics 2013. https://doi.org/10.1093/bioinformatics/btt473 (method origin)

Credit: After Jumper et al., Nature 2021 (doi:10.1038/s41586-021-03819-2), Varadi et al., Nucleic Acids Res. 2022 (doi:10.1093/nar/gkab1061) and Abramson et al., Nature 2024 (doi:10.1038/s41586-024-07487-w). Structures, alignment, PAE and pLDDT are synthetic examples; numbers from the papers.

## Questions

**What does the “AlphaFold: a predicted structure and how sure it is” template show?** From a sequence and its alignment to a 3D model coloured by confidence: per-residue pLDDT, the predicted aligned error between domains, and AlphaFold 3 complexes with ligands. The AlphaFold Database opened with over 360,000 predicted structures across 21 proteomes.

**What are the steps shown in “AlphaFold: a predicted structure and how sure it is”?** 1. Alignment: Homologous sequences found in genetic databases; coevolving columns carry contact information. 2. Prediction: AlphaFold 2 predicts structures with atomic accuracy competitive with experiment in most CASP14 cases (Jumper 2021). 3. Confidence: pLDDT per residue: above 90 very high, below 50 very low (often disordered). 4. Domains: The PAE map shows which parts are placed confidently relative to each other. 5. Complexes: AlphaFold 3 predicts complexes with nucleic acids, small molecules, ions and modified residues (Abramson 2024).

**What are the starting materials and the product?** Starting materials: An amino acid sequence and its multiple sequence alignment. Product: A 3D model with per-residue confidence (pLDDT) and pairwise error (PAE).

**Which papers is “AlphaFold: a predicted structure and how sure it is” based on?** Jumper et al., “Highly accurate protein structure prediction with AlphaFold”, Nature 2021 (doi:10.1038/s41586-021-03819-2); Varadi et al., “AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models”, Nucleic Acids Res. 2022 (doi:10.1093/nar/gkab1061); Abramson et al., “Accurate structure prediction of biomolecular interactions with AlphaFold 3”, Nature 2024 (doi:10.1038/s41586-024-07487-w); Mariani et al., “lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests”, Bioinformatics 2013 (doi:10.1093/bioinformatics/btt473).

**Can I edit the “AlphaFold: a predicted structure and how sure it is” template?** Yes. Open it in the LabCanvas editor: all 63 layers are editable, including 5 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).