Reading the output
What each panel means and how to act on it.
pLDDT — structure confidence
pLDDT (predicted Local Distance Difference Test) is a per-residue confidence score reported by structure prediction models. foldfunc reports the mean across the full sequence. Use it as a guide to how much to trust the predicted fold, not as a quality score for the protein itself.
Very high confidence
Well-ordered region, likely reflects the true structure.
Confident
Generally reliable. Minor local errors possible.
Low confidence
Treat with caution. May indicate flexibility or prediction uncertainty.
Very low / disordered
Likely intrinsically disordered. Predicted coordinates are not meaningful.
3D structure viewer
The viewer renders the predicted structure in cartoon representation with two colour modes, toggled via the Rainbow / Mutations switch:
Rainbow
Spectral colouring from blue (N-terminus) to red (C-terminus). Useful for tracking chain topology and orientation.
Mutations
Residues coloured by mutational sensitivity — green for tolerant positions, red for conserved/critical ones. Use this to visually locate mutation hotspots on the 3D structure.
You can rotate (click + drag), zoom (scroll), and pan (right-click + drag). The viewer runs entirely in the browser — no data is sent at this stage.
Mutation scores
Each scored position displays a wild-type log-probability — how expected that amino acid is at that position according to evolutionary patterns learned from millions of protein sequences. The heatmap colour reflects mutational tolerance:
Click any position in the heatmap to expand the full breakdown: all 20 amino acid scores shown as bars, plus the top 5 suggested substitutions ranked by log-probability. The top substitution is the amino acid our scoring model considers most probable at that site given the surrounding sequence context.
AI interpretation
The AI panel is structured into four parts:
Protein family
A classification based on sequence patterns and literature context. Treat as a hypothesis, not a definitive annotation.
Structural observations
Key observations about the predicted structure — notable domains, disordered regions, and how confidence distributes across the sequence.
Research questions
Open questions the analysis raises. Useful for framing next experimental steps.
Confidence note
An honest assessment of prediction reliability given sequence length, pLDDT, and available literature. Lower-confidence outputs are flagged explicitly.
Literature snippets
Up to 3 paper titles are shown, selected by relevance to your protein name. These are the same publications used to ground the AI interpretation. Click any title to read the full abstract. If no protein name was provided, this panel will be empty.