DeepSol results and interpretation

You get back a solubility probability (0–1) and a soluble/insoluble call for each sequence. That score sets your next move: a high score prioritizes a protein for direct expression, while a low one flags likely inclusion-body formation, so you can plan solubility tags, refolding, or an alternative host before running the trial.

Output fields

• id: your submitted identifier from FASTA header or CSV (if provided).
• sequence: the input amino acid sequence processed by the model.
• solubility_score: predicted probability of soluble E. coli expression (0-1 scale).
• prediction: binary classification (soluble or insoluble) based on 0.5 threshold.
• confidence: model certainty derived from CNN output layer.
• warnings: array of strings with validation or domain-fit notes.

Interpreting solubility scores

The solubility score represents the probability that a protein will be expressed in soluble form in E. coli:

• 0.7-1.0 (High): Strong likelihood of soluble expression; prioritize for direct expression trials.
• 0.5-0.7 (Medium): Moderate solubility expected; consider optimizing expression conditions or using solubility tags.
• 0.3-0.5 (Low): Likely to form inclusion bodies; plan for refolding protocols or alternative expression systems.
• 0.0-0.3 (Very Low): High probability of insoluble expression; consider fusion partners, truncations, or alternative hosts.

Model performance context

DeepSol achieves 77% accuracy with a Matthews Correlation Coefficient (MCC) of 0.55 on benchmark datasets. This represents:

• Over 3.5% higher accuracy than PaRSnIP
• Over 15% higher accuracy than PROSO II

The CNN architecture exploits k-mer structure in protein sequences and combines sequence-level features with structural properties derived from the amino acid composition.

Calibration and limits

• Model trained and validated on E. coli expression data; predictions are specific to bacterial expression systems.
• Predictions for proteins far from the training domain (e.g., membrane proteins, highly disordered sequences) may be less reliable.
• The model predicts intrinsic solubility tendency; actual expression outcomes also depend on conditions, strain, and protocol.
• For critical decisions, validate predictions experimentally with small-scale expression trials.

Quality checks

• Confirm warnings is empty before downstream use; otherwise flag for manual review.
• Track distribution of scores across your protein set to identify potential batch effects.
• Compare predictions against any known soluble/insoluble proteins in your dataset as internal controls.
• Consider the biological context: some proteins may require cofactors or partners for proper folding.

Canvas Visualization

When a DeepSol job completes, a result card appears on the canvas.

• Card size: 350x300px
• Border color: Orange (#FF9800)
• Visualization: Solubility prediction summary

Tier 2 Metrics

• Solubility score
• Confidence percentage
• LogP
• Molecular weight
• HBD / HBA counts

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