---
title: DeepSol FAQ
description: DeepSol scores 77% accuracy and MCC 0.55, predicts E. coli expression only, works best from 50 to 2,000 residues, and is unreliable on membrane proteins.
---

These answers cover how accurate [DeepSol](/docs/deepsol) is, the E. coli expression system and sequence lengths it is trained for, how to handle tags and membrane proteins, and how to read borderline solubility scores.

## Frequently asked questions

## How accurate is DeepSol?

DeepSol achieves 77% accuracy with a Matthews Correlation Coefficient (MCC) of 0.55 on benchmark datasets for E. coli expression solubility prediction. This represents over 3.5% higher accuracy than PaRSnIP and over 15% higher accuracy than PROSO II. The model was published in Bioinformatics 2018.

## What is the underlying technology?

DeepSol uses convolutional neural networks (CNNs) that exploit k-mer structure in protein sequences. The model combines sequence-level features with structural properties derived from amino acid composition to predict solubility.

## What organism is this trained for?

DeepSol specifically predicts solubility for proteins expressed in E. coli. Predictions for other expression systems (yeast, insect cells, mammalian) may not be directly applicable.

## Can I submit protein sequences with tags?

Yes. Submit the complete sequence as it will be expressed, including any fusion tags (His-tag, GST, MBP, etc.). The model will predict solubility for the full construct.

## What about membrane proteins?

Membrane proteins and highly hydrophobic sequences may fall outside the training domain. Predictions for these proteins should be treated with caution; check the confidence score and warnings.

## Are results deterministic?

Yes—identical sequences and parameters produce identical outputs.

## How big can batches be?

Keep under ~1,000 sequences for optimal turnaround. Larger sets should be chunked; respect rate limits and retry with backoff on 429/503.

## Why is confidence low?

Likely out-of-domain sequence characteristics, unusual amino acid composition, or extreme sequence length. Check warnings for specific issues.

## What sequence length works best?

The model performs best on sequences between 50-2000 residues. Very short peptides or extremely long proteins may have reduced prediction accuracy.

## How should I interpret borderline scores?

Scores near 0.5 indicate uncertain predictions. For these proteins, consider:
- Optimizing expression conditions (temperature, induction level)
- Using solubility-enhancing fusion tags
- Testing multiple constructs with different truncations
- Small-scale expression trials before scale-up

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