---
title: DeepSol on the platform
description: Predict protein solubility for E. coli expression from an amino acid sequence.
tags:
  - deepsol
---

DeepSol predicts protein solubility from sequence using a convolutional neural network, flagging whether a protein is likely to express solubly. Reach for it when a solubility call gates the next step — screening expression constructs or triaging candidates before wet-lab work. It predicts solubility by a different method than [GraphSol](/docs/graphsol), so run both and treat agreement as a consensus. See [what you can achieve](/docs/capabilities#admet-and-safety) for the full set.

## What DeepSol does

DeepSol is a deep learning-based **protein solubility predictor** using convolutional neural networks (CNNs). Published in Bioinformatics, it exploits k-mer structure and additional sequence/structural features to predict whether proteins will be soluble when expressed in *E. coli*.

### Citations

> Khurana S, Rawi R, Kunji K, Chuang GY, Bensmail H, Mall R. DeepSol: a deep learning framework for sequence-based protein solubility prediction. *Bioinformatics*. 2018 Aug 1;34(15):2605-2613. doi: [10.1093/bioinformatics/bty166](https://doi.org/10.1093/bioinformatics/bty166)

### Performance benchmarks

DeepSol outperformed all known sequence-based solubility prediction methods:
- **Accuracy**: 0.77
- **Matthew's correlation coefficient (MCC)**: 0.55
- At least **3.5% more accurate** than PaRSnIP
- At least **15% more accurate** than PROSO II

DeepSol is freely available at https://zenodo.org/record/1162886.

## Webapp integration and capabilities

### Input in .bloom*

- **Amino acid sequence**: Paste standard 1-letter-code sequence directly, or fetch by UniProt accession ID (e.g. `P00533`).
- **Model**: Choose `deepsol1` (default), `deepsol2`, or `deepsol3`.
- **SCRATCH-1D features**: Enabled by default; adds structural features to the CNN input alongside the k-mer sequence features.

### Prediction outputs

- **Solubility score**: 0–1, where a score above 0.5 indicates likely soluble expression.
- **Solubility class**: `soluble` or `insoluble`, from the 0.5 threshold.
- **Confidence score**: model certainty for the call.

## Model characteristics and limitations

- **Sequence length**: 20–2000 residues (see [inputs & parameters](/docs/deepsol/inputs) for the recommended 50–2000 range).
- **Standard residues only**: non-standard amino acids (X, B, Z, J) are rejected.
- **Applicability**: trained on *E. coli* expression data — accuracy for other expression hosts hasn't been validated by the platform.
- **DeepSol predicts by CNN sequence features; [GraphSol](/docs/graphsol) predicts from a structure-aware graph built on predicted contact maps.** The two use different signals, so treat agreement between them as a consensus rather than relying on either alone.

## Chat Usage

### Example Prompts

- "Predict solubility for this protein sequence using DeepSol"
- "Is this protein likely to be soluble? Run DeepSol"

### Workflow Chaining

- **Feeds into**: expression-construct triage, GraphSol consensus check
- **Accepts from**: UniProt (sequences), ESMFold (predicted structures)

## Read next