---
title: DeepSol quickstart
description: >-
  Fast path to predict protein solubility for E. coli expression using CNN-based
  deep learning.
---

You want to know whether a protein will express solubly in E. coli before you commit to an expression trial. This walks you from a single amino acid sequence (or a FASTA batch) to a solubility prediction in five steps: open the tool, add your sequences, configure settings, submit, and interpret the scores.

## Quick protein solubility prediction with .bloom* platform

DeepSol uses convolutional neural networks (CNNs) to predict protein solubility when expressed in E. coli. Published in Bioinformatics 2018, the model exploits k-mer structure and combines sequence-level and structural features to achieve 77% accuracy.

## Step-by-step solubility analysis

### 1. Access the DeepSol tool

Navigate to the **Protein Solubility Prediction** tool in .bloom*'s interactive workspace. The tool provides guided input validation and instant result visualization powered by the DeepSol CNN architecture.

### 2. Prepare your protein sequences

**Option A: Single sequence (fastest)**
- Click **Sequence Input** in the proteins section
- Enter your amino acid sequence in single-letter code (e.g., "MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSH...")
- .bloom* automatically validates the sequence and shows basic properties
- Preview sequence length and composition before submission

**Option B: Batch processing**
- Click **File Upload** in the proteins section
- Upload a FASTA file with multiple protein sequences
- Optional: Include descriptive headers for tracking
- .bloom* parses the file and shows sequence count and validation status

**Option C: From previous protein jobs**
- Click **From Previous Job** in the proteins section
- Select outputs from AlphaFold, protein design tools, or other workflows
- Choose specific sequences or use all generated proteins
- .bloom* handles format conversion automatically

### 3. Configure prediction settings

**Basic settings (recommended):**
- Keep default parameters for standard predictions
- Job name: Enter descriptive name or leave blank for auto-generation

**Advanced settings (via gear icon):**
- **Batch size**: Adjust for optimal processing (default 32)
- **Binary classification**: Return soluble/insoluble labels
- **Probability scores**: Include raw CNN output probabilities
- **Feature extraction**: Enable to output learned representations

### 4. Submit and monitor the job

- Click **Predict Solubility** to start analysis
- .bloom* shows real-time progress as sequences pass through the CNN layers
- Typical completion: seconds to minutes depending on batch size
- Receive notifications when complete (if enabled in settings)

### 5. Analyze and interpret results

**Interactive result exploration:**
- View results in sortable table format
- Filter by solubility prediction, confidence scores, or warnings
- Visualize distribution with histograms and scatter plots
- Compare proteins side-by-side with property overlays

**Result interpretation:**
- **Solubility score**: Probability of soluble expression (0-1 scale)
- **Binary prediction**: Soluble or insoluble classification
- **Confidence scores**: Model certainty based on CNN output
- **Warnings**: Flags for sequences outside training domain

**Export options:**
- **CSV download**: Full results with all properties
- **Filtered datasets**: Export only sequences meeting criteria
- **Visualization exports**: Charts and plots for reports

## Common prediction scenarios

### Protein library screening
1. Upload FASTA file with protein variants
2. Run batch prediction with default settings
3. Filter results by solubility score (focus on high probability soluble)
4. Export prioritized sequence list for expression trials

### Protein engineering optimization
1. Prepare sequences for protein variants
2. Submit batch prediction
3. Sort by solubility scores to identify trends
4. Visualize sequence-solubility relationships
5. Guide selection of best candidates for E. coli expression

### Expression construct assessment
1. Input candidate protein sequences from design pipelines
2. Check solubility predictions against expression requirements
3. Flag sequences likely to form inclusion bodies
4. Generate reports for downstream expression planning

## Quality control and validation

### Input preparation checklist

**Sequence format:**
- [ ] Valid amino acid sequence (standard 20 amino acids)
- [ ] No ambiguous residues (X, B, Z, J) unless specifically supported
- [ ] Single sequence per entry (no multi-chain complexes)
- [ ] Sequence length within model limits

**Sequence properties:**
- [ ] Sequence length in optimal range (typically 50-2000 residues)
- [ ] No non-standard amino acids or post-translational modifications
- [ ] Represents mature protein (signal peptides removed if applicable)
- [ ] Consistent with E. coli codon usage considerations

### Result validation

**Prediction reliability:**
- [ ] Check confidence scores (higher = more reliable)
- [ ] Review warning flags and their implications
- [ ] Validate against known soluble/insoluble proteins when possible
- [ ] Consider experimental verification for critical decisions

**Batch consistency:**
- [ ] Ensure consistent sequence formatting across proteins
- [ ] Check for duplicates that may skew results
- [ ] Verify batch size is appropriate for dataset
- [ ] Monitor for systematic patterns in predictions

## Troubleshooting common issues

### Invalid sequence errors
- Check for non-standard amino acid characters
- Remove header lines if pasting raw sequences
- Ensure proper FASTA format for file uploads
- Consider sequence cleanup tools for normalization

### Poor predictions
- Verify sequences are within the training domain
- Check sequence length and composition
- Consider if proteins have extreme properties (very hydrophobic, disordered)
- Review training domain limitations for specialized protein classes

### Batch processing issues
- Split very large FASTA files into smaller batches
- Ensure consistent sequence formatting across the dataset
- Check for encoding issues in uploaded files
- Validate FASTA format and header specifications

### Performance issues
- Reduce batch size for very long sequences
- Consider splitting jobs for very large datasets
- Check file format and remove unnecessary metadata
- Monitor system resources during processing

## Next steps after prediction

**Protein prioritization:**
- Use solubility data to rank expression candidates
- Combine with other biophysical predictions for comprehensive assessment
- Guide protein engineering optimization efforts

**Expression planning:**
- Identify proteins likely to require refolding from inclusion bodies
- Plan alternative expression strategies for low-solubility candidates
- Design fusion tags or solubility-enhancing mutations based on predictions

**Further analysis:**
- Integrate with structure prediction tools
- Feed results to protein design workflows for optimization
- Generate comprehensive expression profiling reports

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