> ## Documentation Index
> Fetch the complete documentation index at: https://proto.evodesign.org/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Promoter Strength
> Evaluate promoter strength using Salis Lab Promoter Calculator
**License:** Salis Lab Promoter Calculator has a GPL-3.0 license. Please refer to [the license](https://github.com/barricklab/promoter-calculator/blob/master/LICENSE) for full terms.
This constraint is open source. Any third-party models, product names, or trademarks referenced are the property of their respective owners, and Proto is not affiliated with them.
```bibtex theme={null}
@article{lafleur2022promoter,
title={Automated Model-Predictive Design of Synthetic Promoters to Control Transcriptional Profiles in Bacteria},
author={LaFleur, Travis L. and Hossain, Ayaan and Salis, Howard M.},
journal={Nature Communications},
volume={13},
number={1},
pages={5159},
year={2022},
doi={10.1038/s41467-022-32829-5}
}
```
Evaluate bacterial promoter strength using Salis Lab Promoter Calculator.
This constraint function uses the Salis Lab Promoter Calculator to predict
E. coli sigma-70 promoter strength. The calculator scans sequences for canonical
promoter elements (-10 and -35 boxes) and computes either binding free energy (dG)
or predicted transcription initiation rate (tx\_rate).
The constraint returns penalty scores where lower values indicate stronger
promoters. The penalty mapping differs based on scoring type:
* **dG scoring**: Promoters with dG \< -3.0 kcal/mol are strong (penalty 0.0-0.5)
* **tx\_rate scoring**: Promoters with tx\_rate > 10000 are strong (penalty 0.0-0.5)
The calculator can identify multiple promoters in a single sequence; only the
strongest forward-strand candidate contributes to the penalty.
## API Reference
Configuration for promoter strength constraint using Salis Lab Promoter Calculator.
This class defines configuration parameters for evaluating bacterial promoter
strength using the Salis Lab Promoter Calculator, a biophysical model that
predicts RNA polymerase binding affinity and transcription initiation
rates for sigma-70 promoters in E. coli. The calculator identifies promoter elements
(-10 and -35 boxes, spacer regions) and computes binding free energy (dG) and
predicted transcription rates.
The Salis Lab Promoter Calculator specifically models E. coli sigma-70 promoters.
Penalty scores are mapped from raw predictions:
* **For dG scoring**: Strong promoters (dG \< -3.0) get low penalties (0.0-0.5),
moderate promoters (-3.0 to -1.5) get medium penalties (0.5-1.0), weak or unlikely
promoters (> -1.5) get maximum penalty (1.0).
* **For tx\_rate scoring**: Strong promoters (>10000) get low penalties (0.0-0.5),
moderate promoters (3000-10000) get medium penalties (0.5-1.0), weak
promoters (\<3000) get maximum penalty (1.0).
If True, adds flanking nucleotides to short sequences to meet calculator length minimums
Number of 'A' nucleotides to add on each end when add\_context=True
Number of threads for parallel processing of promoter calculations
If True, treat sequences as circular for promoter detection across ends
Score type to use: 'dG' (binding free energy) or 'tx\_rate' (transcription rate). Defaults to 'dG'.
Options: `dG`, `tx_rate`
ReturnsConstraintOutput
One result per sequence. Score ranges from 0.0 (strong
promoter) to 1.0 (weak/no promoter). `metadata` carries a single
`promoter_strength` dict:
**When promoter is found:**
* `penalty`: Float penalty score (0.0-1.0)
* `tx_rate` OR `dG_rate`: Float best promoter strength value
(depending on scoring\_type)
* `raw_output`: List of dictionaries with detailed promoter predictions
including -10/-35 box positions, sequences, spacer length, and
individual energy terms
**When no promoter is found:**
* `penalty`: Float 1.0 (maximum penalty)
* `reason`: String "no\_promoter\_found"
* `raw_output`: Empty list \[]
## Usage
Evaluating promoter strength using dG scoring:
```python python icon="python" theme={null}
>>> from proto_language.core import Sequence
>>> # lacUV5 promoter padded with 20 nt of A on each side (calculator
>>> # needs ~20 nt of flanking sequence to score the promoter elements)
>>> seq = Sequence("A" * 20 + "AAAATTGTGAGCGGATAACAATTTCACACAGGAAACAGCTATGACC" + "A" * 20, "dna")
>>> config = PromoterStrengthConfig(scoring_type="dG")
>>> results = promoter_strength_constraint([(seq,)], config)
>>> print(results[0].score)
```
## Metadata
| Property | Value |
| --------------- | ------------------------------ |
| Key | `promoter-strength` |
| Function | `promoter_strength_constraint` |
| Category | `sequence_annotation` |
| Mode | `discrete` |
| Uses GPU | `False` |
| Supported Types | `dna` |
evo-design/proto-language/proto\_language/constraint/sequence\_annotation/promoter\_strength\_constraint.py
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