> ## 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.
# Salis Lab Promoter Calculator
> The [Salis Lab Promoter Calculator](https://github.com/barricklab/promoter-calculator) is a 346-parameter biophysical and machine-learning model from the [Salis Lab](https://salislab.net/) that predicts the strength of [σ70](https://en.wikipedia.org/wiki/Sigma_factor) housekeeping promoters in *Escherichia coli*. It scans both strands of an input DNA sequence for every candidate transcription start site, decomposes the surrounding region into the canonical promoter elements ([UP element](https://en.wikipedia.org/wiki/Promoter_(genetics)), −35 hexamer, spacer, [−10 hexamer (Pribnow box)](https://en.wikipedia.org/wiki/Pribnow_box), and discriminator), and returns the predicted [Gibbs free energy](https://en.wikipedia.org/wiki/Gibbs_free_energy) of RNA polymerase holoenzyme binding (`dG_total`) together with a calibrated transcription initiation rate (`Tx_rate`).
**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 toolkit 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}
}
```
| Function | Description | |
| --------------------------- | --------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `run_promoter_calculator()` | Predict E. coli sigma70 promoter strength (dG and Tx rate) on DNA sequences | Docs Source |
## Background
The Promoter Calculator ([LaFleur et al., 2022](https://doi.org/10.1038/s41467-022-32829-5)) predicts site-specific σ70 transcription initiation rates from DNA sequence alone. σ70 is the housekeeping [sigma factor](https://en.wikipedia.org/wiki/Sigma_factor) that recruits *E. coli* RNA polymerase to the majority of constitutive promoters, and its strength is set by the sequence and geometry of the surrounding promoter elements. The model decomposes a candidate promoter into the UP element, the −35 hexamer, the spacer, the extended and core −10 hexamers, the discriminator, and the initial transcribed region, then fits 346 Ridge-regression coefficients that link these elements to an overall binding free energy ΔG\_total. The fitted ΔG\_total is mapped to an absolute transcription initiation rate via `log(TX / TX_ref) = -β (ΔG_total - ΔG_total_ref)`, with the fit grounded in [massively parallel reporter assay](https://en.wikipedia.org/wiki/Reporter_gene) measurements. The released coefficients were trained on 5,193 designed promoters with a single dominant transcription start site and validated against 22,132 diverse bacterial σ70 promoters drawn from multiple datasets.
The reference Python implementation used here is the [barricklab/promoter-calculator](https://github.com/barricklab/promoter-calculator) fork from the [Barrick Lab](https://barricklab.org/), which packages the original [Salis Lab algorithm](https://github.com/hsalis/SalisLabCode/tree/master/Promoter_Calculator) for streamlined installation and adds optional multi-threading for the internal transcription-start-site scan.
### Learning Resources
* [barricklab/promoter-calculator](https://github.com/barricklab/promoter-calculator) (Barrick Lab) - the Python fork used here, with installation instructions and the command-line surface that the wrapper drives.
* [hsalis/SalisLabCode (Promoter\_Calculator)](https://github.com/hsalis/SalisLabCode/tree/master/Promoter_Calculator) (Salis Lab) - the original reference implementation distributed alongside the publication and the source of the trained coefficients.
* [salislab.net](https://salislab.net/) (Salis Lab) - the lab home page and entry point to the hosted Promoter Calculator web service.
## Tools
### Salis Lab Promoter Calculator (`promoter-calculator`)
Scans one or more DNA sequences on both strands for every candidate σ70 transcription start site and returns, per sequence, a list of `PromoterPrediction` rows. Each row carries the predicted TSS position and strand, the binding free energy `dG_total` (kcal/mol), the transcription initiation rate `Tx_rate` (arbitrary units), the DNA spanning the predicted promoter, and the start and end positions of the UP, −35, spacer, −10, and discriminator elements.
#### API Reference
Source
DNA sequences to scan for sigma70 promoters. Labeled positionally (`seq_0`, `seq_1`, ...); results are returned in input order.
Source
CPU threads for the calculator's internal parallelism. Default 1.
Treat sequences as circular (examines the wraparound junction). Default False.
Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`.
Device to run the tool on.
Maximum execution time in seconds. `None` waits indefinitely.
Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded.
Source
Per-sequence predictions.
ID of the input sequence
Predicted promoters across both strands
#### Applications
Use this to quantify σ70 promoter strength when designing or analysing *E. coli* expression cassettes. Common workflows include ranking a synthetic library such as the [Anderson collection](https://parts.igem.org/Promoters/Catalog/Anderson) of J231xx variants by predicted `Tx_rate` to pick a target strength, sweeping a candidate construct to flag unintended cryptic promoters that could drive off-target transcription before ordering DNA, and annotating native intergenic regions across an *E. coli* genome to estimate baseline σ70 activity. Pair the predicted transcription rate with downstream [ribosome binding site](https://en.wikipedia.org/wiki/Shine-Dalgarno_sequence) strength and plasmid copy number when projecting end-to-end protein expression.
#### Usage Tips
* **Set `circular=True` for plasmids and bacterial chromosomes.** Linear scanning of a circular sequence cannot see candidates that span the wraparound origin. When the input is circular, the calculator examines the junction explicitly and recovers any promoter that straddles it.
* **Short inputs need flanking context to score.** The element scan needs roughly 20 nucleotides on either side of the promoter region; sequences shorter than the scan window return no predictions. Pad with neutral flanking sequence when scoring a single promoter element in isolation.
* **Predictions are calibrated for *E. coli* σ70 only.** Applying the model to other organisms or to alternative sigma factors (σS, σ32, σ54, σ28) is not validated. Treat any cross-organism output as a relative ranking, not as a calibrated rate.
* **`Tx_rate` is transcription initiation, not protein expression.** The model captures RNA polymerase binding and open-complex formation. End-to-end expression also depends on the ribosome binding site, mRNA stability, copy number, and growth state, none of which the calculator sees.
* **The model does not see transcription factors, attenuation, or anti-σ factors.** Repression by a TF bound near the promoter, riboswitch attenuation, and anti-σ sequestration all reduce in vivo output without changing the predicted `Tx_rate`. Use the prediction as the unregulated upper bound.
## Toolkit Notes
These apply to every Promoter Calculator tool in this toolkit (`promoter-calculator`).
* **Runs on CPU only.** The model is a Ridge-regression sum over 346 coefficients that ship with the Python source. No neural network is loaded at inference time, and there is no GPU acceleration to enable.
* **Self-contained after install.** The standalone setup builds a Python virtual environment and pulls dependencies once; subsequent runs need no further network access and no model-weight downloads.
* **`threads` parallelises the internal TSS scan within a single sequence.** Raising it shortens wall-clock time on long inputs but does not change the predictions. Across input sequences the wrapper itself runs sequentially.
**Example notebook:** See the [full working example](https://github.com/evo-design/proto-tools/blob/47e34afa5ea240a3b406e323dc38aa5dc85f223e/proto_tools/tools/gene_annotation/promoter_calculator/examples/example.ipynb) for a copy-paste-ready walkthrough.
## Infrastructure Guides
The following guides cover how to run tools efficiently and at scale.
Keep a tool's model warm across calls instead of reloading it every invocation.How GPUs are allocated to tools and how to target specific devices.Fan a batch of inputs out across multiple GPUs.
evo-design/proto-tools/proto\_tools/tools/gene\_annotation/promoter\_calculator
