Python implementation of the Promoter Calculator v1.0 originally by Travis La Fleur, Ayaan Hossain, and Howard Salis. This fork is maintained by the Barrick Lab to support a streamlined installation process, parallel processing, and light output filtering to reduce memory utilization.
When using this code, remember to cite its publication: La Fleur, Travis L., Ayaan Hossain, and Howard M. Salis. "Automated Model-Predictive Design of Synthetic Promoters to Control Transcriptional Profiles in Bacteria." bioRxiv (2021)
Correspondence about the original algorithm should be addressed to H.M.S. ([email protected]). Bug reports and questions about the features specific to this fork should be asked here by opening an issue.
Abstract: Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combined massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any σ70 promoter sequence, validated across 17396 bacterial promoters with diverse sequences. We applied the model to predict genetic context effects, design σ70 promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems.