PromoterPredict: sequence-based modelling of Escherichia coli σ⁷⁰ promoter strength yields logarithmic dependence between promoter strength and sequence

We present PromoterPredict, a dynamic multiple regression approach to predict the strength of Escherichia coli promoters binding the σ⁷⁰ factor of RNA polymerase. σ⁷⁰ promoters are ubiquitously used in recombinant DNA technology, but characterizing their strength is demanding in terms of both time and money. Using a well-characterized set of promoters, we trained a multivariate linear regression model and found that the log of the promoter strength is significantly linearly associated with a weighted sum of the –10 and –35 sequence profile scores. It was found that the two regions contributed almost equally to the promoter strength. PromoterPredict accepts –10 and –35 hexamer sequences and returns the predicted promoter strength. It is capable of dynamic learning from user-supplied data to refine the model construction and yield more confident estimates of promoter strength.