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

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. We parsed a comprehensive database of bacterial promoters for the -35 and -10 hexamer regions of σ⁷⁰-binding promoters and used these sequences to construct the respective position weight matrices (PWM). Next we used a well-characterized set of promoters to train a multivariate linear regression model and learn the mapping between PWM scores of the -35 and -10 hexamers and the promoter strength. We found that the log of the promoter strength is significantly linearly associated with a weighted sum of the -10 and -35 sequence profile scores. We applied our model to 100 sets of 100 randomly generated promoter sequences to generate a sampling distribution of mean strengths of random promoter sequences and obtained a mean of 6E-4 ± 1E-7. Our model was further validated by cross-validation and on independent datasets of characterized promoters. 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 robust estimates of promoter strength. PromoterPredict is available as both a web service (https://promoterpredict.com) and standalone tool (https://github.com/PromoterPredict). Our work presents an intuitive generalization applicable to modelling the strength of other promoter classes.