Abstract
Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10-20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought.
Highlights
Describing the relationship between sequence and function lies at the heart of understanding biology and evolution
To predict gene expression levels from any random sequence, we developed a mechanistic and generalizable model based on statistical thermodynamics that expands on the Standard model by accounting for several structural features of bacterial promoters
We developed a comprehensive and generalizable thermodynamic model (‘Extended model’) that expands on the Standard model by accounting for six essential structural features of bacterial promoters that are not present in the Standard model (Figure 1A and B). (i) The possibility that σ70-RNA polymerase (RNAP) binds the promoter region in multiple configurations that independently and cumulatively contribute to gene expression (Storz, 2014; Belliveau et al, 2018), as commonly observed in transcriptomics studies (Srikumar et al, 2015)
Summary
Describing the relationship between sequence (genotype) and function (phenotype) lies at the heart of understanding biology and evolution. Direct experimental characterizations of genotype- phenotype mapping abound (Lehner, 2013; Kemble et al, 2019), existing technology limits experimental exploration to only a tiny fraction of all possible sequences (Sarkisyan et al, 2016). Due to these technological limitations, there is a need to develop theoretical approaches capable of predicting how any genotype maps onto phenotype (Yi and Dean, 2019). Predictive genotype- phenotype maps are rare and incomplete: predicting protein structure from sequence is possible only
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
