TY - JOUR TI - Predicting bacterial promoter function and evolution from random sequences AU - Lagator, Mato AU - Sarikas, Srdjan AU - Steinrueck, Magdalena AU - Toledo-Aparicio, David AU - Bollback, Jonathan P AU - Guet, Calin C AU - Tkačik, Gašper A2 - Krishna, Sandeep A2 - Walczak, Aleksandra M A2 - van Nimwegen, Erik A2 - Einav, Tal VL - 11 PY - 2022 DA - 2022/01/26 SP - e64543 C1 - eLife 2022;11:e64543 DO - 10.7554/eLife.64543 UR - https://doi.org/10.7554/eLife.64543 AB - 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. KW - gene regulation KW - RNA polymerase KW - genotype-phenotype map KW - adaptive evolution KW - promoter JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -