In silico design of DNA sequences for in vivo nucleosome positioning

The computational design of synthetic DNA sequences with desired in vivo properties is gaining traction in the field of synthetic genomics. We propose here a computational method which combines a kinetic Monte Carlo framework with a deep mutational screening based on deep learning predictions. We apply our method to build regular nucleosome arrays with tailored nucleosomal repeat lengths (NRL) in yeast. Our design is validated in vivo by successfully engineering and integrating thousands of kilobases long tandem arrays of computationally optimized sequences which could accommodate NRLs much larger than the yeast natural NRL. This method delineates the key sequence rules for nucleosome positioning in yeast and is readily applicable to other sequence properties and other genomes.