# Nucleosome positioning in yeast: from prediction to synthetic design

Alex Westbrook, Etienne Routhier, Julien Mozziconacci, Romain Koszul, Jean-Baptiste Boulé

► To cite this version:

Alex Westbrook, Etienne Routhier, Julien Mozziconacci, Romain Koszul, Jean-Baptiste Boulé. Nucleosome positioning in yeast: from prediction to synthetic design. Journées du GDR BIM, Nov 2023, Villeneuve d'Ascq (Lille 3), France. mnhn-04344672

HAL Id: mnhn-04344672

https://mnhn.hal.science/mnhn-04344672

Submitted on 14 Dec 2023

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Distributed under a Creative Commons Attribution 4.0 International License
# Nucleosome positioning in yeast: from prediction to synthetic design

Alex Westbrook, Etienne Routhier, Julien Mozziconacci

## Introduction

Nucleosomes are the primary elements of DNA folding within eukaryotic cells, essential for both **chromosome structure** and **gene regulation**. Nucleosomal tracks are derived experimentally from MNase sequencing. Using **deep neural networks**, these tracks can be predicted from the DNA sequence alone in yeast.

<figure><img src="image_2.png" /></figure>

## Objectives

* How well does a yeast model **generalize on exogenous sequences** ?

* How can this model help in **designing new sequences** ?

## Exogenous sequences

Yeast strains with a bacterial chromosome

<figure><img src="image_3.png" /></figure>

Different proportions of G and C nucleotides

<figure><img src="image_4.png" /></figure>

Sample of the predicted and experimental tracks on each sequence, and correlation plots over whole chromosome

<figure><img src="image_5.png" /></figure>

## Sequence design methodology

Multi-step mutation selection with model-based energies

<figure><img src="image_6.png" /></figure>

## Experimental validation

Integration of repeated synthetic sequences in yeast, with **nucleosomal repeat length of 167bp** (naturally occurring in yeast), **197bp** and **237bp** (highest known in eukaryotes)

Comparison to the **601 sequence**, designed in vitro to position nucleosomes preferentially

<figure><img src="image_7.png" /></figure>

## Conclusion

We show that a nucleosome model trained on yeast chromatin can **accurately predict nucleosome tracks of bacterial chromosomes** inside the yeast nucleus, even when there are notable differences in GC content.

With a computational approach based on deep learning prediction, we designed and validated **regular nucleosome arrays with tailored nucleosomal repeat length** in yeast, much larger than the natural one.

Our next step is to integrate modelling of **polymerase and cohesin** binding into our designs, to get better control over the yeast chromatin.