Looping and Clustering model for the organization of protein-DNA complexes on the bacterial genome

• Published in: arXiv.org 2017-12
• Main Authors: Jean-Charles, Walter, Nils-Ole Walliser, Gabriel, David, Dorignac, Jérôme, Geniet, Frédéric, Palmeri, John, Parmeggiani, Andrea, Wingreen, Ned S, Broedersz, Chase P
• Format: Article
• Language: English
• Subjects: Bacteria; Binding sites; Biological activity; Clustering; Deoxyribonucleic acid; DNA; Extrusion; Genomes; Proteins; Tightness
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1707.01373

The bacterial genome is organized in a structure called the nucleoid by a variety of associated proteins. These proteins can form complexes on DNA that play a central role in various biological processes, including chromosome segregation. A prominent example is the large ParB-DNA complex, which forms an essential component of the segregation machinery in many bacteria. ChIP-Seq experiments show that ParB proteins localize around centromere-like parS sites on the DNA to which ParB binds specifically, and spreads from there over large sections of the chromosome. Recent theoretical and experimental studies suggest that DNA-bound ParB proteins can interact with each other to condense into a coherent 3D complex on the DNA. However, the structural organization of this protein-DNA complex remains unclear, and a predictive quantitative theory for the distribution of ParB proteins on DNA is lacking. Here, we propose the Looping and Clustering (LC) model, which employs a statistical physics approach to describe protein-DNA complexes. The LC model accounts for the extrusion of DNA loops from a cluster of interacting DNA-bound proteins. Conceptually, the structure of the protein-DNA complex is determined by a competition between attractive protein interactions and the configurational and loop entropy of this protein-DNA cluster. Indeed, we show that the protein interaction strength determines the "tightness" of the loopy protein-DNA complex. With this approach we consider the genomic organization of such a protein-DNA cluster around a single high-affinity binding site. Thus, our model provides a theoretical framework to quantitatively compute the binding profiles of ParB-like proteins around a cognate (parS) binding site.