Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning

Abstract

Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction ([≥]1 kbp) are challenging to address experimentally or by all-atom molecular dynamics simulations, making coarse-grained simulations a natural approach. Here we present a simple and generic coarse-grained model for the DNA-protein and protein-protein interactions, and investigate the role of the latter in the protein-induced compaction of DNA. Our approach models the DNA as a discrete worm-like chain. The proteins are treated in the grand-canonical ensemble and the protein-DNA binding strength is taken from experimental measurements. Protein-DNA interactions are modeled as an isotropic binding potential with an imposed binding valency, without specific assumptions about the binding geometry. To systematically and quantitatively classify DNA-protein complexes, we present an unsupervised machine learning pipeline that receives a large set of structural order parameters as input, reduces the dimensionality via principal component analysis, and groups the results using a Gaussian mixture model. We apply our method to recent data on the compaction of viral genome-length DNA by HIV integrase and we find that protein-protein interactions are critical to the formation of looped intermediate structures seen experimentally. Our methodology is broadly applicable to DNA-binding proteins and to protein-induced DNA compaction and provides a systematic and quantitative approach for analyzing their mesoscale complexes. SIGNIFICANCEDNA is central to the storage and transmission of genetic information and is frequently compacted and condensed by interactions with proteins. Their size and dynamic nature make the resulting complexes difficult to probe experimentally and by all-atom simulations. We present a simple coarse-grained model to explore [~]kbp DNA interacting with proteins of defined valency and concentration. Our analysis uses unsupervised learning to define conformational states of the DNA-protein complexes and pathways between them. We apply our simulations and analysis to the compaction of viral genome-length DNA by HIV integrase. We find that protein-protein interactions are critical to account for the experimentally observed intermediates and our simulated complexes are in good agreement with experimental observations.