Data-driven polymer model for mechanistic exploration of diploid genome organization

Qi, Yuanming; Reyes, Alejandro; Johnstone, Sarah E.; Aryee, Martin J.; Bernstein, B; Zhang, Bin

doi:10.1101/2020.02.27.968735

Cited by 14 publications

(22 citation statements)

References 66 publications

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“…A series of coarse-grained polymer simulation based studies have been performed to characterize non-random organization of the chromosomes using gene activity and random and biological looping constraints [ 50 – 52 ]. In a recent polymer modeling based study, the researchers used Hi-C derived properties and a chromatin state based energy function to study the principles of the spatial as well as radial genome organization [ 53 ].…”

Section: Introductionmentioning

confidence: 99%

Inferring chromosome radial organization from Hi-C data

2020

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Background The nonrandom radial organization of eukaryotic chromosome territories (CTs) inside the nucleus plays an important role in nuclear functional compartmentalization. Increasingly, chromosome conformation capture (Hi-C) based approaches are being used to characterize the genome structure of many cell types and conditions. Computational methods to extract 3D arrangements of CTs from this type of pairwise contact data will thus increase our ability to analyze CT organization in a wider variety of biological situations. Results A number of full-scale polymer models have successfully reconstructed the 3D structure of chromosome territories from Hi-C. To supplement such methods, we explore alternative, direct, and less computationally intensive approaches to capture radial CT organization from Hi-C data. We show that we can infer relative chromosome ordering using PCA on a thresholded inter-chromosomal contact matrix. We simulate an ensemble of possible CT arrangements using a force-directed network layout algorithm and propose an approach to integrate additional chromosome properties into our predictions. Our CT radial organization predictions have a high correlation with microscopy imaging data for various cell nucleus geometries (lymphoblastoid, skin fibroblast, and breast epithelial cells), and we can capture previously documented changes in senescent and progeria cells. Conclusions Our analysis approaches provide rapid and modular approaches to screen for alterations in CT organization across widely available Hi-C data. We demonstrate which stages of the approach can extract meaningful information, and also describe limitations of pairwise contacts alone to predict absolute 3D positions.

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Section: Introductionmentioning

confidence: 99%

Inferring chromosome radial organization from Hi-C data

2020

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“…22,62 At even larger scales such as a whole chromosome or the entire genome, integrative approaches that take into account the impact of the nucleus environment with experimental constraints may provide more faithful structural models for chromatin. 63,64 Several aspects of the near-atomistic model can be further improved for better accuracy. In particular, fine-tuning protein-protein interactions could lead to a more balanced treatment of both ordered and disordered regions of histone proteins.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Stability and folding pathways of tetra-nucleosome from six-dimensional free energy surface

Ding

Lin

Zhang

2021

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The three-dimensional organization of chromatin is expected to play critical roles in regulating genome functions. High-resolution characterization of its structure and dynamics could improve our understanding of gene regulation mechanisms but has remained challenging. Using a near-atomistic model that preserves the chemical specificity of protein-DNA interactions at residue and base-pair resolution, we studied the stability and folding pathways of a tetra-nucleosome. Dynamical simulations performed with an advanced sampling technique uncovered multiple pathways that connect open chromatin configurations with the zigzag crystal structure. Intermediate states along the simulated folding pathways resemble chromatin configurations reported from in situ experiments. We further determined a six-dimensional free energy surface as a function of the inter-nucleosome distances via a deep learning approach. The zigzag structure can indeed be seen as the global minimum of the surface. However, it is not favored by a significant amount relative to the partially unfolded, in situ configurations. Chemical perturbations such as histone H4 tail acetylation and thermal fluctuations can further tilt the energetic balance to stabilize intermediate states. Our study provides insight into the connection between various reported chromatin configurations and has implications on the in situ relevance of the 30nm fiber.

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“…62 Maximum entropy optimization has become widely popular due to its relation to statistical mechanics and information theory. [63][64][65][66][67][68][69] We further introduced an iterative algorithm based on maximum entropy optimization to create a transferable force field capable of reproducing the radius of gyration for various IDPs (MOFF-IDP). 44 As detailed in the SI, the essence of this algorithm is to reparameterize the protein-specific linear bias derived from maximum entropy optimization with a transferable contact potential between pairs of amino acids,…”

Section: Amino Acid Contact Potential From Maximum Entropy Optimizationmentioning

confidence: 99%

Consistent Force Field Captures Homolog Resolved HP1 Phase Separation

Latham

Zhang

2021

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Many proteins have been shown to function via liquid-liquid phase separation. Computational modeling could offer much needed structural details of protein condensates and reveal the set of molecular interactions that dictate their stability. However, the presence of both ordered and disordered domains in these proteins places a high demand on the model accuracy. Here, we present an algorithm to derive a coarse-grained force field, MOFF, that can model both ordered and disordered proteins with consistent accuracy. It combines maximum entropy biasing, least-squares fitting, and basic principles of energy landscape theory to ensure that MOFF recreates experimental radii of gyration while predicting the folded structures for globular proteins with lower energy. The theta temperature determined from MOFF separates ordered and disordered proteins at 300 K and exhibits a strikingly linear relationship with amino acid sequence composition. We further applied MOFF to study the phase behavior of HP1, an essential protein for posttranslational modification and spatial organization of chromatin. The force field successfully resolved the structural difference of two HP1 homologs, despite their high sequence similarity. We carried out large scale simulations with hundreds of proteins to determine the critical temperature of phase separation and uncover multivalent interactions that stabilize higher-order assemblies. In all, our work makes significant methodological strides to connect theories of ordered and disordered proteins and provides a powerful tool for studying liquid-liquid phase separation with near-atomistic details.

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Data-driven polymer model for mechanistic exploration of diploid genome organization

Cited by 14 publications

References 66 publications

Inferring chromosome radial organization from Hi-C data

Inferring chromosome radial organization from Hi-C data

Stability and folding pathways of tetra-nucleosome from six-dimensional free energy surface

Consistent Force Field Captures Homolog Resolved HP1 Phase Separation

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