Chromatin fiber breaks into clutches under tension and crowding

Liu, Shuming; Lin, Xin; Zhang, Bin

doi:10.1093/nar/gkac725

Cited by 18 publications

(23 citation statements)

References 109 publications

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“…In a prior paper, we found that many in vivo factors, most notably crowding, could disrupt fibril configurations in favor of inter-chain contacts. 76 The inter-chain contacts can indeed be driven by favorable inter-nucleosome interactions.…”

Section: Discussionmentioning

confidence: 99%

“…The large system size of chromatin and the slow timescale for its conformational relaxation necessitates coarse-grained modeling. Following previous studies, 5,31,75,76,92 we adopted a residue-level coarse-grained model for efficient simulations of chromatin. The structure-based model 93,94 was applied to represent the histone proteins with one bead per amino acid and to preserve the tertiary structure of the folded regions.…”

Section: Methodsmentioning

confidence: 99%

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Explicit Ion Modeling Predicts Physicochemical Interactions for Chromatin Organization

Lin

Zhang

2023

Preprint

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Molecular mechanisms that dictate chromatin organizationin vivoare under active investigation, and the extent to which intrinsic interactions contribute to this process remains debatable. A central quantity for evaluating their contribution is the strength of nucleosome-nucleosome binding, which previous experiments have estimated to range from 2 to 14kBT. We introduce an explicit ion model to dramatically enhance the accuracy of residue-level coarse-grained modeling approaches across a wide range of ionic concentrations. This model allows forde novopredictions of chromatin organization and remains computationally efficient, enabling large-scale conformational sampling for free energy calculations. It reproduces the energetics of protein-DNA binding and unwinding of single nucleosomal DNA, and resolves the differential impact of mono and divalent ions on chromatin conformations. Moreover, we showed that the model can reconcile various experiments on quantifying nucleosomal interactions, providing an explanation for the large discrepancy between existing estimations. We predict the interaction strength at physiological conditions to be 9kBT, a value that is nonetheless sensitive to DNA linker length and the presence of linker histones. Our study strongly supports the contribution of physicochemical interactions to the phase behavior of chromatin aggregates and chromatin organization inside the nucleus.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Explicit Ion Modeling Predicts Physicochemical Interactions for Chromatin Organization

Lin

Zhang

2023

Preprint

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“…We modeled protein-DNA interactions with both excluded volume effects and electrostatic interactions. Previous studies have shown that these treatments successfully quantify the stability of various protein-DNA complexes, 37,41,[79][80][81][82][83][84] revealing the complex phase behavior of protein-DNA condensates. 47,85 Detailed expressions of the energy functions for all 8 the force fields are provided in the Supporting Information Section: Force Field Definitions, with the parameters provided in Tables S1-S6.…”

Section: Flexible Biomolecular Simulations With Diverse Force Fieldsmentioning

confidence: 99%

OpenABC Enables Flexible, Simplified, and Efficient GPU Accelerated Simulations of Biomolecular Condensates

Liu

Latham

Ding

et al. 2023

Preprint

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Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performances on a single GPU that rival the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that OpenABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates. OpenABC is available at https://github.com/ZhangGroup-MITChemistry/OpenABC

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“…This approach is crucial for studying large and complex biomolecular systems over biologically relevant timescales, which would be infeasible with atomistic simulations. CG force fields enable researchers to explore phenomena such as protein folding, − protein aggregation, − ,− and large-scale conformational changes, , providing insights into biological processes that are otherwise inaccessible. Understandably, there is great interest in developing methodologies for building accurate and transferable CG force fields. ,− …”

Section: Introductionmentioning

confidence: 99%

Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks

Airas,

Ding,

Zhang

2023

ACS Cent. Sci.

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Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective.

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Chromatin fiber breaks into clutches under tension and crowding

Cited by 18 publications

References 109 publications

Explicit Ion Modeling Predicts Physicochemical Interactions for Chromatin Organization

Explicit Ion Modeling Predicts Physicochemical Interactions for Chromatin Organization

OpenABC Enables Flexible, Simplified, and Efficient GPU Accelerated Simulations of Biomolecular Condensates

Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks

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