OpenABC enables flexible, simplified, and efficient GPU accelerated simulations of biomolecular condensates

Design and Characterization of DNA-Driven Condensates: Regulating Topology, Mechanical Properties, and Immunorecognition

Cells maintain spatiotemporal control over biochemical processes through the formation and dissolution of biomolecular condensates, dynamic membraneless organelles formed via liquid-liquid phase separation. Composed primarily of proteins and nucleic acids, these condensates regulate key cellular functions, and their properties are influenced by the concentration and type of molecules involved. The structural versatility challenges the de novo design and assembly of condensates with predefined properties. Through feedback between computational and experimental approaches, we introduce a modular system for assembling condensates using nucleic acid nanotechnology. By utilizing programmable oligonucleotides and orthogonal synthesis methods, we control the structural parameters, responsive behavior, and immunorecognition of the products. Dissipative particle dynamics simulations predict some conditions to produce larger, well-defined condensates with compact, globular cores, while others result in smaller, more diffuse analogs. Fluorescence microscopy confirms these findings and microrheology demonstrates the viscoelastic adaptability of tested condensates. Nucleases trigger disruption of structures, and ethidium bromide intercalation protects condensates from digestion. Immunostimulatory assays suggest condensate-specific activation of the IRF pathway via cGAS-STING signaling. This study provides a framework for developing biomolecular condensates with customizable properties and immunorecognition for various biological applications.

Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates

Phase separation is a fundamental process that enables cellular organization by forming biomolecular condensates. These assemblies regulate diverse functions by creating distinct environments, influencing reaction kinetics, and facilitating processes such as genome organization, signal transduction, and RNA metabolism. Recent studies highlight the complexity of condensate properties, shaped by intrinsic molecular features and external factors such as temperature and pH. Molecular simulations serve as an effective approach to establishing a comprehensive framework for analyzing these influences, offering high-resolution insights into condensate stability, dynamics, and material properties. This review evaluates recent advancements in biomolecular condensate simulations, with a particular focus on coarse-grained 1-bead-per-amino-acid (1BPA) protein models, and emphasizes OpenABC, a tool designed to simplify and streamline condensate simulations. OpenABC supports the implementation of various coarse-grained force fields, enabling their performance evaluation. Our benchmarking identifies inconsistencies in phase behavior predictions across force fields, even though these models accurately capture single-chain statistics. This finding underscores the need for enhanced force field accuracy, achievable through enriched training data sets, many-body potentials, and advanced optimization techniques. Such refinements could significantly improve the predictive capacity of coarse-grained models, bridging molecular details with emergent condensate behaviors.

Chemically Informed Coarse-Graining of Electrostatic Forces in Charge-Rich Biomolecular Condensates

Biomolecular condensates composed of highly charged biomolecules, such as DNA, RNA, chromatin, and nucleic-acid binding proteins, are ubiquitous in the cell nucleus. The biophysical properties of these charge-rich condensates are largely regulated by electrostatic interactions. Residue-resolution coarse-grained models that describe solvent and ions implicitly are widely used to gain mechanistic insights into the biophysical properties of condensates, offering transferability, computational efficiency, and accurate predictions for multiple systems. However, their predictive accuracy diminishes for charge-rich condensates due to the implicit treatment of solvent and ions. Here, we present Mpipi-Recharged, a residue-resolution coarse-grained model that improves the description of charge effects in biomolecular condensates containing disordered proteins, multidomain proteins, and/or disordered single-stranded RNAs. Mpipi-Recharged introduces a pair-specific asymmetric Yukawa electrostatic potential, informed by atomistic simulations. We show that this asymmetric coarse-graining of electrostatic forces captures intricate effects, such as charge blockiness, stoichiometry variations in complex coacervates, and modulation of salt concentration, without requiring explicit solvation. Mpipi-Recharged provides excellent agreement with experiments in predicting the phase behavior of highly charged condensates. Overall, Mpipi-Recharged improves the computational tools available to investigate the physicochemical mechanisms regulating biomolecular condensates, enhancing the scope of computer simulations in this field.

Molecular condensation of the CO/NF-YB/NF-YC/FT complex gates floral transition in Arabidopsis

The plant master photoperiodic regulator CONSTANS (CO) interacts with Nuclear Factor-Y subunits B2 (NF-YB2) and C9 (NF-YC9) and transcriptionally activates the florigen gene FLOWERING LOCUS T (FT), regulating floral transition. However, the molecular mechanism of the functional four-component complex assembly in the nucleus remains elusive. We report that co-phase separation of CO with NF-YB2/NF-YC9/FT precisely controls heterogeneous CO assembly and FT transcriptional activation. In response to light signals, CO proteins form functional percolation clusters from a diffuse distribution in a B-box-motif-dependent manner. Multivalent coassembly with NF-YC9 and NF-YB2 prevents inhibitory condensate formation and is necessary to maintain proper CO assembly and material properties. The intrinsically disordered region (IDR) of NF-YC9, containing a polyglutamine motif, fine-tunes the functional properties of CO/NF-YB/NF-YC condensates. Specific FT promoter recognition with polyelectrolyte partitioning also enables the fluidic functional properties of CO/NF-YB/NF-YC/FT condensates. Our findings offer novel insights into the tunable macromolecular condensation of the CO/NF-YB/NF-YC/FT complex in controlling flowering in the photoperiod control.

Cellular Function of a Biomolecular Condensate Is Determined by Its Ultrastructure

Biomolecular condensates play key roles in the spatiotemporal regulation of cellular processes. Yet, the relationship between atomic features and condensate function remains poorly understood. We studied this relationship using the polar organizing protein Z (PopZ) as a model system, revealing how its material properties and cellular function depend on its ultrastructure. We revealed PopZ's hierarchical assembly into a filamentous condensate by integrating cryo-electron tomography, biochemistry, single-molecule techniques, and molecular dynamics simulations. The helical domain drives filamentation and condensation, while the disordered domain inhibits them. Phase-dependent conformational changes prevent interfilament contacts in the dilute phase and expose client binding sites in the dense phase. These findings establish a multiscale framework that links molecular interactions and condensate ultrastructure to macroscopic material properties that drive cellular function.

Nonspecific Yet Selective Interactions Contribute to Small Molecule Condensate Binding

Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to underlie disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by nonspecific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity toward specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.

Nucleosome condensate and linker DNA alter chromatin folding pathways and rates

Chromatin organization is essential for DNA packaging and gene regulation in eukaryotic genomes. While significant progresses have been made, the exact atomistic arrangement of nucleosomes remains controversial. Using a well-calibrated residue-level coarse-grained model and advanced dynamics modeling techniques, particularly the non-Markovian dynamics model, we map the free energy landscape of tetra-nucleosome systems, identify both metastable conformations and intermediate states in folding pathways, and quantify the folding kinetics. Our findings show that chromatin with 10 n base pairs (bp) DNA linker lengths favor zigzag fibril structures. However, longer linker lengths destabilize this conformation. When the linker length is 10 n + 5 bp , chromatin loses unique conformations, favoring a dynamic ensemble of structures resembling folding intermediates. Embedding the tetra-nucleosome in a nucleosome condensate similarly shifts stability towards folding intermediates as a result of the competition of inter-nucleosomal contacts. These results suggest that chromatin organization observed in vivo arises from the unfolding of fibril structures due to nucleosome crowding and linker length variation. This perspective aids in unifying experimental studies to develop atomistic models for chromatin.

Significance

Atomic structures of chromatin have become increasingly accessible, largely through cryo-EM techniques. Nonetheless, these approaches often face limitations in addressing how intrinsic in vivo factors influence chromatin organization. We present a structural characterization of chromatin under the combined effects of nucleosome condensate crowding and linker DNA length variation-two critical in vivo features that have remained challenging to capture experimentally. This work leverages a novel application of non-Markovian dynamical modeling, providing accurate mapping of chromatin folding kinetics and pathways. Our findings support a hypothesis that in vivo chromatin organization arises from folding intermediates advancing toward a stable fibril configuration, potentially resolving longstanding questions surrounding chromatin atomic structure.

Non-specific yet selective interactions contribute to small molecule condensate partitioning behavior

Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to be underly disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by non-specific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity towards specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.

Fundamental Aspects of Phase-Separated Biomolecular Condensates

Biomolecular condensates, formed through phase separation, are upending our understanding in much of molecular, cell, and developmental biology. There is an urgent need to elucidate the physicochemical foundations of the behaviors and properties of biomolecular condensates. Here we aim to fill this need by writing a comprehensive, critical, and accessible review on the fundamental aspects of phase-separated biomolecular condensates. We introduce the relevant theoretical background, present the theoretical basis for the computation and experimental measurement of condensate properties, and give mechanistic interpretations of condensate behaviors and properties in terms of interactions at the molecular and residue levels.

Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks

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.