Micropolarity governs the structural organization of biomolecular condensates.

Membraneless organelles within cells have unique microenvironments that play a critical role in their functions. However, how microenvironments of biomolecular condensates affect their structure and function remains unknown. In this study, we investigated the micropolarity and microviscosity of model biomolecular condensates by fluorescence lifetime imaging coupling with environmentally sensitive fluorophores. Using both in vitro and in cellulo systems, we demonstrated that sufficient micropolarity difference is key to forming multilayered condensates, where the shells present more polar microenvironments than the cores. Furthermore, micropolarity changes were shown to be accompanied by conversions of the layered structures. Decreased micropolarities of the granular components, accompanied by the increased micropolarities of the dense fibrillar components, result in the relocation of different nucleolus subcompartments in transcription-stalled conditions. Our results demonstrate the central role of the previously overlooked micropolarity in the regulation of structures and functions of membraneless organelles.

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

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 performance on a single GPU that rivals 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.

On the stability and layered organization of protein-DNA condensates.

Multi-component phase separation is emerging as a key mechanism for the formation of biological condensates that play essential roles in signal sensing and transcriptional regulation. The molecular factors that dictate these condensates' stability and spatial organization are not fully understood, and it remains challenging to predict their microstructures. Using a near-atomistic, chemically accurate force field, we studied the phase behavior of chromatin regulators that are crucial for heterochromatin organization and their interactions with DNA. Our computed phase diagrams recapitulated previous experimental findings on different proteins. They revealed a strong dependence of condensate stability on the protein-DNA mixing ratio as a result of balancing protein-protein interactions and charge neutralization. Notably, a layered organization was observed in condensates formed by mixing HP1, histone H1, and DNA. This layered organization may be of biological relevance, as it enables cooperative DNA packaging between the two chromatin regulators: histone H1 softens the DNA to facilitate the compaction induced by HP1 droplets. Our study supports near-atomistic models as a valuable tool for characterizing the structure and stability of biological condensates.

Multiscale modeling of genome organization with maximum entropy optimization.

Three-dimensional (3D) organization of the human genome plays an essential role in all DNA-templated processes, including gene transcription, gene regulation, and DNA replication. Computational modeling can be an effective way of building high-resolution genome structures and improving our understanding of these molecular processes. However, it faces significant challenges as the human genome consists of over 6 × 109 base pairs, a system size that exceeds the capacity of traditional modeling approaches. In this perspective, we review the progress that has been made in modeling the human genome. Coarse-grained models parameterized to reproduce experimental data via the maximum entropy optimization algorithm serve as effective means to study genome organization at various length scales. They have provided insight into the principles of whole-genome organization and enabled de novo predictions of chromosome structures from epigenetic modifications. Applications of these models at a near-atomistic resolution further revealed physicochemical interactions that drive the phase separation of disordered proteins and dictate chromatin stability in situ. We conclude with an outlook on the opportunities and challenges in studying chromosome dynamics.

Structure and Dynamics of N-Glycosylated Human Ribonuclease 1.

Glycosylation is a common modification that can endow proteins with altered physical and biological properties. Ribonuclease 1 (RNase 1), which is the human homologue of the archetypal enzyme RNase A, undergoes N-linked glycosylation at asparagine residues 34, 76, and 88. We have produced the three individual glycoforms that display the core heptasaccharide, Man5GlcNAc2, and analyzed the structure of each glycoform by using small-angle X-ray scattering along with molecular dynamics simulations. The glycan on Asn34 is relatively compact and rigid, donates hydrogen bonds that "cap" the carbonyl groups at the C-terminus of an α-helix, and enhances protein thermostability. In contrast, the glycan on Asn88 is flexible and can even enter the enzymic active site, hindering catalysis. The N-glycosylation of Asn76 has less pronounced consequences. These data highlight the diverse behaviors of Man5GlcNAc2 pendants and provide a structural underpinning to the functional consequences of protein glycosylation.

Microphase Separation Produces Interfacial Environment within Diblock Biomolecular Condensates.

The phase separation of intrinsically disordered proteins is emerging as an important mechanism for cellular organization. However, efforts to connect protein sequences to the physical properties of condensates, i.e., the molecular grammar, are hampered by a lack of effective approaches for probing high-resolution structural details. Using a combination of multiscale simulations and fluorescence lifetime imaging microscopy experiments, we systematically explored a series of systems consisting of diblock elastin-like polypeptides (ELP). The simulations succeeded in reproducing the variation of condensate stability upon amino acid substitution and revealed different microenvironments within a single condensate, which we verified with environmentally sensitive fluorophores. The interspersion of hydrophilic and hydrophobic residues and a lack of secondary structure formation result in an interfacial environment, which explains both the strong correlation between ELP condensate stability and interfacial hydrophobicity scales, as well as the prevalence of protein-water hydrogen bonds. Our study uncovers new mechanisms for condensate stability and organization that may be broadly applicable.

Utilizing Molecular Simulations to Examine Nanosuspension Stability.

Drug nanosuspensions offer a promising approach to improve bioavailability for poorly soluble drug candidates. Such formulations often necessitate the inclusion of an excipient to stabilize the drug nanoparticles. However, the rationale for the choice of the correct excipient for a given drug candidate remains unclear. To gain molecular insight into formulation design, this work first utilizes a molecular dynamics simulation to computationally investigate drug-excipient interactions for a number of combinations that have been previously studied experimentally. We find that hydrophobic interactions drive excipient adsorption to drug nanoparticles and that the fraction of polar surface area serves as a predictor for experimental measurements of nanosuspension stability. To test these ideas prospectively, we applied our model to an uncharacterized drug compound, GDC-0810. Our simulations predicted that a salt form of GDC-0810 would lead to more stable nanosuspensions than the neutral form; therefore, we tested the stability of salt GDC-0810 nanosuspensions and found that the salt form readily formed nanosuspensions even without the excipient. To avoid computationally expensive simulations in the future, we extended our model by showing that simple, two-dimensional properties of single drug molecules can be used to rationalize nanosuspension designs without simulations. In all, our work demonstrates how computational tools can provide molecular insight into drug-excipient interactions and aid in rational formulation design.

Micropolarity governs the structural organization of biomolecular condensates.

Microenvironment is critical to the function of cells and organisms. One example is provided by biomolecular condensates, whose microenvironment can be vastly different from the surrounding cellular environments to engage unique biological functions. How microenvironments of biomolecular condensates affect their structure and function remains unknown. Here, we show that the arrangements and partitioning of biomolecules are dictated by the differences between the micropolarity of each subcompartment. Sufficient difference in micropolarity results in layered structures with the exterior shell presenting a more polar microenvironment than the interior core. Accordingly, micropolarity inversion is accompanied by conversions of the layered structures. These findings demonstrated the central role of the previously overlooked microenvironment in regulating the structural organization and function of membraneless organelles.

Unifying coarse-grained force fields for folded and disordered proteins.

Liquid-liquid phase separation drives the formation of biological condensates that play essential roles in transcriptional regulation and signal sensing. Computational modeling could provide high-resolution structural characterizations of these condensates and help uncover physicochemical interactions that dictate their stability. However, many protein molecules involved in phase separation often contain multiple ordered domains connected with flexible, structureless linkers. Simulating such proteins necessitates force fields with consistent accuracy for both folded and disordered proteins. We provide a critical review of existing coarse-grained force fields for disordered proteins and highlight the challenges in their application to folded proteins. After discussing existing algorithms for force field parameterization, we propose an optimization strategy that should lead to computer models with improved transferability across protein types.

Molecular Determinants for the Layering and Coarsening of Biological Condensates.

Many membraneless organelles, or biological condensates, form through phase separation, and play key roles in signal sensing and transcriptional regulation. While the functional importance of these condensates has inspired many studies to characterize their stability and spatial organization, the underlying principles that dictate these emergent properties are still being uncovered. In this review, we examine recent work on biological condensates, especially multicomponent systems. We focus on connecting molecular factors such as binding energy, valency, and stoichiometry with the interfacial tension, explaining the nontrivial interior organization in many condensates. We further discuss mechanisms that arrest condensate coalescence by lowering the surface tension or introducing kinetic barriers to stabilize the multidroplet state.

Single-stranded nucleic acid binding and coacervation by linker histone H1.

The H1 linker histone family is the most abundant group of eukaryotic chromatin-binding proteins. However, their contribution to chromosome structure and function remains incompletely understood. Here we use single-molecule fluorescence and force microscopy to directly visualize the behavior of H1 on various nucleic acid and nucleosome substrates. We observe that H1 coalesces around single-stranded DNA generated from tension-induced DNA duplex melting. Using a droplet fusion assay controlled by optical tweezers, we find that single-stranded nucleic acids mediate the formation of gel-like H1 droplets, whereas H1-double-stranded DNA and H1-nucleosome droplets are more liquid-like. Molecular dynamics simulations reveal that multivalent and transient engagement of H1 with unpaired DNA strands drives their enhanced phase separation. Using eGFP-tagged H1, we demonstrate that inducing single-stranded DNA accumulation in cells causes an increase in H1 puncta that are able to fuse. We further show that H1 and Replication Protein A occupy separate nuclear regions, but that H1 colocalizes with the replication factor Proliferating Cell Nuclear Antigen, particularly after DNA damage. Overall, our results provide a refined perspective on the diverse roles of H1 in genome organization and maintenance, and indicate its involvement at stalled replication forks.

Consistent Force Field Captures Homologue-Resolved HP1 Phase Separation.

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, which 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 post-translational modification and spatial organization of chromatin. The force field successfully resolved the structural difference of two HP1 homologues 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.

Maximum Entropy Optimized Force Field for Intrinsically Disordered Proteins.

Intrinsically disordered proteins (IDPs) constitute a significant fraction of eukaryotic proteomes. High-resolution characterization of IDP conformational ensembles can help elucidate their roles in a wide range of biological processes but remains challenging both experimentally and computationally. Here, we present a generic algorithm to improve the accuracy of coarse-grained IDP models using a diverse set of experimental measurements. It combines maximum entropy optimization and least-squares regression to systematically adjust model parameters and improve the agreement between simulation and experiment. We successfully applied the algorithm to derive a transferable force field, which we term the maximum entropy optimized force field (MOFF), for de novo prediction of IDP structures. Statistical analysis of force field parameters reveals features of amino acid interactions not captured by potentials designed to work well for folded proteins. We anticipate its combination of efficiency and accuracy will make MOFF useful for studying the phase separation of IDPs, which drives the formation of various biological compartments.

Phosphorylation-Dependent Conformations of the Disordered Carboxyl-Terminus Domain in the Epidermal Growth Factor Receptor.

The epidermal growth factor receptor (EGFR), a receptor tyrosine kinase, regulates basic cellular functions and is a major target for anticancer therapeutics. The carboxyl-terminus domain is a disordered region of EGFR that contains the tyrosine residues, which undergo autophosphorylation followed by docking of signaling proteins. Local phosphorylation-dependent secondary structure has been identified and is thought to be associated with the signaling cascade. Deciphering and distinguishing the overall conformations, however, have been challenging because of the disordered nature of the carboxyl-terminus domain and resultant lack of well-defined three-dimensional structure for most of the domain. We investigated the overall conformational states of the isolated EGFR carboxyl-terminus domain using single-molecule Förster resonance energy transfer and coarse-grained simulations. Our results suggest that electrostatic interactions between charged residues emerge within the disordered domain upon phosphorylation, producing a looplike conformation. This conformation may enable binding of downstream signaling proteins and potentially reflect a general mechanism in which electrostatics transiently generate functional architectures in disordered regions of a well-folded protein.

Improving Coarse-Grained Protein Force Fields with Small-Angle X-ray Scattering Data.

Small-angle X-ray scattering (SAXS) experiments provide valuable structural data for biomolecules in solution. We develop a highly efficient maximum entropy approach to fit SAXS data by introducing minimal biases to a coarse-grained protein force field, the associative memory, water mediated, structure, and energy model (AWSEM). We demonstrate that the resulting force field, AWSEM-SAXS, succeeds in reproducing scattering profiles and models protein structures with shapes that are in much better agreement with experimental results. Quantitative metrics further reveal a modest, but consistent, improvement in the accuracy of modeled structures when SAXS data are incorporated into the force field. Additionally, when applied to a multiconformational protein, we find that AWSEM-SAXS is able to recover the population of different protein conformations from SAXS data alone. We, therefore, conclude that the maximum entropy approach is effective in fine-tuning the force field to better characterize both protein structure and conformational fluctuation.