Biology subjects

Potoyan, D. A.

Publications and source records attributed to Potoyan, D. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Temperature-responsive contacts in TRPV1 concentrate in the cytoplasmic coupling domains

TRPV1, the heat-activated cation channel of pain-sensing neurons, is a major target for new analgesics, yet where it senses heat remains unresolved. Candidate sensors have been proposed in nearly every part of its structure, from the ankyrin repeats to the pore. Here we ask where the contact network of the closed channel responds to temperature. We simulated human TRPV1 in a multicomponent membrane at six temperatures spanning its activation threshold and three salt concentrations, at atomistic and coarse-grained resolution. We decomposed the contacts that recur across its four identical subunits into collective thermal modes and tested each mode against temperature and, as a control, salinity. Only the leading atomistic mode follows temperature, which explains 59% of its variance and no mode follows salinity at either resolution. The temperature mode places 80% of its variance in the cytoplasmic domains, against 63% of all contact variance, and is most enriched in the linker domain that joins the ankyrin repeats to the transmembrane domain. On heating, the linker releases the C-terminal domain and binds the ankyrin repeats more tightly. Lipid contacts cluster along the transmembrane and TRP domains, and the few that reach the ankyrin repeats and the linker are mostly with the anionic inner-leaflet phosphatidylserine. The coarse-grained model reproduces the atomistic fluctuations and lipid organization, but the temperature mode emerges only at atomistic resolution. Its localization matches that of the dominant temperature mode of the related channel TRPV3. These results narrow the search for the heat sensor. In both channels, the contact network responds to temperature not in the pore but in the cytoplasmic coupling region where the ankyrin repeats, the linker, and the C-terminal domain meet.

biophysics↗

bioRxiv2026

Coevolution-informed Bayesian optimization for sample-efficient protein design

Protein engineering is limited less by generating variants than by the cost of evaluating them, so designing under a tight budget demands sequence features that let a model learn fitness from very few examples. We introduce ALSEBO (Active Learning Sequence Exploration via Bayesian Optimization), which couples a generative latent sequence landscape to Bayesian optimization and featurizes candidates with direct-coupling-analysis (DCA) coevolutionary statistics. This representation carries a specific inductive bias: it places the dominant organizer of the fitness landscape along a single linear coordinate, producing a smooth, funnel-like objective that a low-data surrogate navigates efficiently. On a virtual avGFP fluorescence benchmark, ALSEBO reaches the optimum in [~]40 evaluations and outpaces protein-language-model embeddings and raw latent coordinates; controls with representation-neutral oracles confirm that the advantage is intrinsic, not an artifact of the benchmark. Molecular dynamics of the optimized variant recovers structural hallmarks of fluorescence, and ALSEBO transfers to divergent GFP orthologs and to a non-GFP enzyme, establishing a data-efficient route to protein design.

biophysics↗

bioRxiv2026

Synthetic Biomolecular Condensates as Tunable Microtubule Assembly Hubs

Phase separation of proteins and nucleic acids (NAs) into nano-to-microscale condensates can regulate biochemical processes, including assembly and organization of cytoskeletal networks such as actin and microtubules. This study examines the functional role of condensate material properties in microtubule assembly. Learning from the sequence grammar of naturally occurring intrinsically disordered regions in microtubule-associated proteins, two-component peptide-NA condensates with programmable material properties were designed. These synthetic condensates catalyze tubulin polymerization into microtubule filaments with tunable outcomes. Tubulin preferentially partitions to the condensate interface and nucleates microtubule assembly. Enhanced tubulin self-assembly produces long filaments that exhibit branching and bundling. Using a minimal stochastic chemo-mechanical model, we show that sequence-encoded condensate viscoelasticity is a tunable element that controls filament morphologies and identifies interfacial rheology as the key regulator of filament growth. Fluorescence recovery after photobleaching experiments support this model, revealing a direct correlation between interfacial tubulin mobility and condensate-directed microtubule assembly. Distinct regimes emerge due to competition between bulk adsorption and lateral diffusion of tubulin at the condensate interface, which determines whether filament tips grow or stall. Since dynamic microtubule assembly and restructuring are essential for various cellular functions, this work highlights a critical role of condensate interfacial rheology in cytoskeletal organization.

biophysics↗

bioRxiv2026

β-motifs and molecular flux promote amyloid nucleation at condensate interfaces

Biomolecular condensates are increasingly implicated as intermediates in the formation of pathological amyloid assemblies, yet the mechanisms by which sequence-encoded structural motifs and non-equilibrium molecular transport cooperate at condensate interfaces remain incompletely understood. Here, we introduce Flux-Driven Molecular Dynamics (FD-MD), a simulation framework that combines sequence-encoded {beta}-prone interactions with sustained molecular influx to examine fibril formation at condensate interfaces. Within this framework, we establish three main results. First, a scaling analysis of orientational entropy suggests that condensate interfaces can enhance nucleation relative to the bulk by as much as two orders of magnitude, by reducing the entropic cost of coalignment of rigid {beta}-prone segments. Second, varying segment rigidity and molecular supply rate organizes a non-equilibrium phase diagram with four interfacial growth morphologies, ranging from uniform wetting to fibrillar protrusions and inter-condensate bridging networks. Third, directional fibril elongation displays an inverse relationship with drift velocity, consistent with a mechanism in which higher transport rates to the interface favor planar saturation over directed tip incorporation. Together, these results support a picture in which condensate interfaces can act as kinetically favorable nucleation environments, sequence-encoded rigidity helps determine whether interfaces remain liquid-like or become fibrillar, and molecular flux emerges as an additional control axis in the model for condensate aging trajectories.

biophysics↗

bioRxiv2026

A molecular grammar for programmable multiphase protein-RNA vesicles

Protein-RNA phase separation gives rise to biomolecular condensates with rich internal organization, yet the molecular rules that connect sequence-encoded interactions and composition to the emergent architecture of these condensates remain poorly defined. Here, using large-scale residue-level coarse-grained simulations, we identify a molecular grammar that governs the formation and stability of multiphase protein-RNA condensates. We show that asymmetries in protein-protein and protein-RNA interactions, together with protein stoichiometry, chain length, and condensate density, collectively determine whether condensates adopt homogeneous, layered, biphasic, or vesicle-like morphologies. Across a broad parameter space, these rules yield hollow multiphase vesicles with dense shells surrounding dilute interiors. Remarkably, vesicular condensates form spontaneously from well-mixed initial conditions, without requiring flux-driven oversaturation or extreme charge imbalance, distinguishing this mechanism from previously proposed routes to condensate hollowing. Our results establish minimal and general design principles for programming internal condensate architecture solely through sequence and composition, and provide a framework for engineering membrane-free vesicles and multilayered condensates with tunable permeability, encapsulation, and responsiveness.

biophysics↗

bioRxiv2026

3D genome architecture regulates the traffic of transcription factors throughout human chromosomes

Transcription factors (TFs) control the rate of transcription of genetic information by binding to specific DNA sequences. The time needed for a TF to find its specific target sites is a bottleneck to the genetic response mechanism. While TF target site search is a well-studied problem, the effect of genome 3D architecture on the TF target search times is poorly understood. Here, we use accurate and cell-specific 3D structural ensembles of human chromosomes to investigate how the spatial organization of binding sites on chromosomes influences the dynamics of TFs. We use Chromatin Immuno-Precipitation data to map the position of binding sites for several TFs on chromosomal structures and simulate the dynamics of individual TF within chromosomal territories. We find that the distribution of binding sites along chromosomes cooperates with the 3D folding of the chromatin fiber to induce dynamics in which TFs tend to visit sites distributed sequentially along the genome. In this way, genome 3D architecture appears to reduce the time each TF spends in the unbound state while commuting from one target site to the other. At the same time, genome 3D architecture further reduces the flux of TFs between binding sites already well separated along the genome, effectively isolating distant clusters of binding sites. We compare the TF traffic patterns generated by the 3D structures of human chromosomes with those generated by several alternative structural models characterized by increasing randomness. Finally, we study the effect of lengthwise compaction and phase separation, known architectural features of the human genome, in TFs target search. In short, our analysis demonstrates that genome architecture regulates the traffic of TFs within chromosomal territories and reduces the time each TF spends commuting between binding sites.

biophysics↗

bioRxiv2025

A Structure-Aware Generative Framework for Exploring Protein Sequence and Function Space

The rapid expansion of protein sequence databases has far outpaced experimental structure determination, leaving many unannotated sequences, particularly the more remote homologs with low sequence identity. Because protein folds are more conserved and functionally informative than sequences alone, structural information offers a powerful lens for analysis. Here, we introduce a generative, structure-aware framework that integrates geometric encoding and coevolutionary constraints to map, cluster, and design protein sequences. Our approach employs the 3D interaction (3Di) alphabet to convert local residue geometries into compact, 20-state discrete representations. Using ProstT5, we enable bidirectional translation between amino acid sequences and 3Di representations, facilitating sensitive homology detection and structure-guided sequence generation. We then augment the latent generative landscape methodology by combining 3Di-based alignments with direct coupling analysis (DCA) and variational autoencoders (VAE), imbuing tasks such as clustering, annotation, and design with structural information. This integrative framework enhances the detection of coevolutionary signals and enables rational sampling of structural variants, even without functional labels. We demonstrate the utility of our method across diverse protein families, including globins, kinases, and malate dehydrogenases, achieving improved contact prediction, homology inference, and sequence generation. Together, our approach offers a quantitative, generative view of protein structure space, advancing protein evolution and design studies. Significance StatementProtein sequence databases are growing far faster than our ability to experimentally determine structures, leaving much of protein space poorly annotated, especially for distant homologs. Because protein structure is more conserved and informative than sequence alone, new approaches are needed to exploit structural signals at scale. We present a generative framework that integrates compact structural representations with evolutionary constraints to map, cluster, and design protein sequences. By combining geometric encoding with coevolutionary modeling, our approach enables sensitive homology detection, improved inference of structural contacts, and rational exploration of sequence space without requiring functional labels. This work provides a quantitative bridge between protein sequence and structure, advancing our ability to interpret protein evolution and guide protein design.

biophysics↗

bioRxiv2025

Backbone Rigidity Encodes Universal Viscoelastic Signatures in Biomolecular Condensates

Biomolecular condensates exhibit a wide range of viscoelastic properties shaped by their molecular sequence and composition. Coarse-grained molecular models of intrinsically disordered proteins are widely used to complement experiments by revealing the structure and thermodynamics of condensates. However, fully flexible chain representations of inherently disordered proteins often fail to capture their complex viscoelastic behavior, instead predicting purely viscous responses. In this work, we demonstrate that introducing sequence-dependent chain rigidity enables the accurate reproduction of the elastic and viscous moduli for experimentally characterized condensates of A1-LCD and its numerous mutants. Furthermore, we show that the frequency-dependent loss factor can be described by a single parameter that universally correlates with viscosity across different sequences and variations of the coarse-grained molecular energy function. Our results also reveal that increased chain rigidity, indicated by a larger gyration radius, expands the condensates elastic regime. Finally, we elucidate the microscopic origins of sequence-encoded viscoelasticity by showing how it can be tuned through sequence rearrangements that promote sticker cluster formation.

biophysics↗

bioRxiv2025

Histone H3 tail charge patterns govern nucleosome condensate formation and dynamics

Emerging models of nuclear organization suggest that chromatin forms functionally distinct microenvironments through phase separation. As chromatin architecture is organized at the level of the nucleosome and regulated by histone post-translational modifications, we investigated how these known regulatory mechanisms influence nucleosome phase behavior. By systematically altering charge distribution within the H3 tail, we found that the terminal and central regions modulate the phase boundary and tune nucleosome condensate viscosity differentially, as revealed by microscopy-based assays, microrheology, and simulations. Nuclear magnetic resonance relaxation experiments revealed that H3 tails remain dynamically mobile within condensates, and their mobility correlates with condensate viscosity. These results demonstrate that the number, identity, and spatial arrangement of basic residues in the H3 tail critically regulate nucleosome phase separation. Our findings support a model in which nucleosomes, through their intrinsic properties and modifications, actively shape the local chromatin microenvironment--providing new insight into the histone language in chromatin condensates.

biophysics↗

bioRxiv2025

Molecular Drivers of RNA Phase Separation

RNA molecules are essential in orchestrating the assembly of biomolecular condensates and membraneless compartments in cells. Many condensates form via the association of RNA with proteins containing specific RNA binding motifs. However, recent reports indicate that low-complexity RNA sequences can self-assemble into condensate phases without protein assistance. Divalent cations significantly influence the thermodynamics and dynamics of RNA condensates, which exhibit base-specific lower-critical solution temperatures (LCST). The precise molecular origins of these temperatures remain elusive. In this study, we employ atomistic molecular simulations to elucidate the molecular driving forces governing the temperature-dependent phase behavior of RNA, providing new insights into the origins of LCST. Using RNA tetranucleotides and their chemically modified analogs, we map RNA condensates equilibrium thermodynamic profiles and structural ensembles across various temperatures and ionic conditions. Our findings reveal that magnesium ions promote LCST behavior by inducing local order-disorder transitions within RNA structures. Consistent with experimental observations, we demonstrate that the thermal stability of RNA condensates follows the Poly(G) > Poly(A) > Poly(C) > Poly(U) order shaped by the interplay of base-stacking and hydrogen bonding interactions. Furthermore, our simulations show that ionic conditions and post-translational modifications can fine-tune RNA self-assembly and modulate condensate physical properties. Author SummaryRNA molecules are essential for organizing membraneless compartments that play critical roles in cellular processes. While many of these condensates form through interactions between RNA and proteins, recent studies have shown that certain RNA sequences can self-assemble into condensates without protein assistance. This ability is influenced by the sequence composition of RNA and the presence of ions like magnesium. Using detailed molecular simulations we carried out systematic study to reveal how temperature and ionic conditions affect RNA condensation. We discovered that magnesium ions play a key role in driving RNA molecules to condense at lower temperatures by promoting structural changes within the RNA. Our findings also revealed that the stability of RNA condensates varies depending on the RNA sequence, with guanine-rich sequences being the most stable. Additionally, we demonstrated how chemical modifications and ionic conditions can fine-tune the properties of RNA condensates. This study provides new insights into how RNA forms condensates and highlights potential strategies to control their behavior, which could have implications for understanding cellular organization and developing new therapies.

biophysics↗

bioRxiv2024

Thermal Adaptation of Cytosolic Malate Dehydrogenase Revealed by Deep Learning and Coevolutionary Analysis

Protein evolution has produced enzymes that maintain stability and function across various thermal environments. While sequence variation, structural dynamics, and intermolecular interactions are known to influence an enzymes thermal adaptation, how these factors collectively govern stability and function across diverse temperatures remains unresolved. Cytosolic malate dehydrogenase (cMDH), a citric acid cycle enzyme, is an ideal model for studying these mechanisms due to its temperature-sensitive flexibility and broad presence in species from diverse thermal environments. In this study, we employ techniques inspired by deep learning and statistical mechanics to uncover how sequence variation and structural dynamics shape patterns of cMDHs thermal adaptation. By integrating coevolutionary models with variational autoencoders (VAE), we generate a latent generative landscape (LGL) of cMDH sequence space, enabling us to explore evolutionary pathways and predict fitness using direct coupling analysis (DCA). Structural predictions via AlphaFold and molecular dynamics simulations further illuminate how variations in hydrophobic interactions and conformational flexibility contribute to the thermal stability of warm- and cold-adapted cMDH orthologs. The integrative computational framework employed in this study provides powerful insights into protein adaptation at both sequence and structural levels, offering new perspectives on the evolution of thermal stability and creating avenues for the rational design of proteins with optimized thermal properties for biotechnological applications.

biophysics↗

bioRxiv2024

Nucleoprotein phase-separation affinities revealed via atomistic simulations of short peptide and RNA fragments

Liquid-liquid phase separation of proteins and nucleic acids into condensate phases is a versatile mechanism for ensuring compartmentalization of cellular biochemistry. RNA molecules play critical roles in these condensates, particularly in transcriptional regulation and stress responses, exhibiting a wide range of thermodynamic and dynamic behaviors. However, deciphering the molecular grammar that governs the stability and dynamics of protein-RNA condensates remains challenging due to the multicomponent and heterogeneous nature of these biomolecular mixtures. In this study, we employ atomistic simulations of twenty distinct mixtures containing minimal RNA and peptide fragments to dissect the phase-separating affinities of all twenty amino acids in the presence of RNA. Our findings elucidate chemically specific interactions, hydration profiles, and ionic effects that synergistically promote or suppress protein-RNA phase separation. We map a ternary phase diagram of interactions, identifying four distinct groups of residues that promote, maintain, suppress, or disrupt protein-RNA clusters.

biophysics↗

bioRxiv2024

Microscopic Origins of Flow Activation Energy in Biomolecular Condensates

Material properties of biomolecular condensates dictate their form and function, influencing the diffusion of regulatory molecules and the dynamics of biochemical reactions. The increasing quality and quantity of microrheology experiments on biomolecular condensates necessitate a deeper understanding of the molecular grammar that encodes their material properties. Recent reports have identified a characteristic timescale related to network relaxation dynamics in condensates, which governs their temperature-dependent viscoelastic properties. This timescale is intimately connected to an activated process involving the dissociation of sticker regions, with the energetic barrier referred to as flow activation energy. The microscopic origin of activation energy is a complex function of sequence patterns, component stoichiometry, and external conditions. This study elucidates the microscopic origins of flow activation energy in single and multicomponent condensates composed of model peptide sequences with varying sticker and spacer motifs, with RNA as a secondary component. We dissected the effects of condensate density, RNA stoichiometry, and peptide sequence patterning using extensive sequence-resolved coarse-grained simulations. We found that flow activation energy is closely linked to the lifetime of sticker-sticker pairs under certain conditions, though the presence of multiple competing stickers further complicates this relationship. The insights gained in this study should help establish predictive multiscale models for the material properties and serve as a valuable guide for the programmable design of condensates.

biophysics↗

bioRxiv2024

Decoding Biomolecular Condensate Dynamics: An Energy Landscape Approach

A significant fraction of eukaryotic proteins contain low-complexity sequence elements with unknown functions. Many of these sequences are prone to form biomolecular condensates with unique material and dynamic properties. Mutations in low-complexity regions often result in abnormal phase transitions into pathological solid-like states. Therefore, understanding how the low-complexity sequence patterns encode the material properties of condensates is crucial for uncovering the cellular functions and evolutionary forces behind the emergence of low-complexity regions in proteins. In this work, we employ an alphabet-free energy landscape framework of the stickers and spacers to dissect how the low complexity patterns of proteins encode the material properties of condensates. We find a broad phase diagram of material properties determined by distinct energy landscape features, showing that periodic repeat motifs promote elastic-dominated while random sequences are viscous-dominated properties. We find that a certain degree of sticker periodicity is necessary to maintain the fluidity of condensates, preventing them from forming glassy or solid-like states. Finally, we show that the energy landscape framework captures viscoelastic trends seen in the recent experiments on prion domains and makes predictions for systematic variation of protein condensate viscoelasticity via altering the periodicity and strength of sticker motifs. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/614805v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@e0ddbdorg.highwire.dtl.DTLVardef@fdbf70org.highwire.dtl.DTLVardef@12385dcorg.highwire.dtl.DTLVardef@129571b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Mapping energy landscapes of homopolymeric RNAs via simulated tempering and deep unsupervised learning

Conformational dynamics plays crucial roles in RNA functions about sensing and responding to environmental signals. The liquid-liquid phase separation of RNAs and the formation of stress granules partly relies on RNAs conformational plasticity and its ability to engage in multivalent interactions. Recent experiments with homopolymeric and low-complexity RNAs have revealed significant differences in phase separations due to differences in base chemistry of RNA units. We hypothesize that differences in RNA phase-transition dynamics can be traced back to the differences in conformational dynamics of single RNA chains. In the present contribution, we utilize atomistic simulations with numerous unsupervised learning to map temperature dependence conformational free energy landscapes for homopolymeric RNA chains. These landscapes reveal a variety of metastable excited states influenced by the nature of base chemistry. We shed light on the distinct contributions of the polyphosphate backbone versus base chemistry in shaping conformational ensembles of different RNAs. We demonstrate that the experimentally observed temperature-driven shifts in metastable state populations align with experimental phase diagrams for homopolymeric RNAs. The work establishes a microscopic framework to reason about base-specific RNA propensity for phase separation. We believe our work will be valuable for designing novel RNA sensors for biological and synthetic applications.

biophysics↗

bioRxiv2023

Temperature-Sensitive Contact Modes Allosterically Gate TRPV3

TRPV Ion channels are sophisticated molecular sensors designed to respond to distinct temperature thresholds. The recent surge in cryo-EM structures has provided numerous insights into the structural rearrangements accompanying their opening and closing; however, the molecular mechanisms by which TRPV channels establish precise and robust temperature sensing remain elusive. In this work we employ molecular simulations, multi-ensemble contact analysis, graph theory, and machine learning techniques to reveal the temperature-sensitive residue-residue interactions driving allostery in TRPV3. We find that groups of residues exhibiting similar temperature-dependent contact frequency profiles cluster at specific regions of the channel. The dominant mode clusters on the ankyrin repeat domain and displays a linear melting trend while others display non-linear trends. These modes describe the residue-level temperature response patterns that underlie the channels functional dynamics. With network analysis, we find that the community structure of the channel changes with temperature. And that a network of high centrality contacts connects distant regions of the protomer to the gate, serving as a means for the temperature-sensitive contact modes to allosterically regulate channel gating. Using a random forest model, we show that the contact states of specific temperature-sensitive modes are indeed predictive of the channel gates state. Supporting the physical validity of these modes and networks are several residues identified with our analyses that are reported in literature to be functionally critical. Our results offer high resolution insight into thermo-TRP channel function and demonstrate the utility of temperature-sensitive contact analysis.

biophysics↗

bioRxiv2022

Temperature Sensitive Contacts in Disordered Loops Tune Enzyme I Activity

Homologous enzymes with identical folds often exhibit different thermal and kinetic behaviors. Understanding how enzyme sequence encodes catalytic activity at functionally optimal temperatures is a fundamental problem in biophysics. Recently it was shown that the residues that tune catalytic activities of thermophilic/mesophilic variants of the C-terminal domain of bacterial Enzyme I (EIC) are largely localized within disordered loops, offering a model system with which to investigate this phenomenon. In this work, we employ molecular dynamics simulations and mutagenesis experiments to reveal a mechanism of sequence-dependent activity tuning of EIC homologs. We find that a network of contacts in the catalytic loops is particularly sensitive to changes in temperature, with some contacts exhibiting distinct linear or non-linear temperature-dependent trends. Moreover, these trends define structurally clustered dynamical modes and can distinguish regions that tend toward order or disorder at higher temperatures. Assaying several thermophilic EIC mutants, we show that complementary mesophilic mutations to the most temperature-sensitive positions exhibit the most enhanced activity while mutations to relatively temperature insensitive positions exhibit the least enhanced activities. These results provide a mechanistic explanation of sequence-dependent temperature tuning and offer a computational method for rational enzyme modification. SignificanceTemperature affects the catalytic rates of all enzymes. The impact of temperature on the catalytic activity of an enzyme, however, is convoluted from contributions of protein sequence, structure, and dynamics. As such, understanding and designing the molecular features of enzymes which tune catalytic rates at different temperatures remains a fundamental challenge in biophysics. In this work we have employed molecular simulations and mutagenesis experiments to reveal the temperature tuning mechanism of mesophilic and thermophilic homologues of the C domain of bacterial Enzyme l. We find that enzymes can be tuned to their physiological temperatures through a network of temperature-sensitive residue contacts localized in the disordered loops. Furthermore, we find that among temperature-sensitive contacts some exhibit linear and others non-linear dependence on temperature. These clues offer a promising physics-based approach for tuning enzyme activity.

biophysics↗

bioRxiv2021

The interplay of chromatin phase separation and lamina interactions in nuclear organisation

The genetic material of eukaryotes is segregated into transcriptionally active euchromatin and silent heterochromatin compartments. The spatial arrangement of chromatin compartments evolves over the course of cellular life in a process that remains poorly understood. The latest nuclear imaging experiments reveal a number of dynamical signatures of chromatin which are reminiscent of active multi-phase liquids. This includes the observations of viscoelastic response, coherent motions, Ostwald ripening and coalescence of chromatin compartments. There is also growing evidence that liquid-liquid phase separation of protein and nucleic acid components is the underlying mechanism for the dynamical behavior of chromatin. In order to dissect the organizational and dynamical implications of chromatins liquid behavior, we have devised a phenomenological field-theoretic model of nucleus as a multi-phase condensate of liquid chromatin types. Employing the liquid chromatin model of Drosophila nucleus, we have carried out an extensive set of simulations with an objective to shed light on the dynamics and chromatin patterning observed in the latest nuclear imaging experiments. Our simulations reveal the emergence of experimentally detected mesoscale chromatin channels and spheroidal droplets which arise from the dynamic interplay of chromatin type to type interactions and intermingling of chromosomal territories. We also quantitatively reproduce coherent motions of chromatin domains observed in displacement correlation spectroscopy measurements which are explained within the framework of our model by phase separation of chromatin types operating within constrained intra-chromosomal and inter-chromosomal boundaries. Finally, we illuminate the role of heterochromatin-lamina interactions in the nuclear organization by showing that these interactions enhance the mobility of euchromatin and indirectly introduce correlated motions of heterochromatin droplets. SIGNIFICANCE STATEMENTThe latest super-resolution imaging experiments have revealed a surprisingly dynamic and stochastic nature of chromatin in eukaryotic nuclei which is reminiscent of multi-phase fluid behavior. As a result, the concepts from the theory of complex fluids such as phase separation, viscoelasticity, and droplet nucleation have found widespread utility in understanding salient features of nuclear organization. In order to understand and disentangle the complex interplay of forces that contribute to the emergent patterns of organization and dynamics, we have devised a phenomenological field-theoretic model of nucleus as a multi-phase condensate of liquid chromatin types. Armed with a mesoscopic model of nuclear chromatin, we have shed light on the distinct dynamical and structural contributions of chromatin type interactions intermingling of chromosomal territories and lamina binding. We also shed light on the dynamical heterogeneity and coherent motions of chromatin domains which are fully captured by an interplay of micro-phase separation of chromatin types and lamina binding.

biophysics↗