An α-ketoglutarate conformational switch controls iron accessibility, activation, and substrate selection of the human FTO protein.

The fat mass and obesity-associated fatso (FTO) protein is a member of the Alkb family of dioxygenases and catalyzes oxidative demethylation of N6-methyladenosine (m6A), N1-methyladenosine (m1A), 3-methylthymine (m3T), and 3-methyluracil (m3U) in single-stranded nucleic acids. It is well established that the catalytic activity of FTO proceeds via two coupled reactions. The first reaction involves decarboxylation of alpha-ketoglutarate (αKG) and formation of an oxyferryl species. In the second reaction, the oxyferryl intermediate oxidizes the methylated nucleic acid to reestablish Fe(II) and the canonical base. However, it remains unclear how binding of the nucleic acid activates the αKG decarboxylation reaction and why FTO demethylates different methyl modifications at different rates. Here, we investigate the interaction of FTO with 5-mer DNA oligos incorporating the m6A, m1A, or m3T modifications using solution NMR, molecular dynamics (MD) simulations, and enzymatic assays. We show that binding of the nucleic acid to FTO activates a two-state conformational equilibrium in the αKG cosubstrate that modulates the O2 accessibility of the Fe(II) catalyst. Notably, the substrates that provide better stabilization to the αKG conformation in which Fe(II) is exposed to O2 are demethylated more efficiently by FTO. These results indicate that i) binding of the methylated nucleic acid is required to expose the catalytic metal to O2 and activate the αKG decarboxylation reaction, and ii) the measured turnover of the demethylation reaction (which is an ensemble average over the entire sample) depends on the ability of the methylated base to favor the Fe(II) state accessible to O2.

Temperature-sensitive contacts in disordered loops tune enzyme I activity.

Homologous enzymes with identical folds often exhibit different thermal and kinetic behaviors. Understanding how an 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 use 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 nonlinear 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.

Energy landscapes of homopolymeric RNAs revealed by deep unsupervised learning.

Conformational dynamics of RNA plays important roles in a variety of cellular functions such as transcriptional regulation, catalysis, scaffolding, and sensing. Recently, RNAs with low-complexity sequences have been shown to phase separate and form condensate phases similar to lowcomplexity protein domains. The affinity for phase separation and the material characteristics of RNA condensates are strongly dependent on sequence composition and patterning. We hypothesize that differences in the affinities for RNA phase separation can be uncovered by studying sequence-dependent conformational dynamics of single RNA chains. To this end, we have employed atomistic simulations and deep dimensionality reduction techniques to map temperature-dependent conformational free energy landscapes for 20 base-long homopolymeric RNA sequences: poly(U), poly(G), poly(C), and poly(A). The energy landscapes of homopolymeric RNAs reveal a plethora of metastable states with qualitatively different populations stemming from differences in base chemistry. Through detailed analysis of base, phosphate, and sugar interactions, we show that experimentally observed temperature-driven shifts in metastable state populations align with experiments on RNA phase transitions. Specifically, we find that the thermodynamics of unfolding of homopolymeric RNA follows the poly(G) > poly(A) > poly(C) > poly(U) order of stability, mirroring the propensity of RNA to form condensates. To conclude, this work shows that at least for homopolymeric RNA sequences the single-chain conformational dynamics contains sufficient information for predicting and quantifying condensate forming affinities of RNAs. Thus, we anticipate that atomically detailed studies of temeprature -dependent energy landscapes of RNAs will be a useful guide for understanding the propensity of various RNA molecules to form condensates.

Phase separation of multicomponent peptide mixtures into dehydrated clusters with hydrophilic cores.

Phase separation of biomolecules underlies the formation and regulation of various membraneless condensates in cells. How condensates function reliably while surrounded by heterogeneous and dynamic mixtures of biomolecular components with specific and nonspecific interactions is yet to be understood. Studying multicomponent biomolecular mixtures with designer peptides has recently become an attractive avenue for learning about physicochemical principles governing cellular condensates. In this work, we employed long-timescale atomistic simulations of multicomponent tripeptide mixtures with all residue substitutions to illuminate the nature of direct and water-mediated interactions in a prototypical cellular condensate environment. We find that peptide mixtures form clusters with inverse hydrophobic order. Most multivalent and charged residues are localized in the cluster's core, with a large fraction of nonaromatic hydrophobic residues remaining on the surface. This inverse hydrophobic order in peptide clusters is partly driven by the expulsion of nonspecifically bound water molecules following peptide cluster growth. The growth of clusters is also accompanied by the formation of increasing numbers of specific water-mediated interactions between polar and charged residues. While the present study focused on the condensation of short peptide motifs, the general findings and analysis techniques should be helpful for future studies on larger peptides and protein condensation.

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 channel's 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 gate's 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.

Atomistic insights into the reentrant phase-transitions in polyuracil and polylysine mixtures.

The phase separation of protein and RNA mixtures underpins the assembly and regulation of numerous membraneless organelles in cells. The ubiquity of protein-RNA condensates in cellular regulatory processes is in part due to their sensitivity to RNA concentration, which affects their physical properties and stability. Recent experiments with poly-cationic peptide-RNA mixtures have revealed closed-loop phase diagrams featuring lower and upper critical solution temperatures. These diagrams indicate reentrant phase transitions shaped by biomolecular interactions and entropic forces such as solvent and ion reorganization. We employed atomistic simulations to study mixtures with various RNA-polylysine stoichiometries and temperatures to elucidate the microscopic driving forces behind reentrant phase transitions in protein-RNA mixtures. Our findings reveal an intricate interplay between hydration, ion condensation, and specific RNA-polylysine hydrogen bonding, resulting in distinct stoichiometry-dependent phase equilibria governing stabilities and structures of the condensate phase. Our simulations show that reentrant transitions are accompanied by desolvation around the phosphate groups of RNA, with increased contacts between phosphate and lysine side chains. In RNA-rich systems at lower temperatures, RNA molecules can form an extensive pi-stacking and hydrogen bond network, leading to percolation. In protein-rich systems, no such percolation-induced transitions are observed. Furthermore, we assessed the performance of three prominent water force fields-Optimal Point Charge (OPC), TIP4P-2005, and TIP4P-D-in capturing reentrant phase transitions. OPC provided a superior balance of interactions, enabling effective capture of reentrant transitions and accurate characterization of changes in solvent reorganization. This study offers atomistic insights into the nature of reentrant phase transitions using simple model peptide and nucleotide mixtures. We believe that our results are broadly applicable to larger classes of peptide-RNA mixtures exhibiting reentrant phase transitions.

Solution Structure Ensembles of the Open and Closed Forms of the ∼130 kDa Enzyme I via AlphaFold Modeling, Coarse Grained Simulations, and NMR.

Large-scale interdomain rearrangements are essential to protein function, governing the activity of large enzymes and molecular machineries. Yet, obtaining an atomic-resolution understanding of how the relative domain positioning is affected by external stimuli is a hard task in modern structural biology. Here, we show that combining structural modeling by AlphaFold2 with coarse-grained molecular dynamics simulations and NMR residual dipolar coupling data is sufficient to characterize the spatial domain organization of bacterial enzyme I (EI), a ∼130 kDa multidomain oligomeric protein that undergoes large-scale conformational changes during its catalytic cycle. In particular, we solve conformational ensembles for EI at two different experimental temperatures and demonstrate that a lower temperature favors sampling of the catalytically competent closed state of the enzyme. These results suggest a role for conformational entropy in the activation of EI and demonstrate the ability of our protocol to detect and characterize the effect of external stimuli (such as mutations, ligand binding, and post-translational modifications) on the interdomain organization of multidomain proteins. We expect the ensemble refinement protocol described here to be easily transferrable to the investigation of the structure and dynamics of other uncharted multidomain systems and have assembled a Google Colab page (https://potoyangroup.github.io/Seq2Ensemble/) to facilitate implementation of the presented methodology elsewhere.

Re-entrant transitions of locally stiff RNA chains in the presence of polycations leads to gelated architectures.

The liquid-liquid phase separation of protein and nucleic acid mixtures drives the formation of numerous membraneless compartments in cells. Temperature variation is commonly used for mapping condensate phase diagrams, which often display unique upper critical temperatures. Recent report on peptide-RNA mixtures has shown the existence of lower and upper critical solution temperatures, highlighting the importance of temperature-dependent solvent and ion-mediated forces. In the present work, we employ residue-level coarse-grained models of RNA and polycation peptide chains for simulating temperature-induced re-entrant transitions and shedding light on the role played by mobile ions, temperature-dependent dielectric permittivity, and local chain stiffness. We show that differences in bending rigidity can significantly modulate condensate topology leading to the formation of gelated or fibril like architectures. The study also finds that temperature dependence of water permittivity is generally sufficient for recapitulating experimentally observed closed loop and LCST phase diagrams of highly charged protein-RNA mixtures. However, we find that similar-looking closed-loop phase diagrams can correspond to vastly different condensate topologies.

Phase transition of RNA-protein complexes into ordered hollow condensates.

Liquid-liquid phase separation of multivalent intrinsically disordered protein-RNA complexes is ubiquitous in both natural and biomimetic systems. So far, isotropic liquid droplets are the most commonly observed topology of RNA-protein condensates in experiments and simulations. Here, by systematically studying the phase behavior of RNA-protein complexes across varied mixture compositions, we report a hollow vesicle-like condensate phase of nucleoprotein assemblies that is distinct from RNA-protein droplets. We show that these vesicular condensates are stable at specific mixture compositions and concentration regimes within the phase diagram and are formed through the phase separation of anisotropic protein-RNA complexes. Similar to membranes composed of amphiphilic lipids, these nucleoprotein-RNA vesicular membranes exhibit local ordering, size-dependent permeability, and selective encapsulation capacity without sacrificing their dynamic formation and dissolution in response to physicochemical stimuli. Our findings suggest that protein-RNA complexes can robustly create lipid-free vesicle-like enclosures by phase separation.

BIAPSS: A Comprehensive Physicochemical Analyzer of Proteins Undergoing Liquid-Liquid Phase Separation.

The liquid-liquid phase separation (LLPS) of biomolecules is a phenomenon which is nowadays recognized as the driving force for the biogenesis of numerous functional membraneless organelles and cellular bodies. The interplay between the protein primary sequence and phase separation remains poorly understood, despite intensive research. To uncover the sequence-encoded signals of protein capable of undergoing LLPS, we developed a novel web platform named BIAPSS (Bioinformatics Analysis of LLPS Sequences). This web server provides on-the-fly analysis, visualization, and interpretation of the physicochemical and structural features for the superset of curated LLPS proteins.

The interplay of chromatin phase separation and lamina interactions in nuclear organization.

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 that are reminiscent of active multiphase 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. To dissect the organizational and dynamical implications of chromatin's liquid behavior, we have devised a phenomenological field-theoretic model of the nucleus as a multiphase condensate of liquid chromatin types. Employing the liquid chromatin model of the 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 intrachromosomal and interchromosomal 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.

Liquid-liquid phase separation driven compartmentalization of reactive nucleoplasm.

The nucleus of eukaryotic cells harbors active and out of equilibrium environments conducive to diverse gene regulatory processes. On a molecular scale, gene regulatory processes take place within hierarchically compartmentalized sub-nuclear bodies. While the impact of nuclear structure on gene regulation is widely appreciated, it has remained much less clear whether and how gene regulation is impacting nuclear order itself. Recently, the liquid-liquid phase separation emerged as a fundamental mechanism driving the formation of biomolecular condensates, including membrane-less organelles, chromatin territories, and transcriptional domains. The transience and environmental sensitivity of biomolecular condensation are strongly suggestive of kinetic gene-regulatory control of phase separation. To better understand kinetic aspects controlling biomolecular phase-separation, we have constructed a minimalist model of the reactive nucleoplasm. The model is based on the Cahn-Hilliard formulation of ternary protein-RNA-nucleoplasm components coupled to non-equilibrium and spatially dependent gene expression. We find a broad range of kinetic regimes through an extensive set of simulations where the interplay of phase separation and reactive timescales can generate heterogeneous multi-modal gene expression patterns. Furthermore, the significance of this finding is that heterogeneity of gene expression is linked directly with the heterogeneity of length-scales in phase-separated condensates.

Anomalous diffusion, spatial coherence, and viscoelasticity from the energy landscape of human chromosomes.

The nucleus of a eukaryotic cell is a nonequilibrium system where chromatin is subjected to active processes that continuously rearrange it over the cell's life cycle. Tracking the motion of chromosomal loci provides information about the organization of the genome and the physical processes shaping that organization. Optical experiments report that loci move with subdiffusive dynamics and that there is spatially coherent motion of the chromatin. We recently showed that it is possible to predict the 3D architecture of genomes through a physical model for chromosomes that accounts for the biochemical interactions mediated by proteins and regulated by epigenetic markers through a transferable energy landscape. Here, we study the temporal dynamics generated by this quasi-equilibrium energy landscape assuming Langevin dynamics at an effective temperature. Using molecular dynamics simulations of two interacting human chromosomes, we show that the very same interactions that account for genome architecture naturally reproduce the spatial coherence, viscoelasticity, and the subdiffusive behavior of the motion in interphase chromosomes as observed in numerous experiments. The agreement between theory and experiments suggests that even if active processes are involved, an effective quasi-equilibrium landscape model can largely mimic their dynamical effects.

Mesoscale Liquid Model of Chromatin Recapitulates Nuclear Order of Eukaryotes.

The nuclear envelope segregates the genome of Eukaryota from the cytoplasm. Within the nucleus, chromatin is further compartmentalized into architectures that change throughout the lifetime of the cell. Epigenetic patterns along the chromatin polymer strongly correlate with chromatin compartmentalization and, accordingly, also change during the cell life cycle and at differentiation. Recently, it has been suggested that subnuclear chromatin compartmentalization might result from a process of liquid-liquid phase separation orchestrated by the epigenetic marking and operated by proteins that bind to chromatin. Here, we translate these observations into a diffuse interface model of chromatin, which we named the mesoscale liquid model of nucleus. Using this streamlined continuum model of the genome, we study the large-scale rearrangements of chromatin that happen at different stages of the growth and senescence of the cell and during nuclear inversion events. In particular, we investigate the role of droplet diffusion, fluctuations, and heterochromatin-lamina interactions during nuclear remodeling. Our results indicate that the physical process of liquid-liquid phase separation, together with surface effects, is sufficient to recapitulate much of the large-scale morphology and dynamics of chromatin along the life cycle of cells.

A stochastic and dynamical view of pluripotency in mouse embryonic stem cells.

Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals.

Molecular stripping in the NF-κB/IκB/DNA genetic regulatory network.

Genetic switches based on the [Formula: see text] system are master regulators of an array of cellular responses. Recent kinetic experiments have shown that [Formula: see text] can actively remove NF-κB bound to its genetic sites via a process called "molecular stripping." This allows the [Formula: see text] switch to function under kinetic control rather than the thermodynamic control contemplated in the traditional models of gene switches. Using molecular dynamics simulations of coarse-grained predictive energy landscape models for the constituent proteins by themselves and interacting with the DNA we explore the functional motions of the transcription factor [Formula: see text] and its various binary and ternary complexes with DNA and the inhibitor IκB. These studies show that the function of the [Formula: see text] genetic switch is realized via an allosteric mechanism. Molecular stripping occurs through the activation of a domain twist mode by the binding of [Formula: see text] that occurs through conformational selection. Free energy calculations for DNA binding show that the binding of [Formula: see text] not only results in a significant decrease of the affinity of the transcription factor for the DNA but also kinetically speeds DNA release. Projections of the free energy onto various reaction coordinates reveal the structural details of the stripping pathways.

Resolving the NFκB Heterodimer Binding Paradox: Strain and Frustration Guide the Binding of Dimeric Transcription Factors.

Many eukaryotic transcription factors function after forming oligomers. The choice of protein partners is a nonrandom event that has distinct functional consequences for gene regulation. In the present work we examine three dimers of transcription factors in the NFκB family: p50p50, p50p65, and p65p65. The NFκB dimers bind to a myriad of genomic sites and switch the targeted genes on or off with precision. The p65p50 heterodimer of NFκB is the strongest DNA binder, and its unbinding is controlled kinetically by molecular stripping from the DNA induced by IκB. In contrast, the homodimeric forms of NFκB, p50p50 and p65p65, bind DNA with significantly less affinity, which places the DNA residence of the homodimers under thermodynamic rather than kinetic control. It seems paradoxical that the heterodimer should bind more strongly than either of the symmetric homodimers since DNA is a nearly symmetric target. Using a variety of energy landscape analysis tools, here we uncover the features in the molecular architecture of NFκB dimers that are responsible for these drastically different binding free energies. We show that frustration in the heterodimer interface gives the heterodimer greater conformational plasticity, allowing the heterodimer to better accommodate the DNA. We also show how the elastic energy and mechanical strain in NFκB dimers can be found by extracting the principal components of the fluctuations in Cartesian coordinates as well as fluctuations in the space of physical contacts, which are sampled via simulations with a predictive energy landscape Hamiltonian. These energetic contributions determine the specific detailed mechanisms of binding and stripping for both homo- and heterodimers.

On the dephasing of genetic oscillators.

The digital nature of genes combined with the associated low copy numbers of proteins regulating them is a significant source of stochasticity, which affects the phase of biochemical oscillations. We show that unlike ordinary chemical oscillators, the dichotomic molecular noise of gene state switching in gene oscillators affects the stochastic dephasing in a way that may not always be captured by phenomenological limit cycle-based models. Through simulations of a realistic model of the NFκB/IκB network, we also illustrate the dephasing phenomena that are important for reconciling single-cell and population-based experiments on gene oscillators.

The Acetylation Landscape of the H4 Histone Tail: Disentangling the Interplay between the Specific and Cumulative Effects.

Histone tails, the intrinsically disordered terminal regions of histone proteins, are key modulators of the structure and dynamics of chromatin and, consequently, are central to many DNA template-directed processes including replication, repair, and transcription. Acetylation of histone tails is a major post-translational modification (PTM) involved in regulating chromatin, yet it remains unclear how acetylation modifies the disordered state of histone tails and affects their function. We investigated the consequences of increasing acetylation on the isolated H4 histone tail by characterizing the conformational ensembles of unacetylated, mono-, di-, tri-, and tetra-acetylated H4 histone tails using Replica Exchange Molecular Dynamics (REMD) simulations. We found that progressive acetylation has a cumulative effect on the H4 tail, decreasing conformational heterogeneity, increasing helical propensity, and increasing hydrogen bond occupancies. The monoacetylation of lysine 16, however, has unique and specific effects: drastically decreasing the conformational heterogeneity of the H4 tail and leading to highly localized helical secondary structure and elongated conformations. We describe how the cumulative effects of acetylation arise from the charge reduction and increased hydrophobicity associated with adding acetyl groups, while the specific effects are a consequence of steric interactions that are sequence specific. Additionally, we found that increasing the level of acetylation results in the formation of spatially clustered lysines that could serve as recognition patches for binding of chromatin regulating proteins. Hence, we explore the mechanisms by which different acetylation patterns may result in specific recognition of the H4 histone tails by protein or DNA binding partners.

Dichotomous noise models of gene switches.

Molecular noise in gene regulatory networks has two intrinsic components, one part being due to fluctuations caused by the birth and death of protein or mRNA molecules which are often present in small numbers and the other part arising from gene state switching, a single molecule event. Stochastic dynamics of gene regulatory circuits appears to be largely responsible for bifurcations into a set of multi-attractor states that encode different cell phenotypes. The interplay of dichotomous single molecule gene noise with the nonlinear architecture of genetic networks generates rich and complex phenomena. In this paper, we elaborate on an approximate framework that leads to simple hybrid multi-scale schemes well suited for the quantitative exploration of the steady state properties of large-scale cellular genetic circuits. Through a path sum based analysis of trajectory statistics, we elucidate the connection of these hybrid schemes to the underlying master equation and provide a rigorous justification for using dichotomous noise based models to study genetic networks. Numerical simulations of circuit models reveal that the contribution of the genetic noise of single molecule origin to the total noise is significant for a wide range of kinetic regimes.