Photosynthetic microorganisms rely on multiple central-carbon-metabolism pathways to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes that operate across disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. Here, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacterium Prochlorococcus marinus MED4 that explicitly represents the spatial, subcellular organization of key carbon fixation enzymes and genetic information processes coupled to a non-spatial genome-scale metabolic model (GSMM). We combine perturbative, time-resolved multi-omics measurements and cryo-ET derived 3D segmented volumes as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulate carbon fixation by controlling the structural assembly of a protein megacomplex, the "dark complex," at a conserved regulatory node of the Calvin-Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates the importance of spatial heterogeneity in understanding phenotypic robustness. Spatiotemporal sequestration creates a timing hierarchy spanning seconds to hours and noise-buffering behavior that cannot be recovered from well-mixed phenomenological models or purely time-resolved descriptions. Constrained by spatial heterogeneity, local enzyme stoichiometry and diffusion-limited assembly/disassembly determine effective stochastically varying control kinetics. Diffusion-driven fluctuations amplify transcription of highly expressed genes, whereas PTM-dependent regulation on enzyme stoichiometry maintains perturbation-driven phenotypic outcomes. 4D whole cell modeling with perturbation-based multi-modal experiments unlocks the ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and light-dependent central carbon metabolism. The outcome of this work addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such as P. marinus MED4.

Multidomain proteins connected by flexible linkers populate conformational ensembles that are challenging to characterize using conventional structural biology methods. In domain-linker-domain (DLD) proteins, linker-mediated inter-domain relative positions and orientations are functionally relevant, yet their dynamical behavior in solution normally remain poorly described. Small-angle X-ray scattering (SAXS) provides ensemble-averaged structural information for such systems; however, coupling with computational modeling is required to accurately describe the dynamic behavior of this family of proteins in solution. Here, we present a systematic evaluation of five ensemble-generation strategies applied to a set of eighteen proteins sharing the same two globular domains, connected by naturally occurring linkers of varying length and composition. Modeling methods based on different underlying principles are compared by assessing their agreement to experimental SAXS data, showing a large disparity and systematic structural biases among them. Furthermore, for each approach, we examine the effect of refinement against SAXS restraints and assess its capacity to describe the experimental data, as well as the induced biases in global dimensions and inter-domain distance distributions. This analysis underlines the importance of the initial conformational pool for deriving experimentally compatible ensembles. Overall, this work provides a high-quality benchmark for SAXS-driven ensemble modeling of flexible, multidomain proteins and establishes a framework for the critical interpretation of solution scattering data in systems with pronounced conformational heterogeneity.

Although traditional molecular dynamics simulations successfully capture a variety of different molecular interactions, the protonation states of titratable residues are kept static. A recent constant-pH molecular dynamics implementation in the GROMACS package allows pH effects to be captured dynamically, and promises to provide both the accuracy and computational performance required for studying pH-mediated conformational dynamics in large, complex systems containing hundreds of titratable residues. Here, we demonstrate the applicability of this constant-pH implementation by simulating the proton-gated ion channel GLIC at resting and activating pH, starting from closed and open structures. Our simulations identify residues E26 and E35 as especially pH-sensitive and reveal state-dependent pKa shifts at multiple residues, as well as side chain and domain rearrangements in line with the early stages of gating. Our results are consistent with several previous experimental findings, demonstrating the applicability of constant-pH simulations to elucidate pH-mediated activation mechanisms in multidomain membrane proteins, likely extensible to other complex systems.

Integrins are bidirectional mechanochemical receptors that transmit signals upon ligand binding to the cytoskeleton (outside-in) and cytoskeletal forces back across the membrane (inside-out) to the integrin-ligand bond. Integrins are activated prior to ligand binding, which involves large conformational rearrangements across the extracellular, transmembrane, and cytoplasmic regions. While the conformational and energetic basis of outside-in activation is increasingly well defined, the mechanical forces required for separating the tightly packed αβ transmembrane (TM) helices during inside-out signaling remain largely unknown. Here, we directly quantify the forces required to dissociate integrin TM domains (TMDs) in a lipid environment. Engineered α5β1 polypeptides consisting of TMDs and cytoplasmic tails were reconstituted into lipid nanodiscs and probed using single-molecule optical tweezers. Mechanical marker domains on each cytoplasmic tail verified correct vectorial force application, and fluorescent lipids confirmed nanodisc integrity. We find that the heterodimeric TM complex is a mechanically robust unit. In wild-type constructs, no TMD separation was observed in repeated pulls up to ∼35 pN. Point mutations in the β1-TMD (G744L, L748R) that weaken TMD interactions revealed discrete splitting events. The high mechanical forces necessary for TMD separation support a "ratchet-like" role for mechanical forces in inside-out signaling: rather than actively opening closed TMDs they prevent re-closing of spontaneously split TMDs thus keeping them open and activated.

The homotrimeric SARS-CoV-2 spike glycoprotein comprises two subunits: S1, which recognizes host-receptors through its receptor-binding domains (RBDs), and S2, anchored to the viral membrane through its transmembrane domain (TMD), which facilitates the fusion of the viral envelope with the host cell membrane. Upon host-receptor engagement and proteolytic activation, S1 dissociates and triggers a large conformational transition in S2, involving structural rearrangements in the S2 ectomembrane-domains and the TMD. While studies have focused on the ectomembrane-domain dynamics, the TMD has typically been modeled as being in a trimeric state. Here, we use molecular dynamics simulations of a coarse-grained structure-based model (SBM) with an implicit membrane to investigate the role of TMD dynamics in modulating S2 conformational conversion. We first recapitulate previous results from an all-atom SBM with a trimeric TMD and re-emphasize that the extended pre-hairpin intermediate state of S2, which brings the two membranes into apposition, is a byproduct of the prefusion-to-postfusion transition. Next, by introducing dynamics into the TMD, we find a late fusion intermediate structurally similar to a recent cryo-EM structure. A dynamic TMD also makes the conformational transition faster. Simulations including the S1/S2 complex reveal coupled RBD-TMD dynamics: when all three RBDs are in the closed state, they can stabilize the TMD in a trimeric configuration, whereas the opening of a single RBD can trigger a transition to a dynamic TMD. So, the dynamics and the conformational preferences of the TMD can be tuned by the presence and conformation of S1. There is some evidence that the TMDs of class I viral fusion proteins, such as spike, contribute to viral fusion by modulating membrane properties. Our simulations indicate an expanded role for the function of the TMD, where it can directly regulate the kinetics of S2 conformational transitions.

Several members of the adhesion subfamily of G protein-coupled receptors (aGPCRs) are capable of self-activation by an internal agonist sequence (aka the Stachel) that's exposed upon removal or conformational changes of the N-terminal fragment of the receptor. Synthetic peptides derived from the Stachel sequence can be used as exogenous agonists. In the inactive form of the full-length receptor, the Stachel is sequestered as the β13-strand within the GPCR Autoproteolysis-INducing (GAIN) domain, but it engages the seven transmembrane region as a helix when it is either an intramolecular sequence or a synthetic peptide. Little is known about the molecular details underlying this transition, but we hypothesize that a disordered conformation is central to this intermediate state in receptor activation. Despite the primarily helical Stachel AlphaFold3 and PEP-FOLD4 models predicted with high confidence for the entire aGPCR subfamily, computational predictions and biophysical experiments reveal a predominantly disordered conformation in solution. Investigating the ADGRG6 (also known as GPR126) Stachel peptide, circular dichroism (CD) and nuclear magnetic resonance (NMR) experiments reveal a predominantly random coil conformation in aqueous buffer, polar detergent micelles, and zwitterionic lipids. Titration of trifluoroethanol uncovered a two-state equilibrium between an unfolded and helix-containing conformation with NMR localizing a single-turn helix to residues L846-L849. Taken together, these data indicate the ADGRG6 Stachel peptide is primarily disordered with a subset adopting partial helical structures, likely requiring the steric hindrance of the receptor binding pocket to fully induce helix formation in an induced fit mechanism.

Cancer cells in hypoxic environments often proliferate less but exhibit enhanced migration relative to their normoxic counterparts. Recent in vitro and in silico studies have characterized the role of hypoxic memory - the ability of cancer cells to retain their hypoxic phenotype even when reoxygenated - in tumor invasion. However, the observations have been limited either to exposing cancer cells to hypoxia for a fixed duration or by assuming a fixed-time persistence of the hypoxic state upon reoxygenation independent of the duration of hypoxia exposure. Thus, duration-dependent cell-state changes during hypoxia and their impact on hypoxic memory remains unclear. Here, we first analyze transcriptomic data from breast cancer samples to show that the genes upregulated at transcriptional level and hypomethylated at epigenetic level are enriched in cell invasion, indicating hypoxic memory-driven process of tumor invasion. Next, we used a computational model to investigate how the spatial-temporal dynamics of oxygen levels in a tumor drive duration-dependent changes in hypoxic memory and influence tumor invasion dynamics. Our simulation results show that such dynamic hypoxic memory can drive enhanced tumor invasion over a fixed hypoxic memory by a) enriching hypoxic cell density at the tumor front, b) reducing sensitivity of hypoxic cell state to fluctuations in oxygen supply, and c) enhancing effective diffusion of hypoxic cells. Our results highlight the crucial role of dynamic hypoxic memory in shaping tumor invasion dynamics, underscoring the need to elucidate its underlying mechanisms in future studies.

Macromolecular crystallography has historically inferred models of internal crystal architecture from reciprocal-space measurements of Bragg reflections. Nevertheless, direct real-space visualization of crystallographic disorder remains elusive, particularly at the nanoscale. Using a 15-nanometer probe, here we apply both ambient-temperature and cryogenic four-dimensional scanning transmission electron microscopy (4D-STEM) to map the topography of coherently diffracting domains (CDDs) in lysozyme and myoglobin microcrystals at length scales 100 × finer than conventional X-ray and electron beams. Virtual dark-field images show that each Bragg peak arises from spatially distinct subvolumes representing smooth and continuous variations in local lattice orientation. Under sustained irradiation, protein CDDs undergo rearrangements spanning several micrometers of internal movement. Furthermore, pinpoint high-dose "impact crater" experiments reveal delocalized radiolytic damage propagating hundreds of nanometers from primary irradiation sites, behavior similar to small-molecule crystals but amplified in both rate and magnitude. Together, these results establish macromolecular microcrystals as dynamic assemblies whose internal architecture continuously reorganizes during irradiation, laying the foundation for realistic models of mosaicity directly informed by both real-space and reciprocal-space observations.

Within the islets of Langerhans, pancreatic beta cells coordinate pulsatile insulin release that is essential for metabolic homeostasis. This coordination emerges from complex intercellular coupling and unfolds at least on three nested timescales: (i) slow, metabolism-driven oscillations lasting several minutes; (ii) fast, electrically driven bursts of a few seconds; and (iii) ultrafast action-potential spikes on the order of tens of milliseconds. To unravel the principles governing this multiscale collective behavior and its link to secretion, we developed a phenomenological multicellular mathematical model based on realistic intercellular interaction patterns and combined it with timescale-specific functional connectivity analysis. Despite its deliberate simplicity, the model reproduces the rich dynamics observed experimentally, disentangling how structural coupling and multimodal connectivity patterns shape wave-like signal propagation across all three oscillatory domains. By extending the model with a secretion module, we demonstrate that the metabolic oscillation dictates the period of insulin pulses, whereas burst activity modulates their amplitude by sculpting underlying spike trains and determining the duty cycle or active time. The integrated framework therefore explains how coupling between distinct temporal domains and intercellular interactions supports robust regulation of secretion under different stimulatory conditions. Finally, by imposing changes in the form of decreased intercellular coupling and increased cellular excitability, the model predicts diminished insulin pulsatility together with elevated average secretory output, thereby recapitulating key features of secretory dysregulation observed during the early stages of type 2 diabetes pathogenesis. Thus, our findings provide a unifying multiscale perspective on beta cell network dynamics and identify specific network features whose disruption may underlie disease-related secretory defects, offering quantitative targets for experimental interrogation and therapeutic intervention.

The ion channel TRPV1 is expressed in the peripheral nervous system where it mediates heat sensation and pain signaling. Although an inhibitory binding site for phosphoinositides was identified at the vanilloid site in TRPV1 structures, a mechanism for phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) to both inhibit and potentiate TRPV1 activity is lacking. This gap in knowledge led us to examine structural and sequence overlap between TRPV1 and the PI(4,5)P2 binding site of TRPV5. In an adjacent space to the S6 helix we identify a second PI(4,5)P2 binding site in TRPV1, which we term the "front porch". On the one hand, we obtained electrophysiological evidence with the TRPV1-H410R mutant that PI(4,5)P2 associates with TRPV1 at the front porch site through an electrostatic interaction to potentiate channel activity. However, the charge reversal in TRPV1-H410D conferred increased capsaicin sensitivity, which was not expected if the loss of electrostatic interaction with PI(4,5)P2 would negatively impact channel function. Importantly, capsaicin activated currents from TRPV1-H410D experience significant current run-up, consistent with the slow displacement of a lipid in the vanilloid site by capsaicin. Lastly, we tested TRPV1-H410D sensitivity to PI(4,5)P2 with the Pseudojanin rapamycin-phosphatase (PJ) system which converts the membrane pool of PI(4,5)P2 to PI . Depletion of PI(4,5)P2 with the PJ-system in wild type channels induces 1) a robust increase in baseline activity as channels are released from inhibition and 2) loss of current potentiation, driven by PI(4,5)P2 dissociation from the front porch, both of which were absent in TRPV1-H410D. We hypothesize that plasma membrane TRPV1-H410D maintains PI rather than PI(4,5)P2 in its vanilloid site and does not interact with PI(4,5)P2 in the front porch site. Integrating our discoveries that H410 maintains contacts with PI(4,5)P2 at two distinct sites in a mutually exclusive manner, we present a cohesive model for phosphoinositide inhibition and potentiation of TRPV1.

Eukaryotic genomes self-organize into diverse three-dimensional (3D) chromatin conformations through intranuclear long-range interactions, yet it remains challenging to interpret how concurrent interactions combine along a chromatin polymer to shape measurable conformational readouts. Building on a minimal harmonic polymer model and the Gaussian covariance formalism, we present a 3D genome circuit representation that reorganizes the covariance-derived effective interaction strength (EIS) of a target locus pair into motif-level interaction patterns. In this representation, the graph topology of a chromatin interaction network determines the arrangement of circuit operations that summarize how local loop motifs contribute to EIS through series-like, parallel-like, or more general coupled-network combinations. The resulting EIS is then connected to experimentally accessible statistics, including pairwise contact probabilities and spatial-distance distributions from the Gaussian equilibrium ensemble, and loop stability through a first-passage description. Beyond single-pair readouts, we derive a closed-form Pearson correlation coefficient between two inter-locus spatial distances in a general harmonic network, providing an analytical way to quantify coordinated proximity changes between two locus pairs. We apply the framework to (1) estimate the effective Shh-ZRS interaction strength in a preformed CTCF-dependent loop configuration and (2) quantify enhancer-promoter proximity coordination in a shared-enhancer configuration. This motif-level circuit representation provides physics-grounded design rules for interpreting how local interaction topology, interaction strength, and perturbations such as anchor deletion or tether addition alter EIS and associated 3D conformational readouts.

Muscle contraction is a prototypical multiscale chemomechanical process in which ATP hydrolysis at the molecular level drives force generation and mechanical work at larger scales. A central challenge is to incorporate the free energy supplied by ATP hydrolysis into a mechanical cross-bridge model in a way that is thermodynamically consistent and connects microscopic motor cycling to macroscopic force generation. Here we use the Energetic Variational Approach (EnVarA) to combine Hill's cycle-affinity viewpoint with Huxley's sliding-filament mechanics in a single thermodynamically consistent framework. We formulate a three-state Fokker-Planck-jump description for cross-bridge densities evolving on state-dependent free-energy landscapes, and treat ATP, ADP, and Pi as reacting and diffusing chemical species that share the same free-energy functional as the cross-bridge densities. ATP hydrolysis enters the model through local detailed balance, which biases the transition rates. Filament sliding velocity is incorporated as a convective transport term in the Fokker-Planck dynamics, so mechanical power output is defined directly from the energy balance law. Under chemostatted conditions and a fast-equilibration assumption for the two attached states, the model reduces to a closed two-state molecular motor description; in a further small-diffusion limit, this reduction recovers a Huxley-type transport-reaction equation whose effective attachment and detachment rates are derived from the underlying three-state cycle rather than prescribed phenomenologically, with their strain, ATP, and phosphate dependence fixed by the energy landscapes and local detailed balance. We calibrate the reduced model against cross-bridge-level observables and show that it reproduces key velocity-dependent trends and yields a Hill-like force-velocity relation comparable to established cross-bridge models.

Accurately predicting RNA secondary structure remains a central challenge in computational biology. Although standard methods based on minimum free energy (MFE) optimization often produce good predictions, their accuracy varies, and they can still miss helices present in known structures. Because the thermodynamics of multibranch loops strongly influence these predictions, we examine how changes to the multiloop initiation and branching penalties affect prediction performance, focusing specifically on recall. Using a recent algorithm that partitions the branching-parameter space, we generate all distinct optimal structures obtainable under different choices of the multiloop parameters. We then evaluate the recall of these alternative structures for the Archive II dataset. Our results show that many sequences admit multiple alternative structures with substantially higher recall than the MFE prediction, establishing the predictive potential of multiloop reparameterization. We next introduce an energy-based filtering method that retains only those structures whose adjusted residual energy is at least as good as that of the MFE structure. This produces a tractable number of candidates while preserving most of the achievable improvements in recall. Compared with Boltzmann sampling, the resulting ensemble typically provides a more favorable balance between recall and precision at the level of helix classes despite being much smaller, making it particularly useful for identifying lower-probability structural features. Overall, our results show that examining branching configurations optimal modulo the branching energy provides structural information beyond the standard MFE prediction. The proposed energy-filtering approach yields a compact set of alternative structural hypotheses that can complement Boltzmann sampling and provide a practical source of candidate helices for downstream computational or experimental analysis.

The interactions among condensate-forming biomolecules dictate both the specificity and properties of these condensates, including their fluid-like nature and material exchange dynamics among condensate droplets. While interaction specificity is typically associated with mean interaction lifetimes, the role of interaction lifetime distributions in shaping condensate behavior remains unexplored. This is a critical gap where extensive research has focused on interaction strengths and mean lifetimes. Using a heuristic modeling approach, we show that independent and sequential, multi-step binding-unbinding interactions between protein molecules lead to similar average interaction lifetimes but fundamentally different lifetime distributions, exponential and truncated power-law, respectively. Combining the binding-unbinding models with Brownian dynamics simulations, our findings show that an alteration in the binding-unbinding interaction mechanism in a protein-specific system impacts the exchange dynamics, aging, and size distribution of condensates, even when mean interaction lifetimes remain constant. Our work demonstrates a link between binding-unbinding mechanisms, lifetime distributions, and features of condensates.