Biology subjects

Hocky, G. M.

Publications and source records attributed to Hocky, G. M..

14 recordsLinked to original sources

bioRxiv2026

Force-modulated structural landscape of the catch bonding F-actin crosslinker α-actinin-4

Catch bonds, noncovalent supramolecular interactions whose lifetimes are increased by force, are ubiquitous in mechanical signaling pathways. The structural mechanisms of catch-bonding proteins remain unclear, hampering efforts to decipher how they are dysregulated in disease and exploit them therapeutically. The crosslinker -actinin-4 (ACTN4) forms catch bonds with actin filaments (F-actin) to support the function of kidney podocytes, and its force-insensitive K255E variant causes autosomal dominant focal segmental glomerulosclerosis (FSGS). Using cryo-electron microscopy (cryo-EM), we find that wild-type ACTN4 engages F-actin in two modes, which biochemical experiments and molecular dynamics simulations assign as strong- and weak-binding states, while K255E ACTN4 only populates the strong binding state. By implementing a cryo-EM platform for applying tension across crosslinker-F-actin interfaces using myosin motors, we find that force promotes a weak-to-strong binding transition for wild-type ACTN4, consistent with a two-state catch bond model. Beyond providing mechanistic insight into how the K255E mutation disrupts ACTN4 F-actin catch-bonding in FSGS, this approach enables structural dissection of force-sensitive actin-binding proteins.

biophysics↗

bioRxiv2025

Tutorial on quantifying and sampling biomolecular ensembles withShapeGMM

Here we present a detailed workflow for clustering and enhanced sampling of biomolecular conformations using the ShapeGMM methodology. This approach fits a probabilistic model of biomolecular conformations rooted in the idea that the free energy can expressed in terms of local fluctuations in atomic positions around metastable states. We demonstrate using a single model system how to generate and fit equilibrium molecular dynamics simulation data. We then show how to use the resulting model to generate a reaction coordinate between two states, how to sample along that coordinate using Metadynamics using our size-and-shape PLUMED module, and how to cluster those biased conformations to give a refined equilibrium ShapeGMM model.

biophysics↗

bioRxiv2025

Peptidoglycan turnover promotes active transport of protein through the bacterial cell wall

The bacterial cell wall is a critical load-bearing structure, but is not thought to be an important permeability barrier since proteins freely diffuse through isolated cell wall sacculi and bacteria secrete proteins without the aid of any known channels or transporters in the wall. Using new genetically encoded probes to measure the permeability of the cell in situ at the single-cell level, we discovered that the size threshold determining whether proteins can pass through the Bacillus subtilis sacculus is smaller than was previously thought. We found that transport of small proteins (<10 kDa) through the sacculus was consistent with passive diffusion through discrete pores, while larger proteins (>15 kDa) required the generation of larger pores by inducing peptidoglycan hydrolysis unbalanced by synthesis. These data are consistent with physics-based models of diffusion through a random percolation network of finite thickness. Conversely, the ability of the innate immune factor phospholipase (15.2 kDa) to kill B. subtilis was inhibited by membrane de-polarization. The protective effect of de-polarization was dependent on latent peptidoglycan synthesis (decoupled from cell growth) by PBP1 - highlighting a new role for this enzyme - and on reduced peptidoglycan hydrolysis. These results demonstrate that the rapid peptidoglycan turnover that drives cell growth also promotes movement of phospholipase across the cell wall, identifying a quintessentially bacterial mechanism of active transport. Significance StatementGram-positive bacteria, which include many serious pathogens like Staphylococcus aureus, Listeria monocytogenes, and Clostridium difficile, are defined by their thick peptidoglycan cell wall. Here, we demonstrate that this structure is a critical permeability barrier that blocks antibacterial proteins like those used by the human innate immune system. Furthermore, this barrier function depends on the physiological state of the cell: the wall of non-growing cells is less permeable because peptidoglycan turnover during growth actively promotes transport of specific proteins through the cell wall. This prokaryotic paradigm for molecular transport has important implications for host-pathogen interactions since pathogenic bacteria often assume both non-growing and growing states during infection.

microbiology↗

bioRxiv2025

Using Time Dependent Rate Analysis to Evaluate the Quality of Machine Learned Reaction Coordinates for Biasing and Computing Kinetics

Having an accurate reaction coordinate (RC) is essential for reliable kinetic characterization of molecular processes, but there are few quantitative metrics to evaluate RC quality. In this study, we consider the dimensionless{gamma} metric from the Exponential Average Time-dependent Rate (EATR) method, which represents the fraction of a biasing potential along the RC that contributes to increasing the rate constant. We demonstrate that{gamma} can be used to test whether the utility of a reaction coordinate for predicting kinetics with a Meta-dynamics bias improves as the coordinate is iteratively updated to include new data. We evaluate reaction coordinates approximated via the iterative State Predictive Information Bottleneck (SPIB) approach, which was previously shown to be accurate across six protein-ligand dissociation systems. For these same systems, we compute{gamma} values and mean accelerated times [Formula]. After systematically scanning over fitting parameters, the results show that{gamma} increases closer to 1, while [Formula] decreases, revealing a consistent inverse correlation. These results demonstrate that{gamma} serves as a practical criterion for RC evaluation and offers guidance for selecting SPIB-derived coordinates yielding quantitative kinetic predictions.

biophysics↗

bioRxiv2025

Improved data-driven collective variables for biased sampling through iteration on biased data

Our ability to efficiently sample conformational transitions between two known states of a biomolecule using collective variable (CV) based sampling depends strongly on the choice of the CV. We previously reported a data-driven approach to clustering biomolecular configurations with a probabilistic clustering model termed ShapeGMM. ShapeGMM is a Gaussian Mixture Model in cartesian coordinates, with means and covariances in each cluster representing the harmonic approximation to the conformational ensemble around a metastable state. We subsequently showed that Linear Discriminant Analysis on positions (posLDA) is a good reaction coordinate to characterize the transition between two of these states, and moreover can be biased to produce transitions between the states using Metadynamics-like approaches. However, the quality of these LDA coordinates depends on the amount of data used to characterize the states, and here we demonstrate the ability to systematically improve them using enhanced sampling data. Specifically, we demonstrate that improved CVs for sampling can be generated by iteratively performing biased sampling along a posLDA coordinate and then generating a new shapeGMM model from biased data in the previous iteration. The new coordinates derived from our iterative approach show a substantial improvement in being able to induce transitions between metastable states, and to converge a free energy surface.

biophysics↗

bioRxiv2024

A direct computational assessment of vinculin-actin unbinding kinetics reveals catch bonding behavior

Vinculin forms a catch bond with the cytoskeletal polymer actin, displaying an increased bond lifetime upon force application. Notably, this behavior depends on the direction of the applied force, which has significant implications for cellular mechanotransduction. In this study, we present a comprehensive molecular dynamics simulation study, employing enhanced sampling techniques to investigate the thermodynamic, kinetic, and mechanistic aspects of this phenomenon at physiologically relevant forces. We dissect a catch bond mechanism in which force shifts vinculin between either a weakly- or strongly-bound state. Our results demonstrate that models for these states have unbinding times consistent with those from single-molecule studies, and suggest that both have some intrinsic catch bonding behavior. We provide atomistic insight into this behavior, and show how a directional pulling force can promote the strong or weak state. Crucially, our strategy can be extended to capture the difficult-to-capture effects of small mechanical forces on biomolecular systems in general, and those involved in mechanotransduction more specifically.

biophysics↗

bioRxiv2024

Beta-barrel proteins dictate the effect of core oligosaccharide composition on outer membranemechanics

The outer membrane is the defining structure of Gram-negative bacteria. We previously demonstrated that it is critical for the mechanical integrity of the cell envelope and therefore to the robustness of the bacterial cell as a whole. Here, to determine the key molecules and moieties within the outer membrane that underlie its contribution to cell envelope mechanics, we measured cell-envelope stiffness across several sets of mutants with altered outer-membrane sugar content, protein content, and electric charge. To decouple outer membrane stiffness from total cell envelope stiffness, we developed a novel microfluidics-based "osmotic force extension" assay. In tandem, we developed a simple method to increase throughput of microfluidics experiments by performing them on color-coded pools of mutants. Using Escherichia coli as a model Gram-negative bacterium, we found that truncating the core oligosaccharide, deleting the {beta}-barrel protein OmpA, or deleting lipoprotein outer membrane-cell wall linkers all had the same modest, convergent effect on total cell-envelope stiffness but had large, varying effects on the ability of the cell wall to transfer tension to the outer membrane during large hyperosmotic shocks. Surprisingly, altering lipid A charge had little effect on the mechanical properties of the envelope. Importantly, the presence or absence of OmpA determined whether truncating the core oligosaccharide decreased or increased envelope stiffness (respectively), revealing sign epistasis between these components. Based on these data we propose a specific structural model in which the chemical interactions between lipopolysaccharides, {beta}-barrel proteins, and phospholipids coordinately determine cell envelope stiffness, and the ability of the outer membrane to functionally share mechanical loads with the cell wall. Statement of SignificanceThe outer membrane is the defining cellular structure of Gram-negative bacteria, a group that contains many important pathogens like Escherichia coli, Vibrio cholerae, and Pseudomonas aeruginosa. One role of the outer membrane is to block the uptake of small molecules like antibiotics. However, it is becoming increasingly clear that it also functions as a structural exoskeleton that is critical for the cells ability to cope with internal and external mechanical forces. Here, we carefully dissect the molecular basis for the load-bearing capacity of the outer membrane by screening a set of mutants with a new cell biophysics assay.

biophysics↗

bioRxiv2024

Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM

Quantifying the conformational ensembles of biomolecules is fundamental to describing mechanisms of processes such as ligand binding and allosteric regulation. Accurate quantification of these ensembles remains a challenge for all but the simplest molecules. One such challenge is insufficient sampling which enhanced sampling approaches, such as metadynamics, were designed to overcome; however, the non-uniform frame weights that result from many of these approaches present an additional challenge to ensemble quantification techniques such as Markov State Modeling or structural clustering. Here, we present rigorous inclusion of non-uniform frame weights into a structural clustering method entitled shapeGMM. The shapeGMM method fits a Gaussian mixture model to particle positions, and here we advance that approach by incorporating nonuniform frame weights in the estimates of all parameters of the model. The resulting models are high dimensional probability densities for the unbiased systems from which we can compute important thermodynamic properties such as relative free energies and configurational entropy. The accuracy of this approach is demonstrated by the quantitative agreement between GMMs computed by Hamiltonian reweighting and direct simulation of a coarse-grained helix model system. Furthermore, the relative free energy computed from a high dimensional probability density of alanine dipeptide reweighted from a metadynamics simulation quantitatively reproduces the metadynamics free energy in the basins. Finally, the method identifies hidden structures along the actin globular to filamentous-like structural transition from a metadynamics simulation on a linear discriminant analysis coordinate trained on GMM states, demonstrating the broad applicability of combining our prior and new methods, and illustrating how structural clustering of biased data can lead to biophysical insight. Combined, these results demonstrate that frame-weighted shapeGMM is a powerful approach to quantify biomolecular ensembles from biased simulations.

biophysics↗

bioRxiv2023

Mesoscale molecular assembly is favored by the active, crowded cytoplasm

The mesoscale organization of molecules into membraneless biomolecular condensates is emerging as a key mechanism of rapid spatiotemporal control in cells1. Principles of biomolecular condensation have been revealed through in vitro reconstitution2. However, intracellular environments are much more complex than test-tube environments: They are viscoelastic, highly crowded at the mesoscale, and are far from thermodynamic equilibrium due to the constant action of energy-consuming processes3. We developed synDrops, a synthetic phase separation system, to study how the cellular environment affects condensate formation. Three key features enable physical analysis: synDrops are inducible, bioorthogonal, and have well-defined geometry. This design allows kinetic analysis of synDrop assembly and facilitates computational simulation of the process. We compared experiments and simulations to determine that macromolecular crowding promotes condensate nucleation but inhibits droplet growth through coalescence. ATP-dependent cellular activities help overcome the frustration of growth. In particular, actomyosin dynamics potentiate droplet growth by reducing confinement and elasticity in the mammalian cytoplasm, thereby enabling synDrop coarsening. Our results demonstrate that mesoscale molecular assembly is favored by the combined effects of crowding and active matter in the cytoplasm. These results move toward a better predictive understanding of condensate formation in vivo.

biophysics↗

bioRxiv2020

Actin crosslinker competition and sorting drive emergent GUV size-dependent actin network architecture

How complex actin network architectures arise and coexist in membrane-enclosed cell environment remains unknown. By encapsulating actin and crosslinking proteins -actinin and fascin in giant unilamellar vesicles (GUVs), we show that physical confinement and its size can lead to formation of complex actin structures, including rings and asters at GUV peripheries and centers. Strikingly, we find that the materials properties of the aster structures depend on the ratio of the relative concentrations of -actinin and fascin, and we demonstrate that this results from -actinin and fascin sorting into separate domains in the structures. We complement our experiments with molecular simulations that capture the spontaneous formation of competing network architectures; these provide a microscopic view of the dynamics and delineate the molecular features that promote sorting. We propose that the observed boundary-imposed effect on protein sorting is a general mechanism for creating emergent structures in biopolymer networks with multiple crosslinkers.

biophysics↗

bioRxiv2019

The Effective Coarse-grained Hessian Dictates Allostery in IGPS

The long-ranged coupling between residues that gives rise to allostery in a protein is built up from short-ranged physical interactions. Computational tools used to predict this coupling and its functional relevance have relied on the application of graph theoretical metrics to residue-level correlations measured from all-atom molecular dynamics (aaMD) simulations. The short-ranged interactions that yield these long-ranged residue-level correlations are quantified by the effective coarse-grained Hessian. Here we compute an effective harmonic coarse-grained Hessian from aaMD simulations of a benchmark allosteric protein, IGPS, and demonstrate the improved locality of this graph Laplacian over two other connectivity matrices. Additionally, two centrality metrics are developed that indicate the direct and indirect importance of each residue at producing the covariance between the effector binding pocket and the active site. The residue importance indicated by these two metrics is corroborated by previous mutagenesis experiments and leads to unique functional insights; in contrast to previous computational analyses, our results suggest that fP76-hK181 is the most important contact for conveying direct allosteric paths across the HisF-HisH interface. The connectivity around fD98 is found to be important at affecting allostery through indirect means.

biophysics↗

bioRxiv2019

A burst of genetic innovation in actin-related proteins (Arps) for testis-specific function in a Drosophila lineage

Many cytoskeletal proteins form the core of fundamental biological processes and are evolutionarily ancient. For example, the superfamily of actin-related proteins (Arps) specialized early in eukaryotic evolution for diverse cellular roles in the cytoplasm and the nucleus. Despite its strict conservation across eukaryotes, we find that the Arp superfamily has undergone dramatic lineage-specific diversification in Drosophila. Our phylogenomic analyses reveal four independent Arp gene duplications that originated in the common ancestor of the obscura group of Drosophila species and have been mostly preserved in this lineage. All four Arp paralogs have evolved under positive selection and are predominantly expressed in the male germline. We focus our analyses on the divergent Arp2D paralog, which arose via a retroduplication event from Arp2, a component of the 7-membered Arp2/3 complex that polymerizes branched actin networks. Computational modeling analyses suggest that Arp2D should be able to replace Arp2 in the Arp2/3 complex and bind daughter actin monomers, suggesting that Arp2D may augment Arp2s functions in the male germline. We find that Arp2D is expressed during and following meiosis in the male germline, where it localizes to distinct locations such as actin cones-pecialized cytoskeletal structures that separate bundled spermatids into individual mature sperm. We hypothesize that this unprecedented burst of genetic innovation in cytoskeletal proteins may have been driven by the evolution of sperm heteromorphism in the obscura group of Drosophila.

evolutionary biology↗

bioRxiv2018

Mechanical and kinetic factors drive sorting of F-actin crosslinkers on bundles

In cells, actin binding proteins (ABPs) sort to different regions in order to establish F-actin networks with diverse functions, including filopodia used for cell migration, or contractile rings required for cell division. Recent experimental work uncovered a passive mechanism that may facilitate spatial localization of ABPs: binding of a short crosslinker protein to two actin filaments promotes the binding of other short crosslinkers and inhibits the binding of longer crosslinkers (and vice versa). We hypothesize this sorting arises because F-actin is semiflexible and cannot bend over short distances. We develop a mathematical theory and a kinetic Monte Carlo simulation encompassing the most important physical parameters for this process, and use simulations of a coarse-grained but molecularly explicit model to characterize and test our predictions about the interplay of mechanical and kinetic parameters. Our theory and data predict an explicit dependence of crosslinker separation on bundle polymerization rate. We perform experiments that confirm a dependence on polymerization rate, but in an unanticipated non-monotonic manner. We use simulations to show that this non-monotonic behavior can arise in situations where crosslinkers have equal bundling affinity at equilibrium, but differing microscopic binding rates to filaments. This dependence of sorting on actin polymerization rate is a non-equilibrium effect, qualitatively similar to non-equilibrium domain formation in materials growth. Thus our results reveal an avenue by which cells can organize molecular material to drive biological processes, and can also guide the choice and design of crosslinkers for engineered protein-based materials.

biophysics↗

bioRxiv2018

Insights into the cooperative nature of ATP hydrolysis in actin filaments

Actin filaments continually assemble and disassemble within a cell. Assembled filaments \"age\" as a bound nucleotide ATP within each actin subunit quickly hydrolyzes, followed by a slower release of the phosphate Pi, leaving behind a bound ADP. This subtle change in nucleotide state of actin subunits affects filament rigidity as well as its interactions with binding partners. We present here a systematic multiscale ultra-coarse-graining (UCG) approach that provides a computationally efficient way to simulate a long actin filament undergoing ATP hydrolysis and phosphate release reactions, while systematically taking into account available atomistic details. The slower conformational changes and their dependence on the chemical reactions are simulated with the UCG model by assigning internal states to the coarse-grained sites. Each state is represented by a unique potential surface of a local heterogeneous elastic network. Internal states undergo stochastic transitions that are coupled to conformations of the underlying molecular system. The UCG model reproduces mechanical properties of the filament and allows us to study whether fluctuations in actin subunits produce cooperative aging in the filament. Our model predicts that nucleotide state of neighboring subunit significantly modulates the reaction kinetics, implying cooperativity in ATP hydrolysis and Pi release. We further systematically coarse-grain the system into a Markov state model that incorporates assembly and disassembly, facilitating a direct comparison with previously published models. We find that cooperativity in ATP hydrolysis and Pi release significantly affects the filament growth dynamics only near the critical G-actin monomer concentration, while both cooperative and random mechanisms show similar growth dynamics far from the critical concentration. In contrast, filament composition in terms of the bound nucleotide distribution varies significantly at all monomer concentrations studied. These results provide new insights into the cooperative nature of ATP hydrolysis and Pi release and the implications it has for actin filament properties, providing novel predictions for future experimental studies.

biophysics↗