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bioRxiv2023

The assembly landscape of the complete B-repeat superdomain from Staphylococcus epidermidis strain 1457

The accumulation-associated protein (Aap) is the primary determinant of Staphylococcus epidermidis device-related infections. The B-repeat superdomain is responsible for intercellular adhesion that leads to the development of biofilms occurring in such infections. It was recently demonstrated that Zn-induced B-repeat assembly leads to formation of functional amyloid fibrils, which offer strength and stability to the biofilm. Rigorous biophysical studies of Aap B-repeats from S. epidermidis strain RP62A revealed Zn-induced assembly into a stable, reversible dimer and tetramer, prior to aggregation into amyloid fibrils. Genetic manipulation is not tractable for many S. epidermidis strains, including RP62A; instead, many genetic studies have used strain 1457. Therefore, to better connect findings from biophysical and structural studies of B-repeats to in vivo studies, the B-repeat superdomain from strain 1457 was examined. Differences between the B-repeats from strain RP62A and 1457 include the number of B-repeats, which has been shown to play a critical role in assembly into amyloid fibrils, as well as the distribution of consensus and variant B-repeat subtypes, which differ in assembly competency and thermal stability. Detailed investigation of the Zn-induced assembly of the full B-repeat supderdomain from strain 1457 was conducted using analytical ultracentrifugation. Whereas the previous construct from RP62A (Brpt5.5) formed a stable tetramer prior to aggregation, Brpt6.5 from 1457 forms extremely large stable species on the order of 28- and 32-mers, prior to aggregation into similar amyloid fibrils. Importantly, both assembly pathways proceed through the same mechanism of dimerization and tetramerization, and both conclude with the formation of amyloid-like fibrils. Theoretical discussions of the energy landscape of B-repeats from different strains and of different length are provided with considerations of biological implications. Statement of SignificanceStaphylococcus epidermidis is a major pathogen responsible for device-related infections. The primary factor responsible for such infections is the accumulation-associated protein, Aap, through its ability to mediate formation of dense surface-adherent communities of bacteria known as biofilms. Our lab recently demonstrated that the B-repeat superdomain of Aap from strain RP62A undergoes Zn-dependent assembly to form functional amyloid fibrils that improve the strength and resilience of biofilms in vitro. These amyloid fibrils may be responsible for the difficulty in treating device-related infections. However, strain 1457 is commonly used for genetic manipulation. In this manuscript, the Zn-dependent assembly of B-repeats from strain 1457 is shown to lead to the same outcome of amyloidogenesis, although it occurs through different intermediate oligomeric states.

biophysics↗

bioRxiv2023

Effects of Alzheimer's Disease Drug Candidates on Disordered Aβ42 Dissected by Comparative Markov State Analysis (CoVAMPnet)

Computational study of the effect of drug candidates on intrinsically disordered biomolecules is challenging due to their vast and complex conformational space. Here we developed a Comparative Markov State Analysis (CoVAMPnet) framework to quantify changes in the conformational distribution and dynamics of a disordered biomolecule in the presence and absence of small organic drug candidate molecules. First, molecular dynamics trajectories are generated using enhanced sampling, in the presence and absence of small molecule drug candidates, and ensembles of soft Markov state models (MSMs) are learned for each system using unsupervised machine learning. Second, these ensembles of learned MSMs are aligned across different systems based on a solution to an optimal transport problem. Third, the directional importance of inter-residue distances for the assignment to different conformational states is assessed by a discriminative analysis of aggregated neural network gradients. This final step provides interpretability and biophysical context to the learned MSMs. We applied this novel computational framework to assess the effects of ongoing phase 3 therapeutics tramiprosate (TMP) and its metabolite 3-sulfopropanoic acid (SPA) on the disordered A{beta}42 peptide involved in Alzheimers disease. Based on adaptive sampling molecular dynamics and CoVAMPnet analysis, we observed that both TMP and SPA preserved more structured conformations of A{beta}42 by interacting non-specifically with charged residues. SPA impacted A{beta}42 more than TMP, protecting -helices and suppressing the formation of aggregation-prone {beta}-strands. Experimental biophysical analyses showed only mild effects of TMP/SPA on A{beta}42, and activity enhancement by the endogenous metabolization of TMP into SPA. Our data suggest that TMP/SPA may also target other biomolecules than A{beta} peptides. The CoVAMPnet method is broadly applicable to study the effects of drug candidates on the conformational behavior of intrinsically disordered biomolecules. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/523007v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@13eea16org.highwire.dtl.DTLVardef@17a6bd1org.highwire.dtl.DTLVardef@3c6b33org.highwire.dtl.DTLVardef@a20444_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Inferring membrane properties during clathrin-mediated endocytosis using machine learning

Endocytosis is a fundamental cellular process for eukaryotic cells to transport molecules into the cell. To understand the molecular mechanisms behind the process, researchers have obtained abundant biochemical information about the protein dynamics involved in endocytosis via fluorescence microscopy and geometric information about membrane shapes via electron tomography. However, measuring the biophysical information, such as the osmotic pressure and the membrane tension, remains a problem due to the small dimension of the endocytic invagination. In this work, we combine Machine Learning and Helfrich model of the membrane, as well as the dataset of membrane shapes extracted from the electron tomography to infer biophysical information about endocytosis. Our results show that Machine Learning is able to find solutions that both match the experimental profile and fulfill the membrane shape equations. Furthermore, we show that at the early stage of endocytosis, the inferred membrane tension is negative, which implies strong compressive forces acting at the boundary of the endocytic invagination. This method provides a generic framework to extract membrane information from the super-resolution imaging. SIGNIFICANCEEndocytosis is a fundamental cellular process that has been extensively studied with the help of fluorescence microscopy and electron microscopy. A large amount of data has been accumulated about the protein dynamics and the membrane shapes. In this work, we combine the widely used Helfrich model and experimental data of membrane shapes to infer the physical information about endocytosis, including the membrane tension and the osmotic pressure. Our work not only proves Machine Learning as a power tool is able to solve the complicated membrane shape equations, but also provides novel biological insights about the initiation of endocytosis in yeast cells.

biophysics↗

bioRxiv2023

A quantum walk model of Bicoid morphogen formation and interpretation

The establishment and interpretation of the concentration distribution of the morphogen Bicoid (BCD) is considered crucial for the successful embryonic development of fruit flies. However, the biophysical mechanisms behind the timely formation and subsequent interpretation of the BCD morphogen by its target genes are not yet completely understood. Here a discrete-time, one-dimensional quantum walk model of BCD gradient formation is used to explain both the observed values of diffusivity and its precise interpretation. It is shown that the decoding of positional information from the BCD morphogen by its primary target gene hb, with the observed precision of [~] 10%, takes a time period of less than a second, as expected on the basis of recent experimental observations. From this the on-rate (kon) for the binding of BCD to its target loci is obtained. Furthermore, the model is also used to explain certain key observations of recent optogenetic experiments concerning the time windows for BCD interpretation. Finally, it is argued that the presented model represents a significant step in the utilization of quantum computation-based techniques in studying the dynamics of biological systems in general and in the field of developmental biophysics in particular.

biophysics↗

bioRxiv2023

Switch-like Compaction of Poly(ADP-ribose) Upon Cation Binding

Poly(ADP-ribose) (PAR) is a homopolymer of adenosine diphosphate ribose that is added to proteins as a post-translational modification to regulate numerous cellular processes. PAR also serves as a scaffold for protein binding in macromolecular complexes, including biomolecular condensates. It remains unclear how PAR achieves specific molecular recognition. Here, we use single-molecule fluorescence resonance energy transfer (smFRET) to evaluate PAR flexibility under different cation conditions. We demonstrate that, compared to RNA and DNA, PAR has a longer persistence length and undergoes a sharper transition from extended to compact states in physiologically relevant concentrations of various cations (Na+, Mg2+, Ca2+, and spermine). We show that the degree of PAR compaction depends on the concentration and valency of cations. Furthermore, the intrinsically disordered protein FUS also served as a macromolecular cation to compact PAR. Taken together, our study reveals the inherent stiffness of PAR molecules, which undergo switch-like compaction in response to cation binding. This study indicates that a cationic environment may drive recognition specificity of PAR. SignificancePoly(ADP-ribose) (PAR) is an RNA-like homopolymer that regulates DNA repair, RNA metabolism, and biomolecular condensate formation. Dysregulation of PAR results in cancer and neurodegeneration. Although discovered in 1963, fundamental properties of this therapeutically important polymer remain largely unknown. Biophysical and structural analyses of PAR have been exceptionally challenging due to the dynamic and repetitive nature. Here, we present the first single-molecule biophysical characterization of PAR. We show that PAR is stiffer than DNA and RNA per unit length. Unlike DNA and RNA which undergoes gradual compaction, PAR exhibits an abrupt switch-like bending as a function of salt concentration and by protein binding. Our findings points to unique physical properties of PAR that may drive recognition specificity for its function.

biophysics↗

bioRxiv2023

Asymmetric oligomerization state and sequence patterning can tune multiphase condensate miscibility

Endogenous biomolecular condensates, comprised of a multitude of proteins and RNAs, can organize into multiphasic structures, with compositionally-distinct phases. This multiphasic organization is generally understood to be critical for facilitating their proper biological function. However, the biophysical principles driving multiphase formation are not completely understood. Here, we utilize in vivo condensate reconstitution experiments and coarse-grained molecular simulations to investigate how oligomerization and sequence interactions modulate multiphase organization in biomolecular condensates. We demonstrate that increasing the oligomerization state of an intrinsically disordered protein region (IDR) results in enhanced immiscibility and multiphase formation. Interestingly, we found that oligomerization tunes the miscibility of IDRs in an asymmetric manner, with the effect being more pronounced when the IDR exhibiting stronger homotypic IDR interactions is oligomerized. Our findings suggest that oligomerization is a flexible biophysical mechanism which cells can exploit to tune the internal organization of biomolecular condensates and their associated biological functions.

biophysics↗

bioRxiv2023

Phase separation and molecular ordering of the prion-like domain of the thermosensory protein EARLY FLOWERING 3

Liquid-liquid phase separation (LLPS) is an important mechanism enabling the dynamic compartmentalisation of macromolecules, including complex polymers such as proteins and nucleic acids, and occurs as a function of the physicochemical environment. In the model plant, Arabidopsis thaliana, LLPS by the protein EARLY FLOWERING3 (ELF3) occurs in a temperature sensitive manner and controls thermoresponsive growth. ELF3 contains a largely unstructured prion-like domain (PrLD) that acts as a driver of LLPS in vivo and in vitro. The PrLD contains a poly-glutamine (polyQ) tract, whose length varies across natural Arabidopsis accessions. Here, we use a combination of biochemical, biophysical and structural techniques to investigate the dilute and condensed phases of the ELF3 PrLD with varying polyQ lengths. We demonstrate that the dilute phase of the ELF3 PrLD forms a monodisperse higher order oligomer that does not depend on the presence of the polyQ sequence. This species undergoes LLPS in a pH and temperature-sensitive manner and the polyQ region of the protein tunes the initial stages of phase separation. The liquid phase rapidly undergoes aging and forms a hydrogel as shown by fluorescence and atomic force microscopies. Furthermore, we demonstrate that the hydrogel assumes a semi-ordered structure as determined by small angle X-ray scattering, electron microscopy and X-ray diffraction. These experiments demonstrate a rich structural landscape for a PrLD protein and provide a framework to describe the structural and biophysical properties of biomolecular condensates.

biophysics↗

bioRxiv2023

Can a bulky glycocalyx promote catch bonding in early integrin adhesion? Perhaps a bit

Many types of cancer overexpress bulky glycoproteins to form a thick glycocalyx layer. The glycocalyx physically separates the cell from its surroundings, but recent work has shown that the glycocalyx can paradoxically increase adhesion to soft tissues and therefore promote the metastasis of cancer cells. This surprising phenomenon occurs because the glycocalyx forces adhesion molecules (called integrins) on the cells surface into clusters. These integrin clusters have cooperative effects that allow them to form stronger adhesions to surrounding tissues than would be possible with equivalent numbers of un-clustered integrins. These cooperative mechanisms have been intensely scrutinized in recent years; a more nuanced understanding of the biophysical underpinnings of glycocalyx-mediated adhesion could uncover therapeutic targets, deepen our general understanding of cancer metastasis, and elucidate general biophysical processes that extend far beyond the realm of cancer research. This work examines the hypothesis that the glycocalyx has the additional effect of increasing mechanical tension experienced by clustered integrins. Integrins function as mechanosensors that undergo catch bonding - meaning the application of moderate tension increases integrin bond lifetime relative to the lifetime of integrins experiencing low tension. In this work, a three-state chemomechanical catch bond model of integrin tension is used to investigate catch bonding in the presence of a bulky glycocalyx. This modeling suggests that a bulky glycocalyx can lightly trigger catch bonding, increasing the bond lifetime of integrins at adhesion edges by up to 100%. The total number of integrin-ligand bonds within an adhesion is predicted to increase by up to ~60% for certain adhesion geometries. Catch bonding is predicted to decrease the activation energy of adhesion formation by ~1-4 kBT, which translates to a ~3-50x increase in the kinetic rate of adhesion nucleation. This work reveals that integrin mechanic and clustering likely both contribute to glycocalyx-mediated metastasis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=185 HEIGHT=200 SRC="FIGDIR/small/532909v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@5dc213org.highwire.dtl.DTLVardef@170ac28org.highwire.dtl.DTLVardef@36873eorg.highwire.dtl.DTLVardef@1e92ef2_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Active Transcription and Epigenetic Reactions Synergistically RegulateMeso-Scale Genomic Organization

In interphase nuclei, chromatin is organized into interspersed dense domains with characteristic sizes, both in the nuclear interior and periphery. However, the quantitative impact of transcription and histone modifications on the size and distribution of these domains remains unclear. Here, we introduce a mesoscale theoretical model that investigates the relationship between heterochromatic domain sizes and loop extrusion rates from these domains. The model considers chromatin-chromatin and chromatin-lamina interactions, methylation and acetylation kinetics, and diffusion of epigenetic marks and nucleoplasm. Our model generates testable predictions that help reveal the biophysics underlying chromatin organization in the presence of transcription-driven loop extrusion. This process is kinetically captured through the conversion of heterochromatin to euchromatin in response to RNAPII activity. We discovered that a balance between diffusive and reactive fluxes governs the steady-state sizes of heterochromatin domains. Using theory and simulations, we predicted that a loss of transcription results in increased chromatin compaction and larger heterochromatin domain sizes. To validate our predictions, we employed complementary super-resolution and nano-imaging techniques on five different cell lines with impaired transcription. We quantitatively assessed how domain sizes scale with loop extrusion rates at the hetero-euchromatin interfaces. Our analysis of previously obtained super-resolution images of nuclei revealed that excessive loop extrusion leads to smaller heterochromatin domains. The model successfully recapitulated these observations, explaining how transcription loss can counteract the effects of cohesin overloading. As the general biophysical mechanisms regulating heterochromatin domain sizes are independent of cell type, our findings have significant implications for understanding the role of transcription in global genome organization.

biophysics↗

bioRxiv2023

Dark nanodiscs as a model membrane for evaluating membrane protein thermostability by differential scanning fluorimetry

Measuring protein thermostability provides valuable information on the biophysical rules that govern structure-energy relationships of proteins. However, such measurements remain a challenge for membrane proteins. Here, we introduce a new experimental system to evaluate membrane protein thermostability. This system leverages a recently-developed non-fluorescent membrane scaffold protein (MSP) to reconstitute proteins into nanodiscs and is coupled with a nano-format of differential scanning fluorimetry (nanoDSF). This approach offers a label-free and direct measurement of the intrinsic tryptophan fluorescence of the membrane protein as it unfolds in solution without signal interference from the "dark" nanodisc. In this work, we demonstrate the application of this method using the disulfide bond formation protein B (DsbB) as a test membrane protein. NanoDSF measurements of DsbB reconstituted in dark nanodiscs show a complex biphasic thermal unfolding pattern in the presence of lipids with a minor unfolding transition followed by a major transition. The inflection points of the thermal denaturation curve reveal two distinct unfolding midpoint melting temperatures (Tm) of 70.5 {degrees}C and 77.5 {degrees}C, consistent with a three-state unfolding model. Further, we show that the catalytically conserved disulfide bond between residues C41 and C130 drives the intermediate state of the unfolding pathway for DsbB in a nanodisc. We introduce this method as a new tool that can be used to understand how compositionally, and biophysically complex lipid environments drive membrane protein stability.

biophysics↗

bioRxiv2023

Marginated aberrant red blood cells induce pathologic vascular stress fluctuations in a computational model of hematologic disorders

Red blood cell (RBC) disorders affect billions worldwide. While alterations in the physical properties of aberrant RBCs and associated hemodynamic changes are readily observed, in conditions such as sickle cell disease and iron deficiency, RBC disorders can also be associated with vascular dysfunction. The mechanisms of vasculopathy in those diseases remain unclear and scant research has explored whether biophysical alterations of RBCs can directly affect vascular function. Here we hypothesize that the purely physical interactions between aberrant RBCs and endothelial cells, due to the margination of stiff aberrant RBCs, play a key role in this phenomenon for a range of disorders. This hypothesis is tested by direct simulations of a cellular scale computational model of blood flow in sickle cell disease, iron deficiency anemia, COVID-19, and spherocytosis. We characterize cell distributions for normal and aberrant RBC mixtures in straight and curved tubes, the latter to address issues of geometric complexity that arise in the microcirculation. In all cases aberrant RBCs strongly localize near the vessel walls (margination) due to contrasts in cell size, shape, and deformability from the normal cells. In the curved channel, the distribution of marginated cells is very heterogeneous, indicating a key role for vascular geometry. Finally, we characterize the shear stresses on the vessel walls; consistent with our hypothesis, the marginated aberrant cells generate large transient stress fluctuations due to the high velocity gradients induced by their near-wall motions. The anomalous stress fluctuations experienced by endothelial cells may be responsible for the observed vascular inflammation. Significance StatementA common and potentially life-threatening complication of blood cell disorders is inflammation and dysfunction of the vascular wall, for reasons that remain unclear. To address this issue, we explore a purely biophysical hypothesis involving red blood cells using detailed computational simulations. Our results show that red blood cells that are pathologically altered in cell shape, size, and stiffness, which occurs in various blood disorders, strongly marginate, residing primarily in the cell-free layer near blood vessel walls, generating large shear stress fluctuations at the vessel wall that may be responsible for endothelial damage and inflammation.

biophysics↗

bioRxiv2023

Sampling Bottleneck in Validating Membrane Dynamics

Molecular dynamics simulations of lipid membranes have become increasingly impactful in biophysics because they offer atomistic resolution of structural fluctuations in relation to their functional outputs. Yet quantitative characterization of multiscale processes is a formidable challenge due to the distribution of motions that evade analysis of discrete simulation data. Here we investigate the efficient calculation of CH bond relaxation rates from membrane simulations. Widely used computational approaches offer numerical simplicity but fall short of capturing crucial aspects of the orientation dependence of the dynamics. To circumvent this problem, we introduced a robust framework based on liquid crystal theory which considers explicitly the CH bond motions with respect to the director axis (bilayer normal). Analysis of the orientation dependence of the dynamics shows excellent agreement with experiment, illustrating how the ordering potential affects the calculated relaxation rates. Furthermore, a fit-based resampling of the autocorrelation function of the bond fluctuations validates the new approach for low-temporal resolution data. The recovered relaxation rates indicate that at short timescales, both with and without cholesterol, the local motions of CH bonds describe the bilayer microviscosity and resemble liquid hydrocarbons. Our results establish the critical role of the orientational anisotropy in analysis of membrane simulations, explain fundamental aspects of lipid dynamics, and provide guidelines for extracting information that can be compared to experimental data. STATEMENT OF SIGNIFICANCENuclear magnetic resonance data have been historically used to validate membrane simulations through the average order parameters of the lipid chains. However, the bond dynamics that give rise to this equilibrium bilayer structure have rarely been compared between in vitro and in silico systems despite the availability of substantial experimental data. Here we investigate the logarithmic timescales sampled by the lipid chain motions and confirm a recently developed computational protocol that creates a dynamics-based bridge between simulations and NMR spectroscopy. Our results establish the foundations for validating a relatively unexplored dimension of bilayer behavior and thus have far-reaching applications in membrane biophysics.

biophysics↗

bioRxiv2023

Self-propagating wave drives noncanonical antidurotaxis of skull bones in vivo

Cell motility is a key feature of tissue morphogenesis, and it is thought to be driven primarily by the active migration of individual cells or collectives. However, this model is unlikely to apply to cells lacking overt cytoskeletal, stable cell-cell or cell-cell adhesions, and molecular polarity, such as mesenchymal cells. Here, by combining a novel imaging pipeline with biophysical modeling, we discover that during skull morphogenesis, a self-generated collagen gradient expands a population of osteoblasts towards a softer matrix. Biomechanical measurements revealed a gradient of stiffness and collagen along which cells move and divide. The moving cells generate an osteogenic front that travels faster than individual tracked cells, indicating that expansion is also driven by cell differentiation. Through biophysical modeling and perturbation experiments, we found that mechanical feedback between stiffness and cell fate drives bone expansion and controls bone size. Our work provides a mechanism for coordinated motion that does not rely upon the cytoskeletal dynamics of cell migration. We term this self-propagating motion down a stiffness gradient, noncanonical antidurotaxis. Identification of alternative mechanisms of cellular motion will help in understanding how directed cellular motility arises in complex environments with inhomogeneous material properties.

biophysics↗

bioRxiv2023

Design of artificial molecular motor inheriting directionality and scalability.

Realizing artificial molecular motors with autonomous functionality and high performance is a major challenge in biophysics. Such motors not only provide new perspectives in biotechnology but also offer a novel approach for the bottom-up elucidation of biological molecular motors. Directionality and scalability are critical factors for practical applications. However, the simultaneous realization of both remains challenging. In this study, we propose a novel design for a rotary motor that can be fabricated using a currently available technology, DNA origami, and validate its functionality through simulations with practical parameters. We demonstrate that the motor rotates unidirectionally and processively in the direction defined by structural asymmetry, which induces kinetic asymmetry in motor motion. The motor also exhibits scalability, such that increasing the number of connections between the motor and stator allows for a larger speed, run length, and stall force. Statement of SignificanceBiological molecular motors are the most sophisticated nanomachines found in nature. Despite a long history of research, we have yet to understand how they work in detail. A simple and promising approach involves the building of artificial molecular motors. The process of achieving these goals helps us understand how the structures and mechanisms of the biological motors have been optimized through the evolution of their functions. Some artificial motors have already been achieved. However, each realization implements only a part of the characteristics of biological motors. In this work, we propose a novel motor mechanism that simultaneously implements two essential characteristics: scalability and directionality. These results will help us improve the biophysics of molecular motors and contribute to developing high-performance artificial molecular motors with possible industrial applications.

biophysics↗

bioRxiv2023

Influenza A virus infection alters lipid packing and surface electrostatic potential of the host plasma membrane

The pathogenesis of influenza A viruses (IAVs) is influenced by several factors, including IAV strain origin and reassortment, tissue tropism and host type. While such factors were mostly investigated in the context of virus entry, fusion and replication, little is known about the viral-induced changes to the host lipid membranes which might be relevant in the context of virion assembly. In this work, we applied several biophysical fluorescence microscope techniques (i.e., Forster energy resonance transfer, generalized polarization imaging and scanning fluorescence correlation spectroscopy) to quantify the effect of infection by two IAV strains of different origin on the plasma membrane (PM) of avian and human cell lines. We found that IAV infection affects the membrane charge of the inner leaflet of the PM. Moreover, we showed that IAV infection impacts lipid-lipid interactions by decreasing membrane fluidity and increasing lipid packing. Because of such alterations, diffusive dynamics of membrane-associated proteins are hindered. Taken to-gether, our results indicate that the infection of avian and human cell lines with IAV strains of different origins had similar effects on the biophysical properties of the PM.

biophysics↗

bioRxiv2023

Quantifying the Influence of the Actin Cytoskeleton on Ion Transport in Dendritic Spines by Homogenization of the Poisson-Nernst-Planck Equations

Dendritic spines are filled with a very dense actin cytoskeleton. However, due to their small size, the impact of this mesh on biophysical parameters has not been studied so far, and it remains unclear to what extent it might affect ion flow in dendritic spines. Only recently has the three-dimensional internal structure of dendritic spines been quantified in great detail in electron microscopic tomography data. Based on these results, we estimate the effect of the spine actin cytoskeleton on diffusion and permittivity. We apply a method called homogenization to estimate effective diffusion tensors and permittivity tensors in Poisson-Nernst-Planck (PNP) equations. We find that the volume taken up by the intracellular structure alone cannot explain the changes in these biophysical parameters. The characteristic architecture of the intracellular space in dendritic spines will reduce the diffusion of ions by 33% to 46% and the permittivity by 30% to 42%, compared to values found for the cytosol free of intracellular structures. These results can be used to improve computational studies using PNP equations and help to better interpret experimental results of electrical and chemical compartmentalization.

biophysics↗

bioRxiv2023

Unlocking Predictive Power: A Machine Learning Tool Derived from In-Depth Analysis to Forecast the Impact of Missense Variants in Human Filamin C

Cardiomyopathies, diseases of the heart muscle, are a leading cause of heart failure. An increasing proportion of cardiomyopathies have been associated with specific genetic changes, such as mutations in FLNC, the gene that codes for filamin C. Altogether, more than 300 variants of FLNC have been identified in patients, including a number of single point mutations. However, the role of a significant number of these mutations remains unknown. Here, we conducted a comprehensive analysis, starting from clinical data that led to identification of new pathogenic and non-pathogenic FLNC variants. We selected some of these variants for further characterization that included studies of in vivo effects on the morphology of neonatal cardiomyocytes to establish links to phenotype, and the in vitro thermal stability and structure determination to understand biophysical factors impacting function. We used these findings to compile vast datasets of pathogenic and non-pathogenic variant structures and developed a machine-learning-based neural network (AMIVA-F) to predict the impact of single point mutations. AMIVA-F outperformed most commonly used predictors both in disease related as well as neutral variants, approaching [~]80% accuracy. Taken together, our study documents additional FLNC variants, their biophysical and structural properties, and their link to the disease phenotype. Furthermore, we developed a state-of-the-art web-based server AMIVA-F that can be used for accurate predictions regarding the effect of single point mutations in human filamin C, with broad implications for basic and clinical research.

biophysics↗

bioRxiv2023

Increasing the accuracy of single-molecule data analysis using tMAVEN

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physico-chemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule dataset and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from pre-processing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule dataset with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule datasets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physico-chemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule datasets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses. Statement of SignificanceThe power of time-dependent single-molecule biophysical experiments lies in their ability to uncover the molecular mechanisms governing experimental systems by computationally applying kinetic models to the data. While many software solutions have been developed to estimate the optimal parameters of such models, the results reported here show that the models themselves are often inherently mismatched with the molecular mechanisms they are being used to analyze. To investigate these mismatches and demonstrate how to best model the kinetics of a molecular mechanism, we have used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), an open-source software platform we have developed that, among other features, enables the analysis of single-molecule datasets using different kinetic models within a single, extensible, and customizable pipeline.

biophysics↗