SEARCH · bioRxiv Science

Results for “Biophysics”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,621 records · Page 90Linked to original sources

bioRxiv2022

Natural Antimicrobial Peptides Self-assemble as α/β Chameleon Amyloids

Amyloid protein fibrils and some antimicrobial peptides (AMPs) share biophysical and structural properties. This observation suggests that ordered self-assembly can act as an AMP-regulating mechanism, and, vice versa, that human amyloids play a role in host defense against pathogens, as opposed to their common association with neurodegenerative and systemic diseases. Based on previous structural information on toxic amyloid peptides, we developed a sequence-based bioinformatics platform and, led by its predictions, experimentally identified 14 fibril-forming AMPs (ffAMPs) from living organisms, which demonstrated cross-{beta} and cross- amyloid properties. The results support the amyloid-antimicrobial link. The high prevalence of ffAMPs produced by amphibians and marine creatures among other species suggests that they confer unique advantageous properties in distinctive environments, potentially providing stability and adherence properties. Most of the newly identified 14 ffAMPs showed lipid-induced and/or time-dependent secondary structure transitions in the fibril form, indicating structural and functional cross-/{beta} chameleons. Specifically, ffAMP cytotoxicity against human cells correlated with inherent or lipid-induced -helical fibril structure. The findings raise hypotheses about the role of fibril secondary structure switching in regulation of processes, such as the transition between a stable storage conformation and an active state with toxicity against specific cell types.

biophysics↗

bioRxiv2022

Modeling nonsegmented negative-strand RNA virus (NNSV) transcription with ejective polymerase collisions and biased diffusion

Infections by nonsegmented negative-strand RNA viruses (NNSV) are widely thought to entail gradient gene expression from the well-established existence of a single promoter at the 3 end of the viral genome and the assumption of constant transcriptional attenuation between genes. But multiple recent studies show viral mRNA levels in infections by respiratory syncytial virus (RSV), a major human pathogen and member of NNSV, that are inconsistent with a simple gradient. Here we integrate known and newly predicted phenomena into a biophysically reasonable model of NNSV transcription. Our model succeeds in capturing published observations of RSV and vesicular stomatitis virus (VSV) mRNA levels. We therefore propose a novel understanding of NNSV transcription based on the possibility of ejective polymerase-polymerase collisions and, in the case of RSV, biased polymerase diffusion.

biophysics↗

bioRxiv2022

Quantifying cooperative multisite binding through Bayesian inference

Multistep protein-protein interactions underlie most biological processes, but their characterization through methods such as isothermal titration calorimetry (ITC) is largely confined to simple models that provide little information on the intermediate, individual steps. In this study, we primarily examine the essential hub protein LC8, a small dimer that binds disordered regions of 100+ client proteins in two symmetrical grooves at the dimer interface. Mechanistic details of LC8 binding have remained elusive, hampered in part by ITC data analyses employing simple models that treat bivalent binding as a single event with a single binding affinity. We build on existing Bayesian ITC approaches to quantify thermodynamic parameters for multi-site binding interactions impacted by significant uncertainty in protein concentration. Using a two-site binding model, we model LC8 binding and identify positive cooperativity with high confidence for multiple client peptides. Application of an identical model to two-site binding between the coiled-coil dimer NudE and the intermediate chain of dynein reveals little evidence of cooperativity, in contrast to LC8. We propose that cooperativity in the LC8 system drives the formation of saturated 2:2 bound states, which play a functional role in many LC8 complexes. In addition to these system-specific findings, our work advances general ITC analysis in two ways. First, we describe a previously unrecognized mathematical ambiguity in concentrations in standard binding models and clarify how it impacts the precision with which binding parameters can be determined in cases of high uncertainty in analyte concentrations. Second, building on observations in the LC8 system, we develop a system-agnostic heat map of practical parameter identifiability calculated from synthetic data which demonstrates that certain binding parameters intrinsically inflate parameter uncertainty in ITC analysis, independent of experimental uncertainties. Author SummaryMulti-site protein-protein interactions govern many protein functions throughout the cell. Precise determination of thermodynamic constants of multi-site binding is a significant biophysical challenge, however. The application of complex models to multi-step interactions is difficult and hampered further by complications arising from uncertainty in analyte concentrations. To address these issues, we utilize Bayesian statistical techniques which calculate the likelihood of parameters giving rise to experimental observations to build probability density distributions for thermodynamic parameters of binding. To demonstrate the method and improve our understanding how the hub protein LC8 promotes dimerization of its 100+ binding partners, we test the pipeline on several of these partners and demonstrate that LC8 can bind clients cooperatively, driving interactions towards a fully bound functional state. We additionally examine an interaction between the dimer NudE and the intermediate chain of dynein, which does not appear to bind with cooperativity. Our work provides a solid foundation for future analysis of more complicated binding interactions, including oligomeric complexes formed between LC8 and clients with multiple LC8-binding sites.

biophysics↗

bioRxiv2022

From Molecular Dynamics to Supramolecular Organization: The Role of PIM Lipids in the Originality of the Mycobacterial Plasma Membrane

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, a disease that claims ~1.5 million lives annually. The current treatment regime is long and expensive, and missed doses contribute to drug resistance. There is much to be understood about the Mtb cell envelope, a complicated barrier that antibiotics need to negotiate to enter the cell. Within this envelope, the plasma membrane is the ultimate obstacle and is proposed to be comprised of over 50% mannosylated phosphatidylinositol lipids (phosphatidyl-myoinositol mannosides, PIMs), whose role in the membrane structure remains elusive. Here we used multiscale molecular dynamics (MD) simulations to understand the structure-function relationship of the PIM lipid family and decipher how they self-organize to drive biophysical properties of the Mycobacterial plasma membrane. To validate the model, we tested known anti-tubercular drugs and replicated previous experimental results. Our results shed new light into the organization of the Mycobacterial plasma membrane and provides a working model of this complex membrane to use for in silico studies. This opens the door for new methods to probe potential antibiotic targets and further understand membrane protein function. O_FIG O_LINKSMALLFIG WIDTH=169 HEIGHT=200 SRC="FIGDIR/small/498153v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@102b357org.highwire.dtl.DTLVardef@4c30f8org.highwire.dtl.DTLVardef@22f32aorg.highwire.dtl.DTLVardef@8a6f65_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Phospholipids Diffusion on the Surface of Model Lipid Droplets

Lipid droplets (LD) are organelles localized in the membrane of the Endoplasmic Reticulum (ER) that play an important role in metabolic functions. They consist of a core of neutral lipids surrounded by a monolayer of phosphoplipids and proteins resembling an oil-in-water emulsion droplet. Many studies have focused on the biophysical properties of these LDs. However, despite numerous efforts, we are lacking information on the mobility of phospholipids on the LDs surface, although they may play a key role in the protein distribution. In this article, we developed a microfluidic setup that allows the formation of a triolein-buffer interface decorated with a phospholipid monolayer. Using this setup, we measured the motility of phospholipid molecules by performing Fluo-rescent Recovery After Photobleaching (FRAP) experiments for different lipidic compositions. The results of the FRAP measurements reveal that the motility of phospholipids is controlled by the monolayer packing decorating the interface.

biophysics↗

bioRxiv2022

An interpretable machine learning algorithm to predict disordered protein phase separation based on biophysicalinteractions

Protein phase separation is increasingly understood to be an important mechanism of biological organization and biomaterial formation. Intrinsically disordered protein regions (IDRs) are often significant drivers of protein phase separation. A number of protein phase separation prediction algorithms are available, with many specific for particular classes of proteins and others providing results that are not amenable to interpretation of contributing biophysical interactions. Here we describe LLPhyScore, a new predictor of IDR-driven phase separation, based on a broad set of physical interactions or features. LLPhyScore uses sequence-based statistics from the RCSB PDB database of folded structures for these interactions, and is trained on a manually curated set of phase separation driver proteins with different negative training sets including the PDB and human proteome. Competitive training for a variety of physical chemical interactions shows the greatest importance of solvent contacts, disorder, hydrogen bonds, pi-pi contacts, and kinked-beta structure, with electrostatics, cation-pi, and absence of helical secondary structure also contributing. LLPhyScore has strong phase separation prediction recall statistics and enables a quantitative breakdown of the contribution from each physical feature to a sequences phase separation propensity. The tool should be a valuable resource for guiding experiment and providing hypotheses for protein function in normal and pathological states, as well as for understanding how specificity emerges in defining individual biomolecular condensates. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/499043v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@151f0cdorg.highwire.dtl.DTLVardef@984335org.highwire.dtl.DTLVardef@645b3dorg.highwire.dtl.DTLVardef@a22b06_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Liquid-liquid phase separation recapitulates the thermodynamics and kinetics of heterochromatin formation

The spatial segregation of pericentromeric heterochromatin (PCH) into distinct, membrane-less nuclear compartments involves the binding of Heterochromatin Protein 1 (HP1) to H3K9me2/3-rich genomic regions. While HP1 exhibits liquid-liquid phase separation properties in vitro, its mechanistic impact on the structure and dynamics of PCH condensate formation in vivo remains largely unresolved. Here, using biophysical modeling, we systematically investigate the mutual coupling between self-interacting HP1-like molecules and the chromatin polymer. We reveal that the specific affinity of HP1 for H3K9me2/3 loci facilitates coacervation in nucleo, and promotes the formation of stable PCH condensates at HP1 levels far below the concentration required to observe phase separation in purified protein assays in vitro. These heterotypic HP1-chromatin interactions give rise to a strong dependence of the nucleoplasmic HP1 density on HP1-H3K9me2/3 stoichiometry, consistent with the thermodynamics of multicomponent phase separation. The dynamical crosstalk between HP1 and the viscoelastic chromatin scaffold also leads to anomalously-slow equilibration kinetics, which strongly depend on the genomic distribution of H3K9me2/3 domains, and result in the coexistence of multiple long-lived, microphase-separated PCH compartments. The morphology of these complex coacervates is further found to be governed by the dynamic establishment of the underlying H3K9me2/3 landscape, which may drive their increasingly abnormal, aspherical shapes during cell development. These findings compare favorably to 4D microscopy measurements of HP1 condensates that we perform in live Drosophila embryos, and suggest a general quantitative model of PCH formation based on the interplay between HP1-based phase separation and chromatin polymer mechanics. SIGNIFICANCE STATEMENTThe compartmentalization of pericentromeric heterochromatin (PCH), the highly-repetitive part of the genome, into membrane-less organelles enriched in HP1 proteins, is critical to both genetic stability and cell fate determination. While HP1 can self-organize into liquid-like condensates in vitro, the roles of HP1 and the polymer chromatin in forming 3D PCH domains in vivo are still unclear. Using molecular simulations, we show that key kinetic and thermodynamic features of PCH condensates are consistent with a phase-separation mode of organization driven by the genomic distribution of methylated domains and HP1 self-attraction and affinity for heterochromatin. Our predictions are corroborated by live-microscopy performed during early fly embryogenesis, suggesting that a strong crosstalk between HP1-based phase separation and chromosome mechanics drive PCH condensate formation.

biophysics↗

bioRxiv2022

Discovering functionally important sites in proteins

Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.

biophysics↗

bioRxiv2022

Rapid protein stability prediction using deep learning representations

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate [~] 300 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available--including via a Web interface--and enables large-scale analyses of stability in experimental and predicted protein structures.

biophysics↗

bioRxiv2022

Efficient base-catalysed Kemp elimination in an engineered ancestral enzyme

The routine generation of enzymes with completely new active sites is one of the major unsolved problems in protein engineering. Advances in this field have been so far modest, perhaps due, at least in part, to the widespread use of modern natural proteins as scaffolds for de novo engineering. Most modern proteins are highly evolved and specialized, and, consequently, difficult to repurpose for completely new functionalities. Conceivably, resurrected ancestral proteins with the biophysical properties that promote evolvability, such as high stability and conformational diversity, could provide better scaffolds for de novo enzyme generation. Kemp elimination, a non-natural reaction that provides a simple model of proton abstraction from carbon, has been extensively used as a benchmark in de novo enzyme engineering. Here, we present an engineered ancestral {beta}-lactamase with a new active site capable of efficiently catalysing the Kemp elimination. Our Kemp eliminase is the outcome of a minimalist design based on a single function-generating mutation followed by sharply-focused, low-throughput library screening. Yet, its catalytic parameters (kcat/KM=2{middle dot}105 M-1s-1, kcat=635 s-1) compare favourably with the average modern natural enzyme and with the best proton-abstraction de novo Kemp eliminases reported in the literature. General implications of our results for de novo enzyme engineering are discussed.

biophysics↗

bioRxiv2022

Mechano-biological and bio-mechanical pathways in cutaneous wound healing

Skin injuries heal through coordinated action of fibroblast-mediated extracellular matrix (ECM) deposition, ECM remodeling, and wound contraction. Defects involving the dermis result in fibrotic scars featuring increased stiffness and altered collagen content and organization. Although computational models are crucial to unravel the underlying biochemical and biophysical mechanisms, simulations of the evolving wound biomechanics are seldom benchmarked against measurements. Here, we leverage recent quantifications of local tissue stiffness in murine wounds to refine a previously-proposed systems bio-chemo-mechanobiological finite-element model. Fibroblasts are considered as the main cell type involved in ECM remodeling and wound contraction. Tissue rebuilding is coordinated by the release and diffusion of a cytokine wave, e.g. TGF-{beta}, itself developed in response to an earlier inflammatory signal triggered by platelet aggregation. We calibrate a model of the evolving wound biomechanics through a custom-developed hierarchical Bayesian inverse analysis. Further calibration is based on published biochemical and morphological murine wound healing data over a 21-day healing period. The calibrated model recapitulates the temporal evolution of: inflammatory signal, fibroblast infiltration, collagen buildup, and wound contraction. Moreover, it enables in silico hypothesis testing, which we explore by: (i) quantifying the alteration of wound contraction profiles corresponding to the measured variability in local wound stiffness; (ii) proposing alternative constitutive links connecting the dynamics of the biochemical fields to the evolving mechanical properties; (iii) discussing the plausibility of a stretch- vs. stiffness-mediated mechanobiological coupling. Ultimately, our model challenges the current understanding of wound biomechanics and mechanobiology, beside offering a versatile tool to explore and eventually control scar fibrosis after injury. Author summaryWounds constitute a major healthcare burden, often yielding overly stiff scars that feature altered collagen content and organization. Accurate computational models have the potential to impact the understanding, treatment, and ultimately the outcome of wound healing progression by highlighting key mechanisms of new tissue formation and providing a versatile platform for hypothesis testing. However, the description of wound biomechanics has so far been based on measurements of uninjured tissue behavior, limiting our understanding of the links between wound stiffness and healing outcome. Here, we leverage recent experimental data of the local stiffness changes during murine wound healing to inform a computational model. The calibrated model also recapitulates previously-measured biochemical and morphological aspects of wound healing. We further demonstrate the relevance of the model towards understanding scar formation by evaluating the link between local changes in tissue stiffness and overall wound contraction, as well as testing hypotheses on: (i) how local tissue stiffness is linked to composition; (ii) how a fibrotic response depends on mechanobiological cues.

biophysics↗

bioRxiv2022

Crowding-induced phase separation and solidification by co-condensation of PEG in NPM1-rRNA condensates

The crowdedness of the cell calls for adequate intracellular organization. Biomolecular condensates, formed by liquid-liquid phase separation of intrinsically disordered proteins and nucleic acids, are important organizers of cellular fluids. To underpin the molecular mechanisms of protein condensation, cell-free studies are often used where the role of crowding is not investigated in detail. Here, we investigate the effects of macromolecular crowding on the formation and material properties of a model heterotypic biomolecular condensate, consisting of nucleophosmin (NPM1) and ribosomal RNA (rRNA). We studied the effect of the macromolecular crowding agent PEG, which is often considered an inert crowding agent. We observed that PEG could induce both homotypic and heterotypic phase separation of NPM1 and NPM1-rRNA, respectively. Crowding increases the condensed concentration of NPM1 and decreases its equilibrium dilute phase concentration, while no significant change in the concentration of rRNA in the dilute phase was observed. Interestingly, the crowder itself is concentrated in the condensates, suggesting that co-condensation rather than excluded volume interactions underlie the enhanced phase separation by PEG. Fluorescence recovery after photobleaching (FRAP) measurements indicated that both NPM1 and rRNA become immobile at high PEG concentrations, indicative of a liquid-to-gel transition. Together, these results shed new light onto the role of synthetic crowding agents in phase separation, and demonstrate that condensate properties determined in vitro depend strongly on the addition of crowding agents. STATEMENT OF SIGNIFICANCELiquid-liquid phase separation of proteins and nucleic acids leads to the formation of biomolecular condensates. To mimic biomolecular condensates in vitro, polymeric crowding agents, such as PEG, are often added. Such crowding agents are considered to make in vitro solutions more physiologically relevant, by mimicking the high cellular macromolecule concentrations. However, these crowding agents are commonly selected for their commercial availability and solubility in water, and their influence on phase separation and the physicochemical properties of condensates are seldom studied. Here we use biophysical methods to show that PEG induces phase separation of a model condensate through co-condensation rather than volume exclusion. As a consequence, crowding changes the partitioning, concentrations and viscoelastic properties of the condensates significantly, which sheds new light onto studies aimed at quantifying the material properties of biomolecular condensates.

biophysics↗

bioRxiv2022

Protein Geometry, Function and Mutation

AO_SCPLOWBSTRACTC_SCPLOWThis survey for mathematicians summarizes several works by the author on protein geometry and protein function with applications to viral glycoproteins in general and the spike glycoprotein of the SARS-CoV-2 virus in particular. Background biology and biophysics are sketched. This body of work culminates in a postulate that protein secondary structure regulates mutation, with backbone hydrogen bonds materializing in critical regions to avoid mutation, and disappearing from other regions to enable it.

biophysics↗

bioRxiv2022

Intrinsic disorder in CENP-ACse4 tail and its chaperone facilitates synergistic association for kinetochore stabilization

The kinetochore is a complex multiprotein network that assembles at a specialized DNA locus called the centromere to ensure faithful chromosome segregation. The centromere is epigenetically marked by a histone H3 variant - the CenH3. The budding yeast CenH3, called Cse4, consists of an unusually long and disordered N-terminal tail that has a role in kinetochore assembly. Its disordered chaperone, Scm3 is involved in its centromeric deposition as well as in the maintenance of a segregation-competent kinetochore. The dynamics of the Cse4 N-tail and chaperone interaction have not been studied, leaving a gap in our understanding of their roles at the centromere. Previously, we had shown that Scm3 is an intrinsically disordered protein. Here, using NMR and a variety of biophysical and bioinformatics tools, we show that Cse4 N-tail is also disordered, the two proteins interact with each other at multiple sites, and this interaction reduces the disorder in Scm3; the chain opens up relative to the native state ensemble and the conformational exchange is reduced. Interestingly, this interaction between the two intrinsically disordered protein is fairly specific as seen by positive and negative controls, and is majorly driven by electrostatics as both the proteins have multiple acidic and basic regions. The complex retains a fair amount of disorder, which facilitates a synergistic association with the essential inner kinetochore Ctf19-Mcm21-Okp1-Ame1 complex; a model has been suggested to this effect. Given the abundance of intrinsic disorder in the kinetochore proteins, this type of interaction and adaptation may be prevalent in other proteins as well for mediating kinetochore assembly. Thus, the present study, on one hand, provides significant structural and mechanistic insights into the complex and dynamic process of kinetochore assembly, and on the other hand, illustrates a mechanism that intrinsically disordered proteins would adapt to mediate the formation of complex multiprotein networks, in general. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/504061v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@8fe5cforg.highwire.dtl.DTLVardef@138afb1org.highwire.dtl.DTLVardef@96943forg.highwire.dtl.DTLVardef@1deb673_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Alterations of pulmonary surfactant function by e-cigarette vapour

E-cigarette (EC) and vaping use continue to remain popular amongst teenage and young adult populations, despite several reports of vaping associated lung injury. This popularity is due in part to the vast variety of appealing flavours and nicotine concentrations easily accessible on the market. One of the first compounds that EC aerosols comes into contact within the lungs during a deep inhalation is pulmonary surfactant. This lipid protein mixture lines the alveoli, reducing surface tension and preventing alveolar collapse. Impairment of surfactants critical surface tension reducing activity can contribute to lung dysfunction. Currently, information on how EC aerosols impacts pulmonary surfactant remains limited. We hypothesized that exposure to EC aerosol impairs the surface tension reducing ability of surfactant. Bovine Lipid Extract Surfactant (BLES) was used as a model surfactant in a direct exposure syringe system. BLES (2ml) was placed in a syringe (30ml) attached to an EC. The generated aerosol was drawn into the syringe and then expelled, repeated 30 times. Biophysical analysis after exposure was completed using a constrained drop surfactometer (CDS). Minimum surface tensions increased after exposure to the EC aerosol. Variation in device used, addition of nicotine, or temperature of the aerosol had no additional effect. Two e-liquid flavours, menthol and red wedding, had further detrimental effects, resulting in higher surface tension than the vehicle exposed BLES. Alteration of surfactant properties through interaction with the produced aerosol was observed with a basic e-liquid vehicle, however additional compounds produced by added flavourings appeared to be able to increase inhibition. In conclusion, EC aerosols alter surfactant function through increases in minimum surface tension. This impairment may contribute to lung dysfunction and susceptibility to further injury.

biophysics↗

bioRxiv2022

Serinc5 restricts HIV membrane fusion by altering lipid order and heterogeneity in the viral membrane

The host restriction factor, Serinc5, incorporates into budding HIV particles and inhibits their infection by an incompletely understood mechanism. We have previously reported that Serinc5 but not its paralogue, Serinc2, blocks HIV cell entry by membrane fusion, specifically by inhibiting fusion pore formation and dilation. A compelling body of work also suggests Serinc5 may alter the conformation and clustering of the HIV fusion protein, Env. To contribute an additional perspective to the developing model of Serinc5 restriction, we assessed Serinc2 and Serinc5s effects on HIV pseudoviral membranes. Using fluorescence lifetime imaging with an order sensitive dye, FLIPPER-TR, and by measuring pseudoviral membrane thickness via cryo electron microscopy (cryoEM), Serinc5 was found to increase membrane heterogeneity, skewing the distribution towards a larger fraction of the viral membrane in an ordered phase. We also directly observed for the first time the coexistence of membrane domains within individual viral membrane envelopes. Using a TIRF-based single particle fusion assay, we found that incorporation of exogenous phosphatidylethanolamine (PE) into the viral membrane rescued HIV pseudovirus fusion from restriction by Serinc5, which was accompanied by decreased membrane heterogeneity and order. This effect was specific for PE and did not depend on acyl chain length or saturation. Together, these data suggest that Serinc5 alters multiple interrelated properties of the viral membrane--lipid chain order, rigidity, line tension, and lateral pressure--which decrease accessibility of fusion intermediates and disfavor completion of fusion. These biophysical insights into Serinc5 restriction of HIV infectivity could contribute to the development of novel antivirals that exploit the same weaknesses of HIV and potentially other enveloped viruses.

biophysics↗

bioRxiv2022

Temperature-dependent Thermodynamic and Photophysical Properties of SYTO-13 Dye Bound to DNA

The benefits of dyes in nucleic acid assays above room temperature are limited by a nonlinear, highdimensional relationship between fluorescence and the biophysical and chemical processes occurring in solution. To overcome these limitations, we identify an experimental regime that eliminates bias and unnecessary complexities in this relationship, and develop an experimental-computational workflow to generate the property data required to describe the dependence of fluorescence on temperature and concentration. Specifically, we exploit the temperature-cycling capabilities of real-time PCR machine, as well as the utility of numerical optimization, to determine the binding strength and molar fluorescence of the SYTO-13 dye bound to double-stranded (DS) or single-stranded (SS) DNA at more than 60 temperatures. We find that the data analysis approach is robust; it can account for significant well-to-well and plate-to-plate variation. The weak binding strength of SYTO-13 relative to SYBR Green I is consistent with previous reports of its negligible influence on PCR and melting temperature. Discriminating between molar fluorescence and binding strength clarifies the mechanism for the larger fluorescence of a DS/dye solution than a SS/dye solution; in fact, the explanation is different at high temperature than at low temperature. The temperature-dependence of the binding strength allows for ascertainment of the enthalpic and entropic contributions to the free energy, as well as the sign of the differential heat capacity of binding. The temperature-dependence of the molar fluorescence allows for calculation of the brightness (quantum yield times molar extinction coefficient) of SYTO-13 bound to DS relative to SS. The more accurate and complete description of the relationship between solution behavior and fluorescence enabled by this work can lead to more accurate selection of dyes and quantification of nucleic acids. SIGNIFICANCEFluorescent dyes are often used to quantify nucleic acids. The accuracy and precision of quantification, however, is limited by a complex and high-dimensional relationship between fluorescence and solution behavior. This is especially true for assays above room temperature, where empirical approximations are often required in the absence of available property data. In this work, we present an experimental and computational workflow that can more accurately describe this relationship and more efficiently generate the temperature-dependent thermodynamic and photophysical properties required. This approach can improve quantification and selection of high-performing dyes for particular assays.

biophysics↗

bioRxiv2022

Enhancing Robustness, Precision and Speed of Traction Force Microscopy with Machine Learning

Traction patterns of adherent cells provide important information on their interaction with the environment, cell migration or tissue patterns and morphogenesis. Traction force microscopy is a method aimed at revealing these traction patterns for adherent cells on engineered substrates with known constitutive elastic properties from deformation information obtained from substrate images. Conventionally, the substrate deformation information is processed by numerical algorithms of varying complexity to give the corresponding traction field via solution of an ill-posed inverse elastic problem. We explore the capabilities of a deep convolutional neural network as a computationally more efficient and robust approach to solve this inversion problem. We develop a general purpose training process based on collections of circular force patches as synthetic training data, which can be subjected to different noise levels for additional robustness. The performance and the robustness of our approach against noise is systematically characterized for synthetic data, artificial cell models and real cell images, which are subjected to different noise levels. A comparison to state-of-the-art Bayesian Fourier transform traction cytometry reveals the precision, robustness, and speed improvements achieved by our approach, leading to an acceleration of traction force microscopy methods in practical applications. O_TEXTBOXSIGNIFICANCETraction force microscopy is an important biophysical technique to gain quantitative information about forces exerted by adherent cells. It relies on solving an inverse problem to obtain cellular traction forces from image-based displacement information. We present a deep convolutional neural network as a computationally more efficient and robust approach to solve this ill-posed inversion problem. We characterize the performance and the robustness of our approach against noise systematically for synthetic data, artificial cell models and real cell images, which are subjected to different noise levels and compare performance and robustness to state-of-the-art Bayesian Fourier transform traction cytometry. We demonstrate that machine learning can enhance robustness, precision and speed in traction force microscopy. C_TEXTBOX

biophysics↗