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Audio versions of bioRxiv paper abstracts

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Audio versions of bioRxiv paper abstracts

    Mechanically Induced Integrin Ligation Mediates Intracellular Calcium Signaling with Single Pulsating Cavitation Bubbles

    Mechanically Induced Integrin Ligation Mediates Intracellular Calcium Signaling with Single Pulsating Cavitation Bubbles

    Link to bioRxiv paper:
    http://biorxiv.org/cgi/content/short/2020.10.25.353904v1?rss=1

    Authors: Li, F., Park, T. H., Sankin, G., Gilchrist, C., Liao, D., Chan, C. U., Mao, Z., Hoffman, B. D., Zhong, P.

    Abstract:
    Ultrasound or shockwave-induced cavitation is used therapeutically to stimulate neural and muscle tissue, but the mechanisms underlying this mechanotransduction are unclear. Intracellular calcium signaling is one of the earliest events in mechanotransduction. In this study, we investigate the mechanism of calcium signaling in individual HEK293T cells stimulated by single cavitation bubbles. Calcium responses are rare at cell-bubble distance that avoids membrane poration, even with overexpression of the mechanosensitive ion channel Piezo1, but could be increased in frequency to 42% of cells by attaching RGD beads to the apical surface of the cells. By using Piezo1 knockout and Piezo1-expressing cells, integrin-blocking antibodies, and inhibitors of P2X ion channels, key molecular players are identified in the RGD bead-enhanced calcium response: increased integrin ligation by substrate ECM triggers ATP release and activation of P2X-but not Piezo1-ion channels. These molecular players have not been examined previously in cavitation-induced calcium signaling. The resultant calcium influx causes dynamic changes in cell spread area. This approach to eliciting a calcium response with cavitation microbubbles without cell injury, and the uncovered mechanotransduction mechanism by which increased integrin-ligation mediates ATP release and calcium signaling will inform new strategies to stimulate tissues with ultrasound and shockwaves.

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    Bayesian inference: The comprehensive approach to analyzing single-molecule experiments

    Bayesian inference: The comprehensive approach to analyzing single-molecule experiments

    Link to bioRxiv paper:
    http://biorxiv.org/cgi/content/short/2020.10.23.353110v1?rss=1

    Authors: Kinz-Thompson, C. D., Ray, K. K., Gonzalez, R. L.

    Abstract:
    Biophysics experiments performed at single-molecule resolution contain exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. Here, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous manner to incorporate information from multiple experiments into a single analysis and to find the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.

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    Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution

    Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution

    Link to bioRxiv paper:
    http://biorxiv.org/cgi/content/short/2020.10.24.353318v1?rss=1

    Authors: Wieder, M., Fass, J., Chodera, J. D.

    Abstract:
    The computation of tautomer rations of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios---the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery---is surprisingly difficult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions.

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    Fast and exact single and double mutation-response scanning of proteins

    Fast and exact single and double mutation-response scanning of proteins

    Link to bioRxiv paper:
    http://biorxiv.org/cgi/content/short/2020.10.23.352955v1?rss=1

    Authors: Echave, J.

    Abstract:
    Studying the effect of perturbations on protein structure is a basic approach in protein research. Important problems, such as predicting pathological mutations and understanding patterns structural evolution, have been addressed by computational simulations based on modelling mutations as forces and predicting deformations using the Linear Response Approximation. In single mutation-response scanning simulations, a sensitivity matrix is obtained by averaging deformations over point mutations. In double mutation-response scanning simulations, a compensation matrix is obtained by minimizing deformations over pairs of mutations. These very useful simulation-based methods may be too slow to deal with large supra-molecular complexes, such as a ribosome or a virus capsid, or large number of proteins, such as the human proteome, which limits their applicability. To address this issue, I derived analytical closed formulas to calculate the sensitivity and compensation matrices directly, without simulations. Here, I present these derivations and show that the resulting analytical methods are much faster than their simulation counterparts, and that where the simulation methods are approximate, the analytical methods are exact by design.

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    Towards designing globular antimicrobial peptide mimics: role of polar functional groups in biomimetic ternary antimicrobial polymers

    Towards designing globular antimicrobial peptide mimics: role of polar functional groups in biomimetic ternary antimicrobial polymers

    Link to bioRxiv paper:
    http://biorxiv.org/cgi/content/short/2020.10.24.353243v1?rss=1

    Authors: Rani, G., Kuroda, K., Vemparala, S.

    Abstract:
    Using atomistic molecular dynamics simulations, we study the interaction of ternary methacrylate polymers, composed of charged cationic, hydrophobic and neutral polar groups, with model bacterial membrane. Our simulation data shows that the random ternary polymers can penetrate deep into the membrane interior and partitioning of even a single polymer has a pronounced effect on the membrane structure. Lipid reorganization, on polymer binding, shows a strong affinity of the ternary polymer for anionic POPG lipids and the same is compared with the control case of binary polymers (only cationic and hydrophobic groups). While binary polymers exhibit strong propensity of acquired amphiphilic conformations upon membrane insertion, our results strongly suggest that such amphiphilic conformations are absent in the case of random ternary polymers. The ternary polymers adopt a more folded conformation, staying aligned in the direction of the membrane normal and subsequently penetrating deeper into the membrane interior suggesting a novel membrane partitioning mechanism without amphiphilic conformations. Finally, we also examine the interactions of ternary polymer aggregates with model bacterial membranes, which show that replacing some of the hydrophobic groups by polar groups leads to weakly held ternary aggregates enabling them to undergo rapid partitioning and insertion into membrane interior. Our work thus underscores the role of inclusion of polar groups into the framework of traditional binary biomimetic antimicrobial polymers and suggests different mode of partitioning into bacterial membranes, mimicking antimicrobial mechanism of globular antimicrobial peptides like Defensin.

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    Mesoscale phase separation of chromatin in the nucleus

    Mesoscale phase separation of chromatin in the nucleus

    Link to bioRxiv paper:
    http://biorxiv.org/cgi/content/short/2020.10.23.338079v1?rss=1

    Authors: Bajpai, G., Amiad-Pavlov, D., Lorber, D., Volk, T., Safran, S.

    Abstract:
    Intact-organism imaging of Drosophila larvae reveals and quantifies chromatin-aqueous phase separation. The chromatin can be organized near the lamina layer of the nuclear envelope, conventionally fill the nucleus, be organized centrally, or as a wetting droplet. These transitions are controlled by changes in nuclear volume and the interaction of chromatin with the lamina (part of the nuclear envelope) at the nuclear periphery. Using a simple polymeric model that includes the key features of chromatin self-attraction and its binding to the lamina, we demonstrate theoretically that it is the competition of these two effects that determines the mode of chromatin distribution. The qualitative trends as well as the compositional profiles obtained in our simulations compare well with the observed intact-organism imaging and quantification. Since the simulations contain only a small number of physical variables we can identify the generic mechanisms underlying the changes in the observed phase separations.

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