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  • 1.
    Fernandez-Marino, Ana I.
    et al.
    Univ Wisconsin, SMPH, Dept Neurosci, Madison, WI 53706 USA.;NINDS, Mol Physiol & Biophys Sect, Porter Neurosci Res Ctr, NIH, Bldg 36,Rm 4D04, Bethesda, MD 20892 USA..
    Harpole, Tyler J.
    KTH, School of Engineering Sciences (SCI), Applied Physics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oelstrom, Kevin
    Univ Wisconsin, SMPH, Dept Neurosci, Madison, WI 53706 USA.;Cellular Dynam Int Inc, Madison, WI USA..
    Delemotte, Lucie
    KTH, School of Engineering Sciences (SCI), Applied Physics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Chanda, Baron
    Univ Wisconsin, SMPH, Dept Neurosci, Madison, WI 53706 USA.;Univ Wisconsin, SMPH, Dept Biomol Chem, Madison, WI 53706 USA..
    Gating interaction maps reveal a noncanonical electromechanical coupling mode in the Shaker K+ channel2018In: Nature Structural & Molecular Biology, ISSN 1545-9993, E-ISSN 1545-9985, Vol. 25, no 4, p. 320-326Article in journal (Refereed)
    Abstract [en]

    Membrane potential regulates the activity of voltage-dependent ion channels via specialized voltage-sensing modules, but the mechanisms involved in coupling voltage-sensor movement to pore opening remain unclear owing to a lack of resting state structures and robust methods to identify allosteric pathways. Here, using a newly developed interaction-energy analysis, we probe the interfaces of the voltage-sensing and pore modules in the Drosophila Shaker K+ channel. Our measurements reveal unexpectedly strong equilibrium gating interactions between contacts at the S4 and S5 helices in addition to those between S6 and the S4-S5 linker. Network analysis of MD trajectories shows that the voltage-sensor and pore motions are linked by two distinct pathways: a canonical pathway through the S4-S5 linker and a hitherto unknown pathway akin to rack-and-pinion coupling involving the S4 and S5 helices. Our findings highlight the central role of the S5 helix in electromechanical transduction in the voltage-gated ion channel (VGIC) superfamily.

  • 2.
    Harpole, Tyler J.
    et al.
    KTH, School of Engineering Sciences (SCI), Physics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Delemotte, Lucie
    KTH, School of Engineering Sciences (SCI), Physics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Conformational landscapes of membrane proteins delineated by enhanced sampling molecular dynamics simulations2018In: Biochimica et Biophysica Acta - Biomembranes, ISSN 0005-2736, E-ISSN 1879-2642, Vol. 1860, no 4, p. 909-926Article, review/survey (Refereed)
    Abstract [en]

    The expansion of computational power, better parameterization of force fields, and the development of novel algorithms to enhance the sampling of the free energy landscapes of proteins have allowed molecular dynamics (MD) simulations to become an indispensable tool to understand the function of biomolecules. The temporal and spatial resolution of MD simulations allows for the study of a vast number of processes of interest. Here, we review the computational efforts to uncover the conformational free energy landscapes of a subset of membrane proteins: ion channels, transporters and G-protein coupled receptors. We focus on the various enhanced sampling techniques used to study these questions, how the conclusions come together to build a coherent picture, and the relationship between simulation outcomes and experimental observables.

  • 3.
    Westerlund, Annie M.
    et al.
    KTH, School of Engineering Sciences (SCI), Physics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Harpole, Tyler
    KTH, School of Engineering Sciences (SCI), Physics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Blau, C.
    Delemotte, Lucie
    KTH, School of Engineering Sciences (SCI), Physics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Inference of Calmodulin's Ca2+-Dependent Free Energy Landscapes via Gaussian Mixture Model Validation2018In: Journal of Chemical Theory and Computation, ISSN 1549-9618, E-ISSN 1549-9626, Vol. 14, no 1, p. 63-71Article in journal (Refereed)
    Abstract [en]

    A free energy landscape estimation method based on the well-known Gaussian mixture model (GMM) is used to compare the efficiencies of thermally enhanced sampling methods with respect to regular molecular dynamics. The simulations are carried out on two binding states of calmodulin, and the free energy estimation method is compared with other estimators using a toy model. We show that GMM with cross-validation provides a robust estimate that is not subject to overfitting. The continuous nature of Gaussians provides better estimates on sparse data than canonical histogramming. We find that diffusion properties determine the sampling method effectiveness, such that diffusion-dominated apo calmodulin is most efficiently sampled by regular molecular dynamics, while holo calmodulin, with its rugged free energy landscape, is better sampled by enhanced sampling methods.

  • 4.
    Westerlund, Annie M.
    et al.
    KTH, School of Engineering Sciences (SCI), Applied Physics, Biophysics.
    Harpole, Tyler J.
    KTH, School of Engineering Sciences (SCI), Physics.
    Blau, Christian
    Stockholm Univ, Biochem & Biophys, Stockholm, Sweden..
    Delemotte, Lucie
    KTH, School of Engineering Sciences (SCI), Applied Physics, Biophysics.
    Inference of Calmodulin's Ca2+: Dependent Free Energy Landscapes via Gaussian Mixture Model Validation2018In: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 114, no 3, p. 675A-675AArticle in journal (Refereed)
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