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  • 1.
    Howard, Rebecca J.
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Carnevale, Vincenzo
    Delemotte, Lucie
    Hellmich, Ute A.
    Rothberg, Brad S.
    Permeating disciplines: Overcoming barriers between molecular simulations and classical structure-function approaches in biological ion transport2018In: Biochimica et Biophysica Acta - Biomembranes, ISSN 0005-2736, E-ISSN 1879-2642, Vol. 1860, no 4, p. 927-942Article, review/survey (Refereed)
    Abstract [en]

    Ion translocation across biological barriers is a fundamental requirement for life. In many cases, controlling this process for example with neuroactive drugs demands an understanding of rapid and reversible structural changes in membrane-embedded proteins, including ion channels and transporters. Classical approaches to electrophysiology and structural biology have provided valuable insights into several such proteins over macroscopic, often discontinuous scales of space and time. Integrating these observations into meaningful mechanistic models now relies increasingly on computational methods, particularly molecular dynamics simulations, while surfacing important challenges in data management and conceptual alignment. Here, we seek to provide contemporary context, concrete examples, and a look to the future for bridging disciplinary gaps in biological ion transport. This article is part of a Special Issue entitled: Beyond the Structure-Function Horizon of Membrane Proteins edited by Ute Hellmich, Rupak Doshi and Benjamin Mcllwain.

  • 2. Westerlund, Annie M.
    et al.
    Harpole, Tyler J.
    Blau, Christian
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Delemotte, Lucie
    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.

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