Change search
Refine search result
1 - 3 of 3
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 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. Kasimova, Marina A.
    et al.
    Lindahl, Erik
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). KTH Royal Institute of Technology, Sweden.
    Delemotte, Lucie
    Determining the molecular basis of voltage sensitivity in membrane proteins2018In: The Journal of General Physiology, ISSN 0022-1295, E-ISSN 1540-7748, Vol. 150, no 10, p. 1444-1458Article in journal (Refereed)
    Abstract [en]

    Voltage-sensitive membrane proteins are united by their ability to transform changes in membrane potential into mechanical work. They are responsible for a spectrum of physiological processes in living organisms, including electrical signaling and cell-cycle progression. Although the mechanism of voltage-sensing has been well characterized for some membrane proteins, including voltage-gated ion channels, even the location of the voltage-sensing elements remains unknown for others. Moreover, the detection of these elements by using experimental techniques is challenging because of the diversity of membrane proteins. Here, we provide a computational approach to predict voltage-sensing elements in any membrane protein, independent of its structure or function. It relies on an estimation of the propensity of a protein to respond to changes in membrane potential. We first show that this property correlates well with voltage sensitivity by applying our approach to a set of voltage-sensitive and voltage-insensitive membrane proteins. We further show that it correctly identifies authentic voltage-sensitive residues in the voltage-sensor domain of voltage-gated ion channels. Finally, we investigate six membrane proteins for which the voltage-sensing elements have not yet been characterized and identify residues and ions that might be involved in the response to voltage. The suggested approach is fast and simple and enables a characterization of voltage sensitivity that goes beyond mere identification of charges. We anticipate that its application before mutagenesis experiments will significantly reduce the number of potential voltage-sensitive elements to be tested.

  • 3. 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.

1 - 3 of 3
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf