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Specific interactions of peripheral membrane proteins with lipids: what can molecular simulations show us?

期刊

BIOSCIENCE REPORTS
卷 42, 期 4, 页码 -

出版社

PORTLAND PRESS LTD
DOI: 10.1042/BSR20211406

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资金

  1. Carlsberg Foundation [CF19-0288]
  2. Lundbeck Foundation [R347-2020-2339]
  3. UKRI-BBSRC Interdisciplinary Bioscience Doctoral Training Partnership [BB/M011224/1]
  4. ISIS Facility Development Studentship Program
  5. Wellcome [208361/Z/17/Z]
  6. EPSRC [EP/L000253/1]
  7. Wellcome Trust [208361/Z/17/Z] Funding Source: Wellcome Trust

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This article reviews recent attempts to characterize the interactions between peripheral membrane proteins (PMPs) and biological membranes. The article compares different methods used to identify and analyze lipid-binding sites from simulation data and highlights recent work on characterizing the energetics of these interactions using free energy calculations. The authors describe how improvements in methodologies and computing power will continue to contribute to the field of molecular dynamics simulations.
Peripheral membrane proteins (PMPs) can reversibly and specifically bind to biological membranes to carry out functions such as cell signalling, enzymatic activity, or membrane remodelling. Structures of these proteins and of their lipid-binding domains are typically solved in a soluble form, sometimes with a lipid or lipid headgroup at the binding site. To provide a detailed molecular view of PMP interactions with the membrane, computational methods such as molecular dynamics (MD) simulations can be applied. Here, we outline recent attempts to characterise these binding interactions, focusing on both intracellular proteins, such as phosphatidylinositol phosphate (PIP)-binding domains, and extracellular proteins such as glycolipid-binding bacterial exotoxins. We compare methods used to iden-tify and analyse lipid-binding sites from simulation data and highlight recent work character-ising the energetics of these interactions using free energy calculations. We describe how improvements in methodologies and computing power will help MD simulations to continue to contribute to this field in the future.

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