A Structure-Based Analysis of Single Molecule Force Spectroscopy (SMFS) Data for Bacteriorhodopsin and Four Mutants

  • Annalisa Marsico
  • K. Tanuj Sapra
  • Daniel J. Muller
  • Michael Schroeder
  • Dirk Labudde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4216)


Misfolding of membrane proteins plays an important role in many human diseases such as retinitis pigmentosa, hereditary deafness, and diabetes insipidus. Little is known about membrane proteins as there are only a very few high-resolution structures. Single molecule force spectroscopy is a novel technique, which measures the force necessary to pull a protein out of a membrane. Such force curves contain valuable information on the protein’s structure, conformation, and inter- and intra-molecular forces. High-throughput force spectroscopy experiments generate hundreds of force curves including spurious ones and good curves, which correspond to different unfolding pathways. As it is not known what is the origin of the interactions that estabilish unfolding barriers, in the present work we analyse the unfolding patterns coming from experiments of unfolding of bacteriorhodopsin and four mutants (P50A, P91A, P186A and M56). We correlate the postition, magnitude and probability of occurrence of force peaks with the results of a bioinformatics analysis of residue conservations, structural alignments and residue-residue contact area in the wild type and in the mutants, in order to gain insights about the interaction pattern stabilizing bacteriorhodopsin structure. From residue-residue contact area calculations we show that the analysed point mutations do not affect the stability of the protein in a significant way. We conclude that, even if the arrangement of intra-moleular interactions locally change in the mutated structures, the overall structural stability is not affected.


Force Curve Conservation Score Force Spectroscopy Purple Membrane Worm Like Chain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Annalisa Marsico
    • 1
  • K. Tanuj Sapra
    • 1
  • Daniel J. Muller
    • 1
  • Michael Schroeder
    • 1
  • Dirk Labudde
    • 1
  1. 1.BiotecDresden University of TechnologyGermany

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