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International Conference on Conceptual Modeling

ER 2007: Advances in Conceptual Modeling – Foundations and Applications pp 3–13Cite as

Pattern Recognition of Single-Molecule Force Spectroscopy Data

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4802))

Abstract

Motivation: 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 about the protein’s structure, conformation, and inter- and intra-molecular forces. High-throughput force spectroscopy experiments generate hundreds of force curves including spurious and good curves, which correspond to different unfolding pathways and to different functional states of an investigated membrane protein.

Results: In the present work we propose a novel application of automated unfolding pattern recognition routines. We apply our method to datasets from unfolding experiments of bacteriorhodopsin (bR) and bovine rhodopsin (Rho). As a result, we discuss the different unfolding pathways of bR, and two functional states for Rho could be observed . Overall, the algorithm tackles the force spectroscopy bottleneck and leads to more consistent and reproducible results paving the way for high-throughput analysis of structural features of membrane proteins.

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Author information

Authors and Affiliations

  1. Department of Bioinformatics, Center of Biotechnology, TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany

    Dirk Labudde, Annalisa Marsico & Michael Schroeder

  2. Department of Cellular Machines, Center of Biotechnology, TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany

    Dirk Labudde & K. Tanuj Sapra

Authors
  1. Dirk Labudde
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  2. Annalisa Marsico
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  3. K. Tanuj Sapra
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  4. Michael Schroeder
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Editor information

Jean-Luc Hainaut Elke A. Rundensteiner Markus Kirchberg Michela Bertolotto Mathias Brochhausen Yi-Ping Phoebe Chen Samira Si-Saïd Cherfi Martin Doerr Hyoil Han Sven Hartmann Jeffrey Parsons Geert Poels Colette Rolland Juan Trujillo Eric Yu Esteban Zimányie

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Labudde, D., Marsico, A., Sapra, K.T., Schroeder, M. (2007). Pattern Recognition of Single-Molecule Force Spectroscopy Data. In: Hainaut, JL., et al. Advances in Conceptual Modeling – Foundations and Applications. ER 2007. Lecture Notes in Computer Science, vol 4802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76292-8_2

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  • DOI: https://doi.org/10.1007/978-3-540-76292-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76291-1

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