Volume 89, Number 6, March 2010
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||19 April 2010|
Network theory approach for data evaluation in the dynamic force spectroscopy of biomolecular interactions
Scanning Probe Microscopy Group, Institute for Molecules and Materials, Radboud University Nijmegen, The Netherlands, EU
2 Department of theoretical physics, Jožef Stefan Institute - Ljubljana, Slovenia, EU
3 Department of Biophysical Chemistry, Institute for Molecules and Materials, Radboud University Nijmegen, The Netherlands, EU
Corresponding author: email@example.com
Accepted: 18 March 2010
Investigations of bonds between single molecules and molecular complexes by dynamic force spectroscopy are subject to large fluctuations at nanoscale and possible aspecific binding, which mask the experimental output. Big efforts are devoted to develop methods for the effective selection of the relevant experimental data, before the quantitative analysis of bond parameters. Here we present a methodology which is based on the application of graph theory. The force-distance curves corresponding to repeated pulling events are mapped onto their correlation network (mathematical graph). On these graphs the groups of similar curves appear as topological modules, which are identified using the spectral analysis of graphs. We demonstrate the approach by analyzing a large ensemble of the force-distance curves measured on: ssDNA-ssDNA, peptide-RNA (from HIV1), and peptide-Au surface systems. Within our data sets the methodology systematically separates subgroups of curves which are related to different types of intermolecular interactions and to spatial arrangements in which the molecules are brought together and/or pulling speeds. This demonstrates the sensitivity of the method to the spatial degrees of freedom, suggesting potential applications in the case of large molecular complexes and situations with multiple binding sites.
PACS: 82.37.Rs – Single molecule manipulation of proteins and other biological molecules / 89.75.Hc – Networks and genealogical trees / 02.70.Hm – Spectral methods
© EPLA, 2010
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