  Investigations of molecular bonds between single molecules and molecular
complexes by the dynamic force spectroscopy are subject to large fluctuations
at nanoscale and possible other aspecific binding, which mask the experimental
output. Big efforts are devoted to develop methods for effective selection of
the relevant experimental data, before taking 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 (system from HIV1), and peptide-Au surface. Within our
data sets the methodology systematically separates subgroups of curves which
are related to different 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.
