Pattern Recognition in Single Molecule Force Spectroscopy Data
We have developed an analytical technique for single molecule force spectroscopy (SMFS) data that avoids filtering prior to analysis and performs pattern recognition to identify distinct SMFS events. The technique characterizes the signal similarity between all curves in a data set and generates a hierarchical clustering tree, from which clusters can be identified, aligned, and examined to identify key patterns. This procedure was applied to alpha-lactalbumin (aLA) on polystyrene substrates with flat and nanoscale curvature, and bacteriorhodopsin (bR) adsorbed on mica substrates. Cluster patterns identified for the aLA data sets were associated with different higher-order protein-protein interactions. Changes in the frequency of the patterns showed an increase in the monomeric signal from flat to curved substrates. Analysis of the bR data showed a high level of multiple protein SMFS events and allowed for the identification of a set of characteristic three-peak unfolding events.