Abstract
Single-molecule force spectroscopy has become a versatile tool for investigating the (un)folding of proteins and other polymeric molecules. Like other single-molecule techniques, single-molecule force spectroscopy requires recording and analysis of large data sets to extract statistically meaningful conclusions. Here, we present a data analysis tool that provides efficient filtering of heterogeneous data sets, brings spectra into register based on a reference-free alignment algorithm, and determines automatically the location of unfolding barriers. Furthermore, it groups spectra according to the number of unfolding events, subclassifies the spectra using cross correlation-based sorting, and extracts unfolding pathways by principal component analysis and clustering methods to extracted peak positions. Our approach has been tested on a data set obtained through mechanical unfolding of bacteriorhodopsin (bR), which contained a significant number of spectra that did not show the well-known bR fingerprint. In addition, we have tested the performance of the data analysis tool on unfolding data of the soluble multidomain (Ig27)(8) protein.