Automated alignment and pattern recognition of single-molecule force spectroscopy data.
Recently, direct measurements of forces stabilizing single proteins or individual receptor-ligand bonds became possible with ultra-sensitive force probe methods like the atomic force microscope (AFM). In force spectroscopy experiments using AFM, a single molecule or receptor-ligand pair is tethered between the tip of a micromachined cantilever and a supporting surface. While the molecule is stretched, forces are measured by the deflection of the cantilever and plotted against extension, yielding a force spectrum characteristic for each biomolecular system. In order to obtain statistically relevant results, several hundred to thousand single-molecule experiments have to be performed, each resulting in a unique force spectrum. We developed software and algorithms to analyse large numbers of force spectra. Our algorithms include the fitting polymer extension models to force peaks as well as the automatic alignment of spectra. The aligned spectra allowed recognition of patterns of peaks across different spectra. We demonstrate the capabilities of our software by analysing force spectra that were recorded by unfolding single transmembrane proteins such as bacteriorhodopsin and NhaA. Different unfolding pathways were detected by classifying peak patterns. Deviant spectra, e.g. those with no attachment or erratic peaks, can be easily identified. The software is based on the programming language C++, the GNU Scientific Library (GSL), the software WaveMetrics IGOR Pro and available open-source at http://bioinformatics.org/fskit/.