MOTIVATION: Single-molecule force spectroscopy (SMFS) experiments pose the challenge of analysing protein unfolding data (traces) coming from preparations with heterogeneous composition (e.g. where different proteins are present in the sample). An automatic procedure able to distinguish the unfolding patterns of the proteins is needed. Here, we introduce a data analysis pipeline able to recognize in such datasets traces with recurrent patterns (clusters). RESULTS: We illustrate the performance of our method on two prototypical datasets: ∼50 000 traces from a sample containing tandem GB1 and ∼400 000 traces from a native rod membrane. Despite a daunting signal-to-noise ratio in the data, we are able to identify several unfolding clusters. This work demonstrates how an automatic pattern classification can extract relevant information from SMFS traces from heterogeneous samples without prior knowledge of the sample composition. AVAILABILITY AND IMPLEMENTATION: https://github.com/ninailieva/SMFS_clustering. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Single-molecule force spectroscopy has facilitated the experimental investigation of biomolecular force-coupled kinetics, from which the kinetics at zero force can be extrapolated via explicit theoretical models. The atomic force microscope (AFM) in particular is routinely used to study protein unfolding kinetics, but only rarely protein folding kinetics. The discrepancy arises because mechanical protein refolding studies are more technically challenging. RESULTS: We developed software that can drive and analyse mechanical refolding experiments when used with the commercial AFM setup 'Picoforce AFM', Bruker (previously Digital Instruments). We expect the software to be easily adaptable to other AFM setups. We also developed an improved method for the statistical characterization of protein folding kinetics, and implemented it into an AFM-independent software module. AVAILABILITY: Software and documentation are available at http://code.google.com/p/refolding under Apache License 2.0.

SUMMARY: Hooke is an open source, extensible software intended for analysis of atomic force microscope (AFM)-based single molecule force spectroscopy (SMFS) data. We propose it as a platform on which published and new algorithms for SMFS analysis can be integrated in a standard, open fashion, as a general solution to the current lack of a standard software for SMFS data analysis. Specific features and support for file formats are coded as independent plugins. Any user can code new plugins, extending the software capabilities. Basic automated dataset filtering and semi-automatic analysis facilities are included. AVAILABILITY: Software and documentation are available at (http://code.google.com/p/hooke). Hooke is a free software under the GNU Lesser General Public License.