Automatic classification of single-molecule force spectroscopy traces from heterogeneous samples

Single-molecule force spectroscopy (SMFS) experiments pose the challenge of analyzing 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).