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

Abstract

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). We illustrate the performance of our method on two prototypical datasets: ∼50000 traces from a sample containing tandem GB1 and ∼400000 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. https://github.com/ninailieva/SMFS_clustering. Supplementary data are available at Bioinformatics online.