Pattern Recognition of Single-Molecule Force Spectroscopy Data

摘要

Motivation: Misfolding of membrane proteins plays an important role in many human diseases such as retinitis pigmentosa, hereditary deafness, and diabetes insipidus. Little is known about membrane proteins as there are only a very few high-resolution structures. Single-molecule force spectroscopy is a novel technique which measures the force necessary to pull a protein out of a membrane. Such force curves contain valuable information about the protein's structure, conformation, and inter- and intra-molecular forces. High-throughput force spectroscopy experiments generate hundreds of force curves including spurious and good curves, which correspond to different unfolding pathways and to different functional states of an investigated membrane protein. Results: In the present work we propose a novel application of automated unfolding pattern recognition routines. We apply our method to datasets from unfolding experiments of bacteriorhodopsin (bR) and bovine rhodopsin (Rho). As a result, we discuss the different unfolding pathways of bR, and two functional states for Rho could be observed . Overall, the algorithm tackles the force spectroscopy bottleneck and leads to more consistent and reproducible results paving the way for high-throughput analysis of structural features of membrane proteins.