Efficient unfolding pattern recognition in single molecule force spectroscopy data

BackgroundSingle-molecule force spectroscopy (SMFS) is a technique that measures the force necessary to unfold a protein. SMFS experiments generate Force-Distance (F-D) curves. A statistical analysis of a set of F-D curves reveals different unfolding pathways. Information on protein structure, conformation, functional states, and inter- and intra-molecular interactions can be derived.ResultsIn the present work, we propose a pattern recognition algorithm and apply our algorithm to datasets from SMFS experiments on the membrane protein bacterioRhodopsin (bR). We discuss the unfolding pathways found in bR, which are characterised by main peaks and side peaks. A main peak is the result of the pairwise unfolding of the transmembrane helices. In contrast, a side peak is an unfolding event in the alpha-helix or other secondary structural element. The algorithm is capable of detecting side peaks along with main peaks.Therefore, we can detect the individual unfolding pathway as the sequence of events labeled with their occurrences and co-occurrences special to bR\u27s unfolding pathway. We find that side peaks do not co-occur with one another in curves as frequently as main peaks do, which may imply a synergistic effect occurring between helices. While main peaks co-occur as pairs in at least 50% of curves, the side peaks co-occur with one another in less than 10% of curves. Moreover, the algorithm runtime scales well as the dataset size increases.ConclusionsOur algorithm satisfies the requirements of an automated methodology that combines high accuracy with efficiency in analyzing SMFS datasets. The algorithm tackles the force spectroscopy analysis bottleneck leading to more consistent and reproducible results

POSSIBLE USE OF DNAS IN SINGLE MOLECULE FORCE SPECTROSCOPY TO PROBE SINGLE PROTEIN UNFOLDING SIGNATURES

Master'sMASTER OF SCIENC

Open source single molecule force spectroscopy

Single molecule force spectroscopy (SMFS) experiments provide an experimental benchmark for testing simulated and theoretical predictions of protein unfolding behavior. Despite it use since 1997, the labs currently engaged in SMFS use in-house software and procedures for critical tasks such as cantilever calibration and Monte Carlo unfolding simulation. Besides wasting developer time producing and maintaining redundant implementations, the lack of transparency makes it more difficult to share data and techniques between labs, which slows progress. In some cases it can also lead to ambiguity as to which of several similar approaches, correction factors, etc. were used in a particular paper.In this thesis, I introduce an SMFS sofware suite for cantilever calibration (calibcant), experiment control (unfold-protein), analysis (Hooke), and postprocessing (sawsim) in the context of velocity clamp unfolding of I27 octomers in buffers with varying concentrations of CaCl2. All of the tools are licensed under open source licenses, which allows SMFS researchers to centralize future development. Where possible, care has been taken to keep these packages operating system (OS) agnostic. The experiment logic in unfold-protein and calibcant is still nominally OS agnostic, but those packages depend on more fundamental packages that control the physical hardware in use. At the bottom of the physical-interface stack are the Comedi drivers from the Linux kernel. Users running other operating systems should be able to swap in analogous low level physical-interface packages if Linux is not an option.Ph.D., Physics -- Drexel University, 201