Pattern Recognition in Single Molecule Force Spectroscopy Data

dc.contributor.advisorDutcher, John
dc.contributor.authorPaulin, Hilary
dc.date.accessioned2013-09-05T22:30:49Z
dc.date.available2013-09-05T22:30:49Z
dc.date.copyright2013-08
dc.date.created2013-08-23
dc.date.issued2013-09-05
dc.degree.departmentDepartment of Physicsen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.degree.programmePhysicsen_US
dc.description.abstractWe have developed an analytical technique for single molecule force spectroscopy (SMFS) data that avoids filtering prior to analysis and performs pattern recognition to identify distinct SMFS events. The technique characterizes the signal similarity between all curves in a data set and generates a hierarchical clustering tree, from which clusters can be identified, aligned, and examined to identify key patterns. This procedure was applied to alpha-lactalbumin (aLA) on polystyrene substrates with flat and nanoscale curvature, and bacteriorhodopsin (bR) adsorbed on mica substrates. Cluster patterns identified for the aLA data sets were associated with different higher-order protein-protein interactions. Changes in the frequency of the patterns showed an increase in the monomeric signal from flat to curved substrates. Analysis of the bR data showed a high level of multiple protein SMFS events and allowed for the identification of a set of characteristic three-peak unfolding events.en_US
dc.identifier.urihttp://hdl.handle.net/10214/7466
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectbiophysicsen_US
dc.subjectsingle molecule biophysicsen_US
dc.subjectsingle molecule force spectroscopyen_US
dc.subjectalpha-lactalbuminen_US
dc.subjectbacteriorhodopsinen_US
dc.subjectpattern recognitionen_US
dc.subjecthierarchical clusteringen_US
dc.subjectclusteringen_US
dc.subjectforce spectroscopyen_US
dc.subjectprotein adsorptionen_US
dc.subjectprotein structureen_US
dc.subjectsurface curvatureen_US
dc.subjectatomic force microscopyen_US
dc.titlePattern Recognition in Single Molecule Force Spectroscopy Dataen_US
dc.typeThesisen_US

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