Single molecule fluorescence resonance energy transfer (smFRET) spectroscopy has provided significant advances in our understanding of the relationship between structure and function in biological systems.  Currently, simplifications must be made for experimental systems, data analysis, and theoretical modeling because biomolecules often exhibit mechanistic or conformational heterogeneity.  For example, it is often necessary to treat biomolecular processes as transitions between two well-define states (e.g. folded vs. unfolded) despite clear experimental evidence to the contrary, (e.g. memory effects or unresolvable states).  Thus, although our realization of these heterogeneities has expanded our view of complexity in biological systems, our desire to understand and engineer such properties is still hindered by our ability to acquire, analyze, and model the data generated in these experiments.   The research presented relates recent efforts to develop enhanced analytical tools based on wavelet and other signal processing algorithms to obtain a multiresolution description of biomolecules that exhibit dynamic heterogeneity.

When complexity meets function: Enhanced analysis of noisy single biomolecule data

243rd National Meeting (2012)

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