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Price, A. C.

Publications and source records attributed to Price, A. C..

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The sequence dependent search mechanism of EcoRI

One-dimensional search is an essential step in DNA target recognition. Theoretical studies have suggested that the sequence dependence of one-dimensional diffusion can help resolve the competing demands of fast search and high target affinity, a conflict known as the speed-selectivity paradox. The resolution requires that the diffusion energy landscape is correlated with the underlying specific binding energies. In this work, we report observations of one-dimensional search by QD labeled EcoRI. Our data supports the view that proteins search DNA via rotation coupled sliding over a corrugated energy landscape. We observed that while EcoRI primarily slides along DNA at low salt concentrations, at higher concentrations its diffusion is a combination of sliding and hopping. We also observed long-lived pauses at genomic star sites which differ by a single nucleotide from the target sequence. To reconcile these observations with prior biochemical and structural data, we propose a model of search in which the protein slides over a sequence independent energy landscape during fast search, but rapidly interconverts with a \"hemi-specific\" binding mode in which a half site is probed. This half site interaction stabilizes the transition to a fully specific mode of binding which can then lead to target recognition.

biophysics

ANALYZING DWELL TIMES WITH THE GENERALIZED METHOD OF MOMENTS

The Generalized Method of Moments (GMM) is a statistical method for the analysis of samples from random processes. First developed for the analysis of econometric data, the method is here formulated to extract hidden kinetic parameters from measurements of single molecule dwell times. Our method is based on the analysis of cumulants of the measured dwell times. We develop a general form of an objective function whose minimization can return estimates of decay parameters for any number of intermediates directly from the data. We test the performance of our technique using both simulated and experimental data. We also compare the performance of our method to nonlinear least-squares minimization (NL-LSQM), a commonly-used technique for analysis of single molecule dwell times. Our findings indicate that the GMM performs comparably to NL-LSQM over most of the parameter range we explore. It offers some benefits compared with NL-LSQM in that it does not require binning, exhibits slightly lower bias and variance with small sample sizes (N<20), and is somewhat superior in identifying fast decay times with these same low count data sets. Our results show that the GMM can be a useful tool and complements standard approaches to analysis of single molecule dwell times.

biophysics