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Introduction

Protein folding problem

Current approaches/state of the field as a whole

Single molecule techniques

Literature review

Single molecule force spectroscopy

Original force spectroscopy paper?
Bustamante and lambda DNA (1994)

Demonstrates that labda DNA force-extension curves are well modeled as WLCs, introducing WLCs in the context of force spectroscopy.

Evans and biotin/streptavidin (1997)

Unfolding and theory. Example on ligand-receptor binding.

Rief and Titin (1997)

First force spectroscopy on a multi-globular single protein. Reversible suggests unfolding of individual domains.

Carrion-Vazquez review (2000)

Geometry argument for pulling angle irrelevance.

Li review (2000)

Unfolding not due to pulling domains off the substrate surface.

Carrion-Vazquez and I27 (1997)

First force spectroscopy on synthesized multi-globular I27. Comparisons of unfolding constants with chemical techniques.

Temperature dependent unfolding

Hyeon and Thirumalai (2003)

Extend Kramers’ theory to determine energy landscape roughness.

Yang and temperature control (2006)

Previous measurements on temperature dependent protein unfolding.

Goals

Protein unfolding and related theories

Bell’s model

Kramer’s model

Temperature and energy roughness

Jarzynsky’s inequality

AFM cantilever calibration

Sources of error

Cantilever spring constant effect

Evans, 2001

pages 115–116 and Figure 2.a

Discusses energy landscape curvature and rebinding effects. (Aha, this is what Noy wanted me to talk about).

Walton et al. 2008

Biophys J, 94 (2008), 7 2621–2630.

Energy landscape E E* = E(x) + ½kx² E** = E(x) - Fx + ½kx² F_b = x_b/k_B T k_off = k₀ exp([F_R - ½kx_b]/F_b)

Scaffold effect

Competetion between number of parallel unfolders and effective loading rate. The unfolding rate must be binned somehow, and the scaffold effect makes binning difficult. k(F,Delta F,kappa) = unfolding rate of proteins present within Delta F of an average tension F applied via a linker of stiffness kappa=dF/dx (kappa to deal with the Cantilever spring constant effect. I suggest we use the censored survival statistics common to medical studies, and hopefully see exponential decay for each force. We’ll probably have to lump similar loading rates (=kappa∙v) together, and the more gently loaded proteins will survive longer (having a lower mean force after the same length of time in the [F, F+Delta F] bin). The general trend will also be towards accelerated exponential decay, since the mean forces of all proteins will increase throughout the bin.

Zinober et al. 2002

Protein Science, 11 (2002), 12 2759–2765. Monte Carlo simulation and experiment on I27 and mutants.

Censored survival statistics, Kaplan-Meier

Kaplan, E.L. & Meier, P. 1958

“Nonparametric estimation from incomplete observations,” Journal of the American Statistical Association, 53, 457-481 (1958).

Chris Barker 2009

“The Mean, Median, and Confidence Intervals of the Kaplan-Meier Survival Estimate—Computations and Applications,” The American Statistician, 63(1), 78–80 (2009). DOI 10.1198/tast.2009.0015

Newcombe, Robert G. 1998

“Two-Sided Confidence Intervals for the Single Proportion: Comparison of Seven Methods,” Statistics in Medicine, 17, 857-872 (1998).

Wilson, E. B. 1927

“Probable Inference, the Law of Succession, and Statistical Inference,” Journal of the American Statistical Association, 22, 209-212 (1927).

Greenwood, M. 1926

“The natural duration of cancer.” Reports on Public Health and Medical Subjects 33, 1–26. Her Majesty’s Stationery Office, London.

Meeker, W.Q., and Escobar, L.A. 1998

“Statistical Methods for Reliability Data”, John Wiley & Sons, Inc., New York, 1998. Greenwood’s formula for Kaplan-Meier error. Example calculations: http://www.weibull.com/LifeDataWeb/nonparametric_analysis.htm

Measurements

Temperature dependent energy landscape roughness

Effect of cantilever stiffness on unfolding behavior

Software

Experiment control/analysis software

Sawsim

Summary/future directions