How accurate are polymer models in the analysis of Forster resonance energy transfer experiments on proteins?
E. P. O'Brien, G. Morrison, B. R. Brooks, D. Thirumalai

TL;DR
This study evaluates the accuracy of common polymer models in interpreting FRET data on proteins, revealing that while mean distances are well-estimated, other parameters like Rg and persistence length are less reliable, and proposing a self-consistency test for model validation.
Contribution
The paper introduces a self-consistency test to evaluate the validity of polymer models in FRET analysis of protein DSEs, highlighting limitations of Gaussian models.
Findings
Mean end-to-end distance can be inferred with less than 10% error.
Radius of gyration and persistence length estimates can have up to 25% error.
Gaussian model inadequately describes the DSE P(R) in experimental data.
Abstract
Single molecule Forster resonance energy transfer (FRET) experiments are used to infer the properties of the denatured state ensemble (DSE) of proteins. From the measured average FRET efficiency, <E>, the distance distribution P(R) is inferred by assuming that the DSE can be described as a polymer. The single parameter in the appropriate polymer model (Gaussian chain, Wormlike chain, or Self-avoiding walk) for P(R) is determined by equating the calculated and measured <E>. In order to assess the accuracy of this "standard procedure," we consider the generalized Rouse model (GRM), whose properties [<E> and P(R)] can be analytically computed, and the Molecular Transfer Model for protein L for which accurate simulations can be carried out as a function of guanadinium hydrochloride (GdmCl) concentration. Using the precisely computed <E> for the GRM and protein L, we infer P(R) using the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
