화학공학소재연구정보센터
Journal of Physical Chemistry B, Vol.119, No.49, 15191-15202, 2015
An Adequate Account of Excluded Volume Is Necessary To Infer Compactness and Asphericity of Disordered Proteins by Forster Resonance Energy Transfer
Single-molecule Forster resonance energy transfer (smFRET) is an important tool for studying disordered proteins. It is commonly utilized to infer structural properties of conformational ensembles by matching experimental average energy transfer < E >(exp) with simulated < E >(sim) computed from the distribution of end-to-end distances in polymer models. Toward delineating the physical basis of such interpretative approaches, we conduct extensive sampling of coarse-grained protein chains with excluded volume to determine the distribution of end-to-end distances conditioned upon given values of radius of gyration R-g and asphericity A. Accordingly, we infer the most probable R-g and A of a protein disordered state by seeking the best fit between < E >(exp) and < E >(sim) among various (R-g,A) subensembles. Application of our method to residues 1-90 of the intrinsically disordered cyclin-dependent kinase (Cdk) inhibitor Sicl results in inferred ensembles with more compact conformations than those inferred by conventional procedures that presume either a Gaussian chain model or the mean-field Sanchez polymer theory. The Sicl compactness we infer is in good agreement with small-angle X-ray scattering data for R-g and NMR measurement of hydrodynamic radius R-h. In contrast, owing to neglect or underappreciation of excluded volume, conventional procedures can significantly overestimate the probabilities of short end-to-end distances, leading to unphysically large smFRET-inferred R-g at high [GdmCl]. It follows that smFRET Sicl data are incompatible with the presumed homogeneously expanded or contracted conformational ensembles in conventional procedures but are consistent with heterogeneous ensembles allowed by our subensemble method of inference. General ramifications of these findings for smFRET data interpretation are discussed.