Extended experimental inferential structure determination method in determining the structural ensembles of disordered protein states

Abstract

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. Here we introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, which calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii (Rh), single molecule fluorescence Förster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.

Exploring Curated Conformational Ensembles of Intrinsically Disordered Proteins in the Protein Ensemble Database

“…Links to the source publications are provided for users to easily access the supplementary information and motivation of the ensemble modeling experiment. Besides SAXS and NMR, PED also has a significant number of smFRET‐based entries (Dong et al., 2017; Fuertes et al., 2017; Gomes et al., 2020; Lincoff et al., 2020) that are all combined with SAXS and/or NMR. In the future, we anticipate receiving ensembles generated by other methods as well, including circular dichroism (CD; Nagy, Igaev, Jones, Hoffmann, & Grubmüller, 2019), high‐speed atomic force microscopy (HS‐AFM; Kodera et al., 2021), electrospray ionization mass spectrometry (ESI‐MS; D'Urzo et al., 2015), and electron paramagnetic resonance (EPR) spectroscopy (Karthikeyan et al., 2018).…”

Combining Experimental Data and Computational Methods for the Non-Computer Specialist

“…Hence, a large number of programs have been created to select ensembles that fit the experimental data. For instance, ENSEMBLE [ 28 ], X-EISD [ 29 ], BME [ 30 ], and MESMER [ 19 ] were used to select conformations that matched data from several different experiments. These approaches differ in the way in which the initial ensemble is generated as well as in the algorithm used to search and select the final ensemble.…”

Refining conformational ensembles of flexible proteins against small-angle X-ray scattering data

“…Our approach is based on a reweighting approach that is rooted in Bayesian inference (23)(24)(25)(26)(27)(28)(29)(30)(31) and the Maximum-entropy principle (32)(33)(34)(35)(36)(37). While these methods show similarities to other approaches based for example on genetic algorithms (6,38,39) or Monte Carlo processes (40,41), they differ in how they balance prior information (often encoded in a force field) with the experimental data.…”