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

James Lincoff,João M C Teixeira,Teresa Head-Gordon,Julie D Forman-Kay,Mickael Krzeminski,Gregory-Neal W Gomes,Mojtaba Haghighatlari,Claudiu C Gradinaru

doi:10.1038/s42004-020-0323-0

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.

Highlights

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles
SH3 domain[34], we show through a series of single, dual and complete joint optimizations and cross-validation tests the relative influence of the different data types in scoring the putative structural ensembles
X-EISD approach is able to account for varying levels of uncertainty in both experiment and back-calculation for each data type, and with the very good O(N) scaling with ensemble size facilitates the high number of replicates we can perform, demonstrating the cost-effectiveness of the algorithm

Summary

Introduction

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. 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 Experimental techniques such as X-ray and electron crystallography and microscopy, which have traditionally excelled at determining the atomic structures of protein macromolecules and their complexes, are ill-suited for analysis of proteins with intrinsic or unfolded state disorder[1]. Structure Determination (X-EISD) method, for the statistical modeling of a wide range of experimental data types for proteins with disordered states: NMR chemical shifts and J-couplings[9], homonuclear nuclear Overhauser effects (NOEs)[16,21,22], paramagnetic relaxation enhancements (PREs)[23,24], residual dipolar couplings (RDCs)[25,26], hydrodynamic radii (Rh)[27], and smallangle X-ray scattering (SAXS) intensity curves[28,29]. Starting from either an unoptimized random coil ensemble or using a reported structural ensemble of the unfolded state of the drkN

Methods

Results

Conclusion