4.6 Article

Characterization of Weak Protein Domain Structure by Spin-Label Distance Distributions

Journal

FRONTIERS IN MOLECULAR BIOSCIENCES
Volume 8, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fmolb.2021.636599

Keywords

low-complexity domains; RNA-binding proteins; ensemble structure; EPR spectroscopy; random coil; intrinsically disordered; liquid-liquid phase separation

Funding

  1. Swiss National Science Foundation [200020_188467, hnRNP A1, CRSII5_170976]
  2. Swiss National Science Foundation (SNF) [200020_188467, CRSII5_170976] Funding Source: Swiss National Science Foundation (SNF)

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The study investigates the impact of deviation of conformational ensemble of intrinsically disordered proteins from random coil on their function, and discusses the quantification of this deviation through distance distributions. By analyzing two proteins, it examines whether deviation from random coil can be detected by distance distributions, and proposes a solution to tackle this issue.
Function of intrinsically disordered proteins may depend on deviation of their conformational ensemble from that of a random coil. Such deviation may be hard to characterize and quantify, if it is weak. We explored the potential of distance distributions between spin labels, as they can be measured by electron paramagnetic resonance techniques, for aiding such characterization. On the example of the intrinsically disordered N-terminal domain 1-267 of fused in sarcoma (FUS) we examined what such distance distributions can and cannot reveal on the random-coil reference state. On the example of the glycine-rich domain 188-320 of heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) we studied whether deviation from a random-coil ensemble can be robustly detected with 19 distance distribution restraints. We discuss limitations imposed by ill-posedness of the conversion of primary data to distance distributions and propose overlap of distance distributions as a fit criterion that can tackle this problem. For testing consistency and size sufficiency of the restraint set, we propose jack-knife resampling. At current desktop computers, our approach is expected to be viable for domains up to 150 residues and for between 10 and 50 distance distribution restraints.

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