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An integrative characterization of proline cisand transconformers in a disordered peptide

Authors :
Pettitt, Alice J.
Shukla, Vaibhav Kumar
Figueiredo, Angelo Miguel
Newton, Lydia S.
McCarthy, Stephen
Tabor, Alethea B.
Heller, Gabriella T.
Lorenz, Christian D.
Hansen, D. Flemming
Source :
Biophysical Journal; November 2024, Vol. 123 Issue: 21 p3798-3811, 14p
Publication Year :
2024

Abstract

Intrinsically disordered proteins (IDPs) often contain proline residues that undergo cis/transisomerization. While molecular dynamics (MD) simulations have the potential to fully characterize the proline cisand transsubensembles,they are limited by the slow timescales of isomerization and force field inaccuracies. NMR spectroscopy can report on ensemble-averaged observables for both the cis-proline and trans-proline states, but a full atomistic characterization of these conformers is challenging. Given the importance of proline cis/transisomerization for influencing the conformational sampling of disordered proteins, we employed a combination of all-atom MD simulations with enhanced sampling (metadynamics), NMR, and small-angle x-ray scattering (SAXS) to characterize the two subensembles of the ORF6 C-terminal region (ORF6CTR) from SARS-CoV-2 corresponding to the proline-57 (P57) cisand transstates. We performed MD simulations in three distinct force fields: AMBER03ws, AMBER99SB-disp, and CHARMM36m, which are all optimized for disordered proteins. Each simulation was run for an accumulated time of 180–220 μs until convergence was reached, as assessed by blocking analysis. A good agreement between the cis-P57 populations predicted from metadynamic simulations in AMBER03ws was observed with populations obtained from experimental NMR data. Moreover, we observed good agreement between the radius of gyration predicted from the metadynamic simulations in AMBER03ws and that measured using SAXS. Our findings suggest that both the cis-P57 and trans-P57 conformations of ORF6CTRare extremely dynamic and that interdisciplinary approaches combining both multiscale computations and experiments offer avenues to explore highly dynamic states that cannot be reliably characterized by either approach in isolation.

Details

Language :
English
ISSN :
00063495 and 15420086
Volume :
123
Issue :
21
Database :
Supplemental Index
Journal :
Biophysical Journal
Publication Type :
Periodical
Accession number :
ejs67495916
Full Text :
https://doi.org/10.1016/j.bpj.2024.09.028