Abstract
TAR DNA-binding protein 43 (TDP-43) inclusions have been found in Amyotrophic lateral sclerosis (ALS) and several other neurodegenerative diseases. Many studies suggest the involvement of RNA recognition motifs (RRMs) in TDP-43 proteinopathy. To elucidate the structural stability and the unfolding dynamics of RRMs, we have carried out atomistic molecular dynamics simulations at two different temperatures (300 and 500 K). The simulations results indicate that there are distinct structural differences in the unfolding pathway between the two domains and RRM1 unfolds faster than RRM2 in accordance with the lower thermal stability found experimentally. The unfolding behaviors of secondary structures showed that the α-helix was more stable than β-sheet and structural rearrangements of β-sheets results in formation of additional α-helices. At higher temperature, RRM1 exhibit increased overall flexibility and unfolding than RRM2. The temperature-dependent free energy landscapes consist of multiple metastable states stabilized by non-native contacts and hydrogen bonds in RRM2, thus rendering the RRM2 more prone to misfolding. The structural rearrangements of RRM2 could lead to aberrant protein–protein interactions that may account for enhanced aggregation and toxicity of TDP-43. Our analysis, thus identify the structural and thermodynamic characteristics of the RRMs of TDP-43, which will serve to uncover molecular mechanisms and driving forces in TDP-43 misfolding and aggregation.
Abbreviations:
- TDP-43: TAR DNA-binding protein 43
- ALS: Amyotrophic lateral sclerosis
- RRMs: RNA recognition motifs
- RRM1: RNA recognition motif 1
- RRM2: RNA recognition motif 2
- tRRMs: RRM1+ RRM2
- MD: Molecular dynamics
- FTLD: Frontotemporal lobar dementia
- NES: Nuclear export sequence
- NLS: Nuclear localization sequence
- NTD: N-terminal domain of TDP-43
- RMSD: Root mean square deviation
- Rg: Radius of gyration
- RMSF: Root-mean-square fluctuation
- Nc: Fraction of native contacts
- SASA: Solvent accessible surface area
- PCA: Principal component analysis
- ED: Essential dynamics
- FEL: Free energy landscape
Acknowledgments
A.P. and V.K. sincerely thank Science and Engineering Research Board (SERB), Government of India for the award of Young Scientist, YSS/2015/000228 and SB/YS/LS-161/2014. Authors sincerely thank DST-SERB for providing the GPU computational Facility.