1H,13C and 15N chemical shift assignments of the SUD domains of SARS-CoV-2 non-structural protein 3c: "The SUD-M and SUD-C domains".
Angelo GalloAikaterini C TsikaNikolaos K FourkiotisFrancesca CantiniLucia BanciSridhar SreeramuluHarald SchwalbeGeorgios A SpyrouliasPublished in: Biomolecular NMR assignments (2021)
SARS-CoV-2 RNA, nsP3c (non-structural Protein3c) spans the sequence of the so-called SARS Unique Domains (SUDs), first observed in SARS-CoV. Although the function of this viral protein is not fully elucidated, it is believed that it is crucial for the formation of the replication/transcription viral complex (RTC) and of the interaction of various viral "components" with the host cell; thus, it is essential for the entire viral life cycle. The first two SUDs, the so-called SUD-N (the N-terminal domain) and SUD-M (domain following SUD-N) domains, exhibit topological and conformational features that resemble the nsP3b macro (or "X") domain. Indeed, they are all folded in a three-layer α/β/α sandwich structure, as revealed through crystallographic structural investigation of SARS-CoV SUDs, and they have been attributed to different substrate selectivity as they selectively bind to oligonucleotides. On the other hand, the C-terminal SUD (SUD-C) exhibit much lower sequence similarities compared to the SUD-N & SUD-M, as reported in previous crystallographic and NMR studies of SARS-CoV. In the absence of the 3D structures of SARS-CoV-2, we report herein the almost complete NMR backbone and side-chain resonance assignment (1H,13C,15N) of SARS-CoV-2 SUD-M and SUD-C proteins, and the NMR chemical shift-based prediction of their secondary structure elements. These NMR data will set the base for further understanding at the atomic-level conformational dynamics of these proteins and will allow the effective screening of a large number of small molecules as binders with potential biological impact on their function.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- magnetic resonance
- high resolution
- amino acid
- single cell
- molecular dynamics simulations
- solid state
- molecular dynamics
- mass spectrometry
- life cycle
- single molecule
- stem cells
- transcription factor
- mesenchymal stem cells
- protein protein
- deep learning
- climate change
- cell therapy
- artificial intelligence
- nucleic acid
- energy transfer