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- Biopolymers in vivo (1)
- Computational models (1)
- Data processing (1)
- RNA recognition motif (1)
- SF3b145 (1)
- SF3b49 (1)
- Solution-state NMR (1)
- U2 snRNP (1)
- non-uniform sampling 2 (1)
- nuclear magnetic resonance (1)
The spliceosomal protein SF3b49, a component of the splicing factor 3b (SF3b) protein complex in the U2 small nuclear ribonucleoprotein, contains two RNA recognition motif (RRM) domains. In yeast, the first RRM domain (RRM1) of Hsh49 protein (yeast orthologue of human SF3b49) reportedly interacts with another component, Cus1 protein (orthologue of human SF3b145). Here, we solved the solution structure of the RRM1 of human SF3b49 and examined its mode of interaction with a fragment of human SF3b145 using NMR methods. Chemical shift mapping showed that the SF3b145 fragment spanning residues 598-631 interacts with SF3b49 RRM1, which adopts a canonical RRM fold with a topology of β1-α1-β2-β3-α2-β4. Furthermore, a docking model based on NOESY measurements suggests that residues 607-616 of the SF3b145 fragment adopt a helical structure that binds to RRM1 predominantly via α1, consequently exhibiting a helix-helix interaction in almost antiparallel. This mode of interaction was confirmed by a mutational analysis using GST pull-down assays. Comparison with structures of all RRM domains when complexed with a peptide found that this helix-helix interaction is unique to SF3b49 RRM1. Additionally, all amino acid residues involved in the interaction are well conserved among eukaryotes, suggesting evolutionary conservation of this interaction mode between SF3b49 RRM1 and SF3b145.
To date, in-cell NMR has elucidated various aspects of protein behaviour by associating structures in physiological conditions. Meanwhile, current studies of this method mostly have deduced protein states in cells exclusively based on ‘indirect’ structural information from peak patterns and chemical shift changes but not ‘direct’ data explicitly including interatomic distances and angles. To fully understand the functions and physical properties of proteins inside cells, it is indispensable to obtain explicit structural data or determine three-dimensional (3D) structures of proteins in cells. Whilst the short lifetime of cells in a sample tube, low sample concentrations, and massive background signals make it difficult to observe NMR signals from proteins inside cells, several methodological advances help to overcome the problems. Paramagnetic effects have an outstanding potential for in-cell structural analysis. The combination of a limited amount of experimental in-cell data with software for ab initio protein structure prediction opens an avenue to visualise 3D protein structures inside cells. Conventional nuclear Overhauser effect spectroscopy (NOESY)-based structure determination is advantageous to elucidate the conformations of side-chain atoms of proteins as well as global structures. In this article, we review current progress for the structure analysis of proteins in living systems and discuss the feasibility of its future works.
Investigating three-dimensional (3D) structures of proteins in living cells by in-cell nuclear magnetic resonance (NMR) spectroscopy opens an avenue towards understanding the structural basis of their functions and physical properties under physiological conditions inside cells. In-cell NMR provides data at atomic resolution non-invasively, and has been used to detect protein-protein interactions, thermodynamics of protein stability, the behavior of intrinsically disordered proteins, etc. in cells. However, so far only a single de novo 3D protein structure could be determined based on data derived only from in-cell NMR. Here we introduce methods that enable in-cell NMR protein structure determination for a larger number of proteins at concentrations that approach physiological ones. The new methods comprise (1) advances in the processing of non-uniformly sampled NMR data, which reduces the measurement time for the intrinsically short-lived in-cell NMR samples, (2) automatic chemical shift assignment for obtaining an optimal resonance assignment, and (3) structure refinement with Bayesian inference, which makes it possible to calculate accurate 3D protein structures from sparse data sets of conformational restraints. As an example application we determined the structure of the B1 domain of protein G at about 250 μM concentration in living E. coli cells.