Efficient Strategy for the Rapid Backbone Assignment of Membrane Proteins

Abstract

Investigations of membrane proteins pose one of the biggest current challenges in structural biology. Recent advances in protein production techniques based on cell-free transcription/translation methods have, however, opened new opportunities in this area. Here, we report an efficient protocol for the backbone assignment of membrane proteins as the first step of NMR-based structure determination.

Stable isotope labeling strategy based on coding theory

“…Signal overlapping usually impede amino acid discrimination especially in CSL, therefore, the number of labeled aminoacids is reduced according to its occurrence in some CSL (Trbovic et al 2005;Wu et al 2006;Sobhanifar et al 2010;Löhr et al 2012). In the present study, we labeled all of the non-proline 19 amino acids, however, quantitative peak fitting used for decoding information in SiCode solved the signal overlapping issue as demonstrated.…”

“…As described above, conventional CSL schemes generally uses two SI-labeling levels, enabling that 1 bit information is contained in each labeled sample (Parker et al 2004;Shi et al 2004;Trbovic et al 2005;Staunton et al 2006;Wu et al 2006;Maslennikov et al 2010;Sobhanifar et al 2010;Hefke et al 2011;Krishnarjuna et al 2011;Jaipuria et al 2012; Maslennikov and Choe 2013). In the previously mentioned triple selective CSL approach (Löhr et al 2012), three SIlabeling types can be discriminated for both residues i and i -1: unlabeled, 15 N-labeled, or 2-13 C/ 15 N-labeled for the residue i and unlabeled, 1-13 C-labeled, or 1,2-13 C-labeled for the residue i -1, respectively, being considered that 1 trit (ternary digit) information is contained in each sample.…”

“…However, for the discrimination of all amino-acids, these simple AASIL schemes require a large number of samples, which are typically the same as the number of amino acids (19 for nitrogen or 20 for carbon). For this reason, various combinatorial selective labeling (CSL) schemes (Parker et al 2004;Shi et al 2004;Trbovic et al 2005;Staunton et al 2006;Wu et al 2006;Maslennikov et al 2010;Sobhanifar et al 2010;Hefke et al 2011;Krishnarjuna et al 2011;Jaipuria et al 2012;Löhr et al 2012; Maslennikov and Choe 2013) were developed to reduce required number of samples, by representing amino acids as combination of SI labeled samples rather than simply assigning one amino acid to one SI labeled sample. For example, a CSL scheme developed by Parker et al (2004), which is based on the dual selective approach, can discriminate 16 amino-acids with one uniformly 13 C and 15 N-labeled reference and four selectively (100 or 0 % for 13 C and 100 or 50 % for 15 N, respectively) labeled samples.…”

“…Otting and colleagues reported simpler CSL scheme using five samples (Wu et al 2006), based on the single selective 15 N-labeling approach, in which spectral overlaps were diminished by labeling one amino acid with high occurrence and at most three ones with low occurrence in each sample. Dötche and colleagues developed focused CSL (Trbovic et al 2005;Sobhanifar et al 2010), in which 6 or 7 amino acids frequently appearing in transmembrane regions of membrane proteins were labeled with 15 N or 1-13 C. They further improved the CSL to discriminate up to 20 amino-acids with a number of samples labeled with 15 N and/or 13 C by using dual selective approach (Hefke et al 2011), or to discriminate 12 amino-acids with only three samples by introducing triple selective approach (Löhr et al 2012), in which the samples were labeled with the combination of 15 N, 1-13 C, and 13 C/ 15 N. Choe and colleagues also improved membrane protein-focused CSL (Maslennikov et al 2010;Maslennikov and Choe 2013) to discriminate up to 19 amino-acids except for glutamate with six samples simply labeled with 13 C and/or 15 N. A couple of computational methods for designing labeling patterns for CSL were employed (Maslennikov et al 2010;Hefke et al 2011;Maslennikov and Choe 2013) in order to maximize assignable residues by using dual selective approaches.…”

Transmembrane segment enhanced labeling as a tool for the backbone assignment of α-helical membrane proteins

“…Although the high internal flexibility of an unfolded polypeptide chain leads to slow relaxation, thereby enabling the use of pulse sequences with more and longer transfer delays for resolving overlap (12), for large membrane proteins solubilized in micelles, only the most basic experiments yield enough sensitivity. In a recent publication we demonstrated that the resulting overlap problem can be overcome, allowing for the sequential backbone assignment of membrane proteins Ͼ200 aa (13,14). Our strategy was based on a two-step approach in which we first use standard triple-resonance experiments to obtain as many assignments as possible and then use a double-labeling scheme to select amino acid pairs with a two-dimensional version of the HNCO experiment to sequence-specifically identify additional amino acids (15)(16)(17).…”