Amino-acid selective isotope labeling enables simultaneous overlapping signal decomposition and information extraction from NMR spectra.

Signal overlapping is a major bottleneck for protein NMR analysis. We propose a new method, stable-isotope-assisted parameter extraction (SiPex), to resolve overlapping signals by a combination of amino-acid selective isotope labeling (AASIL) and tensor decomposition. The basic idea of Sipex is that overlapping signals can be decomposed with the help of intensity patterns derived from quantitative fractional AASIL, which also provides amino-acid information. In SiPex, spectra for protein characterization, such as 15N relaxation measurements, are assembled with those for amino-acid information to form a four-order tensor, where the intensity patterns from AASIL contribute to high decomposition performance even if the signals share similar chemical shift values or characterization profiles, such as relaxation curves. The loading vectors of each decomposed component, corresponding to an amide group, represent both the amino-acid and relaxation information. This information link provides an alternative protein analysis method that does not require "assignments" in a general sense; i.e., chemical shift determinations, since the amino-acid information for some of the residues allows unambiguous assignment according to the dual selective labeling. SiPex can also decompose signals in time-domain raw data without Fourier transform, even in non-uniformly sampled data without spectral reconstruction. These features of SiPex should expand biological NMR applications by overcoming their overlapping and assignment problems.

Advances in stable isotope assisted labeling strategies with information science.

Stable-isotope (SI) labeling of proteins is an essential technique to investigate their structures, interactions or dynamics by nuclear magnetic resonance (NMR) spectroscopy. The assignment of the main-chain signals, which is the fundamental first step in these analyses, is usually achieved by a sequential assignment method based on triple resonance experiments. Independently of the triple resonance experiment-based sequential assignment, amino acid-selective SI labeling is beneficial for discriminating the amino acid type of each signal; therefore, it is especially useful for the signal assignment of difficult targets. Various combinatorial selective labeling schemes have been developed as more sophisticated labeling strategies. In these strategies, amino acids are represented by combinations of SI labeled samples, rather than simply assigning one amino acid to one SI labeled sample as in the case of conventional amino acid-selective labeling. These strategies have proven to be useful for NMR analyses of difficult proteins, such as those in large complex systems, in living cells, attached or integrated into membranes, or with poor solubility. In this review, recent advances in stable isotope assisted labeling strategies will be discussed.

Stable isotope labeling strategy based on coding theory.

We describe a strategy for stable isotope-aided protein nuclear magnetic resonance (NMR) analysis, called stable isotope encoding. The basic idea of this strategy is that amino-acid selective labeling can be considered as "encoding and decoding" processes, in which the information of amino acid type is encoded by the stable isotope labeling ratio of the corresponding residue and it is decoded by analyzing NMR spectra. According to the idea, the strategy can diminish the required number of labelled samples by increasing information content per sample, enabling discrimination of 19 kinds of non-proline amino acids with only three labeled samples. The idea also enables this strategy to combine with information technologies, such as error detection by check digit, to improve the robustness of analyses with low quality data. Stable isotope encoding will facilitate NMR analyses of proteins under non-ideal conditions, such as those in large complex systems, with low-solubility, and in living cells.

Cell-Free Expression of Sodium Channel Domains for Pharmacology Studies. Noncanonical Spider Toxin Binding Site in the Second Voltage-Sensing Domain of Human Nav1.4 Channel.

Voltage-gated sodium (NaV) channels are essential for the normal functioning of cardiovascular, muscular, and nervous systems. These channels have modular organization; the central pore domain allows current flow and provides ion selectivity, whereas four peripherally located voltage-sensing domains (VSDs-I/IV) are needed for voltage-dependent gating. Mutations in the S4 voltage-sensing segments of VSDs in the skeletal muscle channel NaV1.4 trigger leak (gating pore) currents and cause hypokalemic and normokalemic periodic paralyses. Previously, we have shown that the gating modifier toxin Hm-3 from the crab spider Heriaeus melloteei binds to the S3-S4 extracellular loop in VSD-I of NaV1.4 channel and inhibits gating pore currents through the channel with mutations in VSD-I. Here, we report that Hm-3 also inhibits gating pore currents through the same channel with the R675G mutation in VSD-II. To investigate the molecular basis of Hm-3 interaction with VSD-II, we produced the corresponding 554-696 fragment of NaV1.4 in a continuous exchange cell-free expression system based on the Escherichia coli S30 extract. We then performed a combined nuclear magnetic resonance (NMR) and electron paramagnetic resonance spectroscopy study of isolated VSD-II in zwitterionic dodecylphosphocholine/lauryldimethylamine-N-oxide or dodecylphosphocholine micelles. To speed up the assignment of backbone resonances, five selectively 13C,15N-labeled VSD-II samples were produced in accordance with specially calculated combinatorial scheme. This labeling approach provides assignment for ∼50% of the backbone. Obtained NMR and electron paramagnetic resonance data revealed correct secondary structure, quasi-native VSD-II fold, and enhanced ps-ns timescale dynamics in the micelle-solubilized domain. We modeled the structure of the VSD-II/Hm-3 complex by protein-protein docking involving binding surfaces mapped by NMR. Hm-3 binds to VSDs I and II using different modes. In VSD-II, the protruding ß-hairpin of Hm-3 interacts with the S1-S2 extracellular loop, and the complex is stabilized by ionic interactions between the positively charged toxin residue K24 and the negatively charged channel residues E604 or D607. We suggest that Hm-3 binding to these charged groups inhibits voltage sensor transition to the activated state and blocks the depolarization-activated gating pore currents. Our results indicate that spider toxins represent a useful hit for periodic paralyses therapy development and may have multiple structurally different binding sites within one NaV molecule.

Protein labeling strategies for liquid-state NMR spectroscopy using cell-free synthesis.

Preparation of a protein sample for liquid-state nuclear magnetic resonance (NMR) spectroscopy analysis requires optimization of many parameters. This review describes labeling strategies for obtaining assignments of protein resonances. Particular emphasis is placed on the advantages of cell-free protein production, which enables exclusive labeling of the protein of interest, thereby simplifying downstream processing steps and increasing the availability of different labeling strategies for a target protein. Furthermore, proteins can be synthesized in milligram yields, and the open nature of the cell-free system allows the addition of stabilizers, scrambling inhibitors or hydrophobic solubilization environments directly during the protein synthesis, which is especially beneficial for membrane proteins. Selective amino acid labeling of the protein of interest, the possibility of addressing scrambling issues and avoiding the need for labile amino acid precursors have been key factors in enabling the introduction of new assignment strategies based on different labeling schemes as well as on new pulse sequences. Combinatorial selective labeling methods have been developed to reduce the number of protein samples necessary to achieve a complete backbone assignment. Furthermore, selective labeling helps to decrease spectral overlap and overcome size limitations for solution NMR analysis of larger complexes, oligomers, intrinsically disordered proteins and membrane proteins.