•Combinatorial amino acid selective labeling strategies were developed.•These strategies have proven to be useful for NMR analyses of difficult proteins.•The strategy is improved by the combination 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.

By using in-cell NMR experiments, we have demonstrated that the protein folding state in cells is significantly influenced by the cellular health conditions. hAK1 was denatured in cells under stressful culture conditions, while it remained functional and properly folded in cells continuously supplied with a fresh medium.

Immuno-PET is a promising approach for improved cancer diagnosis, by taking advantage of the high specificity of antibodies. Here, we present a novel cell-free protein synthesis method for preparing a positron emitter labeled-antibody. Functional anti-human EGFRvIII single chain Fv, MR1-1, was successfully labeled with carbon-11 (half-life = 20.4 min) in 5 min (36% yield) by the direct incorporation of the clinical PET tracer, l-[11C]methionine. The product [11C]MR1-1 was easily and rapidly isolated with high radiochemical purity (>95%) from the reaction solution, by affinity purification. This method would be widely applicable to the preparation of radiolabeled antibodies for PET imaging.

During recent years, the targets of protein structure analysis using nuclear magnetic resonance spectroscopy have become larger and more complicated. As a result, a complete and precise stable isotope labeling technique has been desired. A cell-free protein synthesis system is appropriate for this purpose. In the current study, we achieved precise and complete 15N and 2H labeling using an Escherichia coli cell extract-based cell-free protein synthesis system by controlling the metabolic reactions in the system with their chemical inhibitors. The addition of aminooxyacetate, d-malate, l-methionine sulfoximine, S-methyl-l-cysteine sulfoximine, 6-diazo-5-oxo-l-norleucine, and 5-diazo-4-oxo-l-norvaline was quite effective for precise amino acid-selective 15N labeling even for aspartic acid, asparagine, glutamic acid, and glutamine, which generally suffer from severe isotope scrambling and dilution when using the conventional cell-free system. For 2H labeling, the back-protonation of the Hα and Hβ positions, which commonly occurred in the conventional system, was dramatically suppressed by simply adding aminooxyacetate and d-malate to the cell-free system except for the Hα positions in methionine and cysteine.

Multiphysics modeling, which integrates the models studied in different disciplines so far, is an indispensable approach toward a comprehensive understanding of biological systems composed of diverse phenomena. However, the variety of the models is narrower than the actual diverse phenomena because of the difficulty in coupling independent models separately studied in different disciplines for the actual coupled phenomena. In this study, we develop a mathematical model coupling an enzymatic reaction and mineralization formation. As a test case, we selected an in vitro transcription system where a transcription reaction occurs along with the precipitation formation of magnesium pyrophosphate (Mg2PPi). To begin, we experimentally elucidated how the transcription reaction and the precipitation formation are coupled. In the analysis, we applied a Michaelis-Menten-type equation to the transcription reaction and a semiempirical equation describing the correlation between the induction period and the supersaturation ratio to the precipitation formation, respectively. Based on the experimental results, we then integrated these two models. These models were connected by supersaturation that increases as the transcription reaction proceeds and becomes the driving force of the precipitation. We believe that our modeling approach could significantly contribute to the development of newer multiphysics models in systems biology such as bone metabolic networks.