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The N-terminal modification of nascent proteins, such as acetylation and myristoylation, is one of the most abundant post-translational modifications. To analyze the function of the modification, it is important to compare the modified and unmodified proteins under defined conditions. However, it is technically difficult to prepare unmodified proteins because cell-based systems contain endogenous modification systems. In this study, we developed a cell-free method to conduct N-terminal acetylation and myristoylation of nascent proteins in vitro using a reconstituted cell-free protein synthesis system (PURE system). Proteins synthesized using the PURE system were successfully acetylated or myristoylated in a single-cell-free mixture in the presence of modifying enzymes. Furthermore, we performed protein myristoylation in giant vesicles, which resulted in their partial localization to the membrane. Our PURE-system-based strategy is useful for the controlled synthesis of post-translationally modified proteins.

Nuclear import receptors (NIRs) not only transport RNA-binding proteins (RBPs) but also modify phase transitions of RBPs by recognizing nuclear localization signals (NLSs). Toxic arginine-rich poly-dipeptides from C9orf72 interact with NIRs and cause nucleocytoplasmic transport deficit. However, the molecular basis for the toxicity of arginine-rich poly-dipeptides toward NIRs function as phase modifiers of RBPs remains unidentified. Here we show that arginine-rich poly-dipeptides impede the ability of NIRs to modify phase transitions of RBPs. Isothermal titration calorimetry and size-exclusion chromatography revealed that proline:arginine (PR) poly-dipeptides tightly bind karyopherin-β2 (Kapβ2) at 1:1 ratio. The nuclear magnetic resonances of Kapβ2 perturbed by PR poly-dipeptides partially overlapped with those perturbed by the designed NLS peptide, suggesting that PR poly-dipeptides target the NLS binding site of Kapβ2. The findings offer mechanistic insights into how phase transitions of RBPs are disabled in C9orf72-related neurodegeneration., 博士（医学）・甲第826号・令和4年3月15日, © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/.

For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem., {"references":["Abney, S. (2002). Bootstrapping. The 40th Annual Meeting of the\nAssociation for Computational Linguistics.","Allison, L. (2003). Types and Classes of Machine Learning and\nData Mining. Twenty-Six Australasian Computer Science Conference\n(ACSC2003), pp.207-215, Australia.","Bartlett, P. L., Collins, M., McAllester, D., and Taskar, B. (2004). Large\nmargin methods for structured classification: Exponentiated Gradient\nalgorithms and PAC-Bayesian generalization bounds. NIPS Conference.","Cristianini, N., Shawe-Taylor, J. (2000). Introduction to Support Vector\nMachines. Cambridge University Press.","Jaakkola, T. (2000) Tutorial on Variational Approximation Method. In\nAdvanced Mean Field Methods: Theory and Practice, MIT Press.","Lafferty, J., McCallum, A., Pereira, F. (2001). Conditional Random\nFields: Probabilistic Models for Segmenting and Labeling Sequence Data.\nInternational Conference on Machine Learning (ICML).","Langford, J., Beygelzimer, A. (2002). Sensitive Error Correcting Output\nCodes.","Mitchell, T. (1997). Machine Learning. McGraw Hills.","Okita, T., Manderick, B. (2003). Support Vector Learning in Distributed\nEnvironments (poster), Conference On Learning Theory and Kernel\nMachines, Washington.\n[10] Rabiner, L. R. (1989) A Tutorial on Hidden Markov Models and Selected\nApplications in Speech Recognition. Proceedings of the IEEE, VOL. 77,\nNo. 2, February 1989.\n[11] Scholkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J. (2000).\nSupport Vector Method for Novelty Detection. In Neural Information\nProcessing Systems.\n[12] Shawe-Taylor, J., Cristianini, N. (2004). Kernel Methods for Pattern\nAnalysis. Cambridge University Press."]}