Título:	The dynamic conformational landscape of the protein methyltransferase SETD8
Autor(es):	Shi Chen Wiewiora, Rafal P. Fanwang Meng Babault, Nicolas Anqi Ma Wenyu Yu Kun Qian Hao Hu Hua Zou Junyi Wang Shijie Fan Blum, Gil Pittella-Silva, Fabio Beauchamp, Kyle A. Tempel, Wolfram Hualiang Jiang Kaixian Chen Skene, Robert J. Yujun George Zheng Brown, Peter J. Jian Jin Cheng Luo Chodera, John D. Minkui Luo
Assunto:	Proteínas Câncer
Data de publicação:	13-Mai-2019
Editora:	eLife Sciences Publications Ltd.
Referência:	SHI CHEN et al. The dynamic conformational landscape of the protein methyltransferase SETD8. eLife, v.19, n.8, e45403, 2019. DOI: https://doi.org/10.7554/eLife.45403. Disponível em: https://elifesciences.org/articles/45403. Acesso em: 05 set. 2022.
Abstract:	Elucidating the conformational heterogeneity of proteins is essential for understanding protein function and developing exogenous ligands. With the rapid development of experimental and computational methods, it is of great interest to integrate these approaches to illuminate the conformational landscapes of target proteins. SETD8 is a protein lysine methyltransferase (PKMT), which functions in vivo via the methylation of histone and nonhistone targets. Utilizing covalent inhibitors and depleting native ligands to trap hidden conformational states, we obtained diverse X-ray structures of SETD8. These structures were used to seed distributed atomistic molecular dynamics simulations that generated a total of six milliseconds of trajectory data. Markov state models, built via an automated machine learning approach and corroborated experimentally, reveal how slow conformational motions and conformational states are relevant to catalysis. These findings provide molecular insight on enzymatic catalysis and allosteric mechanisms of a PKMT via its detailed conformational landscape.
Licença:	Copyright Chen et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
DOI:	https://doi.org/10.7554/eLife.45403
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