期刊
ELIFE
卷 8, 期 -, 页码 -出版社
eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.45403
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资金
- UNC Eshelman Institute for Innovation
- Science and Technology Commission of Shanghai Municipality [19XD1404700, 18431907100]
- Ontario Ministry of Economic Development and Innovation
- Novartis Pharma
- National Institute of General Medical Sciences [R35GM131858, R01GM120570, R01GM126154, R01GM121505, R01GM122749, R01GM096056]
- Merck
- Janssen Pharmaceuticals
- National Natural Science Foundation of China [81625022, 21820202008, 81430084, 91853205]
- National Science & Technology Major Project of China [2018ZX09711002]
- Mr. William H. Goodwin and Mrs. Alice Goodwin Commonwealth Foundation for Cancer Research
- Experimental Therapeutics Center of Memorial Sloan Kettering Cancer Center
- Tri-Institutional Therapeutics Discovery Institute
- National Cancer Institute [5P30 CA008748]
- Starr Cancer Consortium
- Memorial Sloan-Kettering Cancer Center Functional Genomics Initiative
- Sloan Kettering Institute
- Memorial Sloan-Kettering Cancer Center
- K. C. Wong Education Foundation
- Chinese Academy of Sciences [XDA12020353]
- Tri-Institutional PhD Program in Chemical Biology
- U.S. Department of Defense [W81XWH-17-1-0412]
- AbbVie
- Bayer Pharma AG
- Boehringer Ingelheim
- Genome Canada
- Innovative Medicines Initiative
- Pfizer
- Sao Paulo Research Foundation
- Takeda Pharmaceutical Company
- Wellcome Trust
- Canada Foundation for Innovation
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.
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