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Elife. 2019 May 13;8. pii: e45403. doi: 10.7554/eLife.45403.

The dynamic conformational landscape of the protein methyltransferase SETD8.

Chen S#1,2, Wiewiora RP#1,3, Meng F4, Babault N5,6,7,8, Ma A5,6,7,8, Yu W9, Qian K10, Hu H10, Zou H11, Wang J2, Fan S4,12, Blum G2, Pittella-Silva F2, Beauchamp KA3, Tempel W9, Jiang H4,12, Chen K4,12, Skene RJ11, Zheng YG10, Brown PJ9, Jin J5,6,7,8, Luo C4,12, Chodera JD3, Luo M2,13.

Author information

1
Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, United States.
2
Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States.
3
Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States.
4
Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
5
Mount Sinai Center for Therapeutics Discovery, Icahn School of Medicine at Mount Sinai, New York, United States.
6
Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.
7
Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.
8
Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, United States.
9
Structural Genomics Consortium, University of Toronto, Toronto, Canada.
10
Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, United States.
11
Takeda California, Science Center Drive, San Diego, United States.
12
University of Chinese Academy of Sciences, Beijing, China.
13
Program of Pharmacology, Weill Cornell Medical College of Cornell University, New York, United States.
#
Contributed equally

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.

KEYWORDS:

biochemistry; chemical biology; computational chemistry; enzymology; epigenetics; human; posttranslational modification

PMID:
31081496
DOI:
10.7554/eLife.45403
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Conflict of interest statement

SC, RW, FM, NB, AM, WY, KQ, HH, HZ, JW, SF, GB, FP, KB, WT, HJ, KC, RS, YZ, PB, JJ, CL, ML No competing interests declared, JC declares that no competing interests exist. In the interests of transparency they wish to make the following disclosures: John D Chodera was a member of the Scientific Advisory Board for Schrodinger, LLC during part of this study; is a member of the Scientific Advisory Board of OpenEye Scientific Software The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, XtalPi, the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, and the Sloan Kettering Institute. A complete funding history for the Chodera lab can be found at http://choderalab.org/funding.

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