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Elife. 2019 May 13;8. pii: e45403. doi: 10.7554/eLife.45403. [Epub ahead of print]

The dynamic conformational landscape of the protein methyltransferase SETD8.

Author information

1
Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, United States.
2
Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Canada.
3
Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.
4
Structural Genomics Consortium, University of Toronto, Toronto, Canada.
5
Department of Pharmaceutical and Biomedical Sciences, College of Pharmacy, The University of Georgia, Athens, United States.
6
Takeda, San Diego, United States.
7
Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States.
8
Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Shanghai, China.
9
Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States.

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; human

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 The other authors declare that no competing interests exist. JC John D Chodera, was a member of the Scientific Advisory Board for Schrödinger, LLC during part of this study.Is a current member of the Scientific Advisory Board of OpenEye Scientific SoftwareThe 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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