Structural basis for histone variant H3tK27me3 recognition by PHF1 and PHF19
Abstract
The PRC2 (Polycomb repressive complex 2) complex is a multi-component histone H3K27 methyltransferase, best known for silencing Hox genes during embryonic development. The Polycomb-like proteins PHF1, MTF2 and PHF19 are critical components of PRC2 by stimulating its catalytic activity in embryonic stem (ES) cells. The Tudor domains of PHF1/19 have been previously shown to be readers of H3K36me3 in vitro. However, some other studies suggest that PHF1 and PHF19 co-localize with the H3K27me3 mark, but not H3K36me3 in cells. Here, we provide further evidence that PHF1 co-localizes with H3t in testis, and its Tudor domain preferentially binds to H3tK27me3 over canonical H3K27me3 in vitro. Our complex structures of the Tudor domains of PHF1 and PHF19 with H3tK27me3 shed light on the molecular basis for preferential recognition of H3tK27me3 by PHF1 and PHF19 over canonical H3K27me3, implicating that H3tK27me3 might be a physiological ligand of PHF1/19.
Data availability
Diffraction data have been deposited in PDB under the accession codes 6WAT, 6WAU, 6WAV
-
Crystal structure of PHF1 in complex with H3K36me3 substitutionProtein Data Bank, 6WAV.
Article and author information
Author details
Funding
National Natural Science Foundation of China (31900865)
- Cheng Dong
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Dong et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,792
- views
-
- 259
- downloads
-
- 17
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Structural Biology and Molecular Biophysics
In eukaryotes, RNAs transcribed by RNA Pol II are modified at the 5′ end with a 7-methylguanosine (m7G) cap, which is recognized by the nuclear cap binding complex (CBC). The CBC plays multiple important roles in mRNA metabolism, including transcription, splicing, polyadenylation, and export. It promotes mRNA export through direct interaction with a key mRNA export factor, ALYREF, which in turn links the TRanscription and EXport (TREX) complex to the 5′ end of mRNA. However, the molecular mechanism for CBC-mediated recruitment of the mRNA export machinery is not well understood. Here, we present the first structure of the CBC in complex with an mRNA export factor, ALYREF. The cryo-EM structure of CBC-ALYREF reveals that the RRM domain of ALYREF makes direct contact with both the NCBP1 and NCBP2 subunits of the CBC. Comparing CBC-ALYREF with other cellular complexes containing CBC and/or ALYREF components provides insights into the coordinated events during mRNA transcription, splicing, and export.
-
- Structural Biology and Molecular Biophysics
Under physiological conditions, proteins continuously undergo structural fluctuations on different timescales. Some conformations are only sparsely populated, but still play a key role in protein function. Thus, meaningful structure–function frameworks must include structural ensembles rather than only the most populated protein conformations. To detail protein plasticity, modern structural biology combines complementary experimental and computational approaches. In this review, we survey available computational approaches that integrate sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling techniques to derive all-atom structural models of rare protein conformations. We also propose strategies to increase the reliability and improve efficiency using deep learning approaches, thus advancing the field of integrative structural biology.