Project description:Sirtuin 6 (SIRT6) is a multifaceted protein deacetylase/deacylase and a major target for small-molecule modulators of longevity and cancer. In the context of chromatin, SIRT6 removes acetyl groups from histone H3 in nucleosomes, but the molecular basis for its nucleosomal substrate preference is unknown. Our cryo-electron microscopy structure of human SIRT6 in complex with the nucleosome shows that the catalytic domain of SIRT6 pries DNA from the nucleosomal entry-exit site and exposes the histone H3 N-terminal helix, while the SIRT6 zinc-binding domain binds to the histone acidic patch using an arginine anchor. In addition, SIRT6 forms an inhibitory interaction with the C-terminal tail of histone H2A. The structure provides insights into how SIRT6 can deacetylate both H3 K9 and H3 K56.TeaserThe structure of the SIRT6 deacetylase/nucleosome complex suggests how the enzyme acts on both histone H3 K9 and K56 residues.
Project description:Sirtuin 6 (SIRT6) is a multifaceted protein deacetylase/deacylase and a major target for small-molecule modulators of longevity and cancer. In the context of chromatin, SIRT6 removes acetyl groups from histone H3 in nucleosomes, but the molecular basis for its nucleosomal substrate preference is unknown. Our cryo-electron microscopy structure of human SIRT6 in complex with the nucleosome shows that the catalytic domain of SIRT6 pries DNA from the nucleosomal entry-exit site and exposes the histone H3 N-terminal helix, while the SIRT6 zinc-binding domain binds to the histone acidic patch using an arginine anchor. In addition, SIRT6 forms an inhibitory interaction with the C-terminal tail of histone H2A. The structure provides insights into how SIRT6 can deacetylate both H3 K9 and H3 K56.
Project description:The chromatin-remodelling complex SWI/SNF is highly conserved and has critical roles in various cellular processes, including transcription and DNA-damage repair1,2. It hydrolyses ATP to remodel chromatin structure by sliding and evicting histone octamers3-8, creating DNA regions that become accessible to other essential factors. However, our mechanistic understanding of the remodelling activity is hindered by the lack of a high-resolution structure of complexes from this family. Here we report the cryo-electron microscopy structure of Saccharomyces cerevisiae SWI/SNF bound to a nucleosome, at near-atomic resolution. In the structure, the actin-related protein (Arp) module is sandwiched between the ATPase and the rest of the complex, with the Snf2 helicase-SANT associated (HSA) domain connecting all modules. The body contains an assembly scaffold composed of conserved subunits Snf12 (also known as SMARCD or BAF60), Snf5 (also known as SMARCB1, BAF47 or INI1) and an asymmetric dimer of Swi3 (also known as SMARCC, BAF155 or BAF170). Another conserved subunit, Swi1 (also known as ARID1 or BAF250), resides in the core of SWI/SNF, acting as a molecular hub. We also observed interactions between Snf5 and the histones at the acidic patch, which could serve as an anchor during active DNA translocation. Our structure enables us to map and rationalize a subset of cancer-related mutations in the human SWI/SNF complex and to propose a model for how SWI/SNF recognizes and remodels the +1 nucleosome to generate nucleosome-depleted regions during gene activation9.
Project description:Centromere protein A (CENP-A) nucleosomes containing the centromere-specific histone H3 variant CENP-A represent an epigenetic mark that specifies centromere position. The Mis18 complex is a licensing factor for new CENP-A deposition via the CENP-A chaperone, Holliday junction recognition protein (HJURP), on the centromere chromatin. Chicken KINETOCHORE NULL2 (KNL2) (ggKNL2), a Mis18 complex component, has a CENP-C-like motif, and our previous study suggested that ggKNL2 directly binds to the CENP-A nucleosome to recruit HJURP/CENP-A to the centromere. However, the molecular basis for CENP-A nucleosome recognition by ggKNL2 has remained unclear. Here, we present the cryo-EM structure of the chicken CENP-A nucleosome in complex with a ggKNL2 fragment containing the CENP-C-like motif. Chicken KNL2 distinguishes between CENP-A and histone H3 in the nucleosome using the CENP-C-like motif and its downstream region. Both the C-terminal tail and the RG-loop of CENP-A are simultaneously recognized as CENP-A characteristics. The CENP-A nucleosome-ggKNL2 interaction is thus essential for KNL2 functions. Furthermore, our structural, biochemical, and cell biology data indicate that ggKNL2 changes its binding partner at the centromere during chicken cell cycle progression.
Project description:Mixed lineage leukemia (MLL) family histone methyltransferases are enzymes that deposit histone H3 Lys4 (K4) mono-/di-/tri-methylation and regulate gene expression in mammals. Despite extensive structural and biochemical studies, the molecular mechanisms whereby the MLL complexes recognize histone H3K4 within nucleosome core particles (NCPs) remain unclear. Here we report the single-particle cryo-electron microscopy (cryo-EM) structure of the NCP-bound human MLL1 core complex. We show that the MLL1 core complex anchors to the NCP via the conserved RbBP5 and ASH2L, which interact extensively with nucleosomal DNA and the surface close to the N-terminal tail of histone H4. Concurrent interactions of RbBP5 and ASH2L with the NCP uniquely align the catalytic MLL1SET domain at the nucleosome dyad, thereby facilitating symmetrical access to both H3K4 substrates within the NCP. Our study sheds light on how the MLL1 complex engages chromatin and how chromatin binding promotes MLL1 tri-methylation activity.
Project description:Automatic single particle picking is a critical step in the data processing pipeline of cryo-electron microscopy structure reconstruction. In recent years, several deep learning-based algorithms have been developed, demonstrating their potential to solve this challenge. However, current methods highly depend on manually labeled training data, which is labor-intensive and prone to biases especially for high-noise and low-contrast micrographs, resulting in suboptimal precision and recall. To address these problems, we propose UPicker, a semi-supervised transformer-based particle-picking method with a two-stage training process: unsupervised pretraining and supervised fine-tuning. During the unsupervised pretraining, an Adaptive Laplacian of Gaussian region proposal generator is proposed to obtain pseudo-labels from unlabeled data for initial feature learning. For the supervised fine-tuning, UPicker only needs a small amount of labeled data to achieve high accuracy in particle picking. To further enhance model performance, UPicker employs a contrastive denoising training strategy to reduce redundant detections and accelerate convergence, along with a hybrid data augmentation strategy to deal with limited labeled data. Comprehensive experiments on both simulated and experimental datasets demonstrate that UPicker outperforms state-of-the-art particle-picking methods in terms of accuracy and robustness while requiring fewer labeled data than other transformer-based models. Furthermore, ablation studies demonstrate the effectiveness and necessity of each component of UPicker. The source code and data are available at https://github.com/JachyLikeCoding/UPicker.
Project description:Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise (SNR) ratios. To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labelled cryo-EM protein particle dataset - CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score and is poised to facilitate the automation of the cryo-EM protein particle picking.
Project description:The CENP-A nucleosome is a key structure for kinetochore assembly. Once the CENP-A nucleosome is established in the centromere, additional proteins recognize the CENP-A nucleosome to form a kinetochore. CENP-C and CENP-N are CENP-A binding proteins. We previously demonstrated that vertebrate CENP-C binding to the CENP-A nucleosome is regulated by CDK1-mediated CENP-C phosphorylation. However, it is still unknown how the phosphorylation of CENP-C regulates its binding to CENP-A. It is also not completely understood how and whether CENP-C and CENP-N act together on the CENP-A nucleosome. Here, using cryo-electron microscopy (cryo-EM) in combination with biochemical approaches, we reveal a stable CENP-A nucleosome-binding mode of CENP-C through unique regions. The chicken CENP-C structure bound to the CENP-A nucleosome is stabilized by an intramolecular link through the phosphorylated CENP-C residue. The stable CENP-A-CENP-C complex excludes CENP-N from the CENP-A nucleosome. These findings provide mechanistic insights into the dynamic kinetochore assembly regulated by CDK1-mediated CENP-C phosphorylation.