CRISPR-Cas9 technology has been widely used for genome engineering. Its RNA-guided endonuclease Cas9 binds specifically to target DNA and then cleaves the two DNA strands with HNH and RuvC nuclease domains. However, structural information regarding the DNA cleavage-activating state of two nuclease domains remains sparse. Here, we report a 5.2 Å cryo-EM structure of Cas9 in complex with sgRNA and target DNA. This structure reveals a conformational state of Cas9 in which the HNH domain is closest to the DNA cleavage site. Compared with two known HNH states, our structure shows that the HNH active site moves toward the cleavage site by about 25 and 13 Å, respectively. In combination with EM-based molecular dynamics simulations, we show that residues of the nuclease domains in our structure could form cleavage-compatible conformations with the target DNA. Together, these results strongly suggest that our cryo-EM structure resembles a DNA cleavage-activating architecture of Cas9.

Phosphorylation of protein kinases (PKs) plays a central role in the signal transduction of cells by transferring a phosphate group from adenosine triphosphate (ATP) to the side chains of particular serine, threonine or tyrosine residues of substrates. Previous studies have shown that Mg²⁺ ions are crucial for the formation of transition state of phosphoryl transfer reaction. However, the roles of Mg²⁺ ions in other steps of phosphorylation remain poorly understood. Here, we performed molecular dynamics simulations to study the interactions of ATP with PKs in the presence and the absence of Mg²⁺ ions in the active sites, respectively. Affinity analysis showed that the binding-free energies of ATP to the PK active sites in the presence of Mg²⁺ ions are lower than those in the absence of Mg²⁺ ions, suggesting that Mg²⁺ ions in the active sites could enhance the binding affinities of ATP to the PKs. This enhancement is mainly attributed to the electrostatic interactions induced by Mg²⁺ ions. Thus, this study shows that Mg²⁺ ions also play a very important role in the binding of ATP to PKs, and thereby gains a better understanding of how Mg²⁺ ions function in protein phosphorylation.

Motivation: Single-particle cryo-electron microscopy (cryo-EM) has become a powerful technique for determining 3D structures of biological macromolecules at near-atomic resolution. However, this approach requires picking huge numbers of macromolecular particle images from thousands of low-contrast, high-noisy electron micrographs. Although machine-learning methods were developed to get rid of this bottleneck, it still lacks universal methods that could automatically picking the noisy cryo-EM particles of various macromolecules. Results: Here, we present a deep-learning segmentation model that employs fully convolutional networks trained with synthetic data of known 3D structures, called PARSED (PARticle SEgmentation Detector). Without using any experimental information, PARSED could automatically segment the cryo-EM particles in a whole micrograph at a time, enabling faster particle picking than previous template/feature-matching and particle-classification methods. Applications to six large public cryo-EM datasets clearly validated its universal ability to pick macromolecular particles of various sizes. Thus, our deep-learning method could break the particle-picking bottleneck in the single-particle analysis, and thereby accelerates the high-resolution structure determination by cryo-EM. Availability and implementation: The PARSED package and user manual for noncommercial use are available as Supplementary Material (in the compressed file: parsed_v1.zip). Supplementary information: Supplementary data are available at Bioinformatics online.