Structures of Mature and Urea-Treated Empty Bacteriophage T5: Insights into Siphophage Infection and DNA Ejection.

T5 is a siphophage that has been extensively studied by structural and biochemical methods. However, the complete in situ structures of T5 before and after DNA ejection remain unknown. In this study, we used cryo-electron microscopy (cryo-EM) to determine the structures of mature T5 (a laboratory-adapted, fiberless T5 mutant) and urea-treated empty T5 (lacking the tip complex) at near-atomic resolutions. Atomic models of the head, connector complex, tail tube, and tail tip were built for mature T5, and atomic models of the connector complex, comprising the portal protein pb7, adaptor protein p144, and tail terminator protein p142, were built for urea-treated empty T5. Our findings revealed that the aforementioned proteins did not undergo global conformational changes before and after DNA ejection, indicating that these structural features were conserved among most myophages and siphophages. The present study elucidates the underlying mechanisms of siphophage infection and DNA ejection.

Simcryocluster: a semantic similarity clustering method of cryo-EM images by adopting contrastive learning.

BACKGROUND: Cryo-electron microscopy (Cryo-EM) plays an increasingly important role in the determination of the three-dimensional (3D) structure of macromolecules. In order to achieve 3D reconstruction results close to atomic resolution, 2D single-particle image classification is not only conducive to single-particle selection, but also a key step that affects 3D reconstruction. The main task is to cluster and align 2D single-grain images into non-heterogeneous groups to obtain sharper single-grain images by averaging calculations. The main difficulties are that the cryo-EM single-particle image has a low signal-to-noise ratio (SNR), cannot manually label the data, and the projection direction is random and the distribution is unknown. Therefore, in the low SNR scenario, how to obtain the characteristic information of the effective particles, improve the clustering accuracy, and thus improve the reconstruction accuracy, is a key problem in the 2D image analysis of single particles of cryo-EM. RESULTS: Aiming at the above problems, we propose a learnable deep clustering method and a fast alignment weighted averaging method based on frequency domain space to effectively improve the class averaging results and improve the reconstruction accuracy. In particular, it is very prominent in the feature extraction and dimensionality reduction module. Compared with the classification method based on Bayesian and great likelihood, a large amount of single particle data is required to estimate the relative angle orientation of macromolecular single particles in the 3D structure, and we propose that the clustering method shows good results. CONCLUSIONS: SimcryoCluster can use the contrastive learning method to perform well in the unlabeled high-noise cryo-EM single particle image classification task, making it an important tool for cryo-EM protein structure determination.

Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm.

Inferring gene regulatory networks (GRNs) from microarray data can help us understand the mechanisms of life and eventually develop effective therapies. Currently, many computational methods have been used in inferring GRNs. However, owing to high-dimensional data and small samples, these methods often tend to introduce redundant regulatory relationships. Therefore, a novel network inference method based on the improved Markov blanket discovery algorithm, IMBDANET, is proposed to infer GRNs. Specifically, for each target gene, data processing inequality was applied to the Markov blanket discovery algorithm for the accurate differentiation of direct regulatory genes from indirect regulatory genes. Finally, direct regulatory genes were used in constructing GRNs, and the network structure was optimized according to the importance degree score. Experimental results on six public network datasets show that the proposed method can be effectively used to infer GRNs.