RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders.

RNA-protein interactions (RPIs) play an important role in several fundamental cellular physiological processes, including cell motility, chromosome replication, transcription and translation, and signaling. Predicting RPI can guide the exploration of cellular biological functions, intervening in diseases, and designing drugs. Given this, this study proposes the RPI-gated graph convolutional network (RPI-GGCN) method for predicting RPI based on the gated graph convolutional neural network (GGCN) and co-regularized variational autoencoder (Co-VAE).

ML-FGAT: Identification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks.

The prediction of multi-label protein subcellular localization (SCL) is a pivotal area in bioinformatics research. Recent advancements in protein structure research have facilitated the application of graph neural networks. This paper introduces a novel approach termed ML-FGAT.

DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape.

Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the models' intrinsic mechanisms and prediction results are difficult to explain. There is still room for improvement in prediction performance.

Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction.

Interactions between DNA and transcription factors (TFs) play an essential role in understanding transcriptional regulation mechanisms and gene expression. Due to the large accumulation of training data and low expense, deep learning methods have shown huge potential in determining the specificity of TFs-DNA interactions. Convolutional network-based and self-attention network-based methods have been proposed for transcription factor binding sites (TFBSs) prediction.

One-Pot Synthesis of Phenylglyoxylic Acid from Racemic Mandelic Acids via Cascade Biocatalysis.

Phenylglyoxylic acid (PGA) are key building blocks and widely used to synthesize pharmaceutical intermediates or food additives. However, the existing synthetic methods for PGA generally involve toxic cyanide and complex processes. To explore an alternative method for PGA biosynthesis, we envisaged cascade biocatalysis for the one-pot synthesis of PGA from racemic mandelic acid.

Biosynthesis of Phenylglyoxylic Acid by LhDMDH, a Novel d-Mandelate Dehydrogenase with High Catalytic Activity.

d-Mandelate dehydrogenase (DMDH) has the potential to convert d-mandelic acid to phenylglyoxylic acid (PGA), which is a key building block in the field of chemical synthesis and is widely used to synthesize pharmaceutical intermediates or food additives. A novel NAD-dependent d-mandelate dehydrogenase was cloned from Lactobacillus harbinensi (LhDMDH) by genome mining and expressed in Escherichia coli BL21. After being purified to homogeneity, the oxidation activity of LhDMDH toward d-mandelic acid was approximately 1200 U·mg, which was close to four times the activity of the probe.

Differences in the population of genetics of Mycobacterium tuberculosis between urban migrants and local residents in Beijing, China.

Background: Currently, migration has become one of the risk factors of high burden of tuberculosis in China. This study was to explore the influence of mass migration on the dynamics of Mycobacterium (M.) tuberculosis in Beijing, the capital and an urban area of China.

[Origin, phylogeny, and spread of Mycobacterium tuberculosis Beijing lineage in the five provinces of northern China].

Objective: Using methodology of molecular genetics to explore the origin, phylogen, and gene flow of Mycobacterium tuberculosis (MTB) Beijing lineage in the five provinces from northern China, including Heilongjiang, Jilin, Liaoning, Neimenggu and Ningxia.

Methods: 234 MTB Beijing lineage strains were genotyped by 24 Variable Number Tandem Repeat (VNTR), and the h (the allelic diversity) value of each VNTR locus was calculated. On individual level of phylogeny, it was constructed Neighbor-Joining (N-J) tree and minimum spanning tree (MST).