Extending Protein Domain Boundary Predictors to Detect Discontinuous Domains

Aflatoxin B1 exposure triggers hepatic lipotoxicity via p53 and perilipin 2 interaction-mediated mitochondria-lipid droplet contacts: An in vitro and in vivo assessment

Aflatoxin B1 (AFB1) is one of the most toxic mycotoxins widely found in food contaminants, and its target organ is the liver. It poses a major food security and public health threat worldwide. However, the lipotoxicity mechanism of AFB1 exposure-induced liver injury remains unclear and requires further elucidation. Herein, we investigated the potential hepatic lipotoxicity of AFB1 exposure using in vitro and in vivo models to assess the public health hazards of high dietary AFB1 exposure. We demonstrated that low-dose of AFB1 (1.25 μM for 48 h, about one-fifth of the IC50 in HepG2 and HepaRG cells, IC50 are 5.995 μM and 5.266 μM, respectively) exposure significantly induced hepatic lipotoxicity, including abnormal lipid droplets (LDs) growth, mitochondria-LDs contacts increase, lipophagy disruption, and lipid accumulation. Mechanistically, we showed that AFB1 exposure promoted the mitochondrial p53 (mito-p53) and LDs-associated protein perilipin 2 (PLIN2) interaction-mediated mitochondria-LDs contacts, resulting in lipid accumulation in hepatocytes. Mito-p53-targeted inhibition, knockdown of PLIN2, and rapamycin application efficiently promoted the lysosome-dependent lipophagy and alleviated the hepatic lipotoxicity and liver injury induced by AFB1 exposure. Overall, our study found that mito-p53 and PLIN2 interaction mediates three organelles-mitochondria, LDs, and lysosomal networks to regulate lipid homeostasis in AFB1 exposure-induced hepatotoxicity, revealing how this unique trio of organelles works together and provides a novel insight into the targeted intervention in inter-organelle lipid sensing and trafficking for alleviating hazardous materials-induced hepatic lipotoxicity.

Protein domain identification methods and online resources

Protein domains are the basic units of proteins that can fold, function, and evolve independently. Knowledge of protein domains is critical for protein classification, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Thus, over the past two decades, a number of protein domain identification approaches have been developed, and a variety of protein domain databases have also been constructed. This review divides protein domain prediction methods into two categories, namely sequence-based and structure-based. These methods are introduced in detail, and their advantages and limitations are compared. Furthermore, this review also provides a comprehensive overview of popular online protein domain sequence and structure databases. Finally, we discuss potential improvements of these prediction methods.

Multimodularity of a GH10 Xylanase Found in the Termite Gut Metagenome

Xylan is the major hemicellulosic polysaccharide in cereals and contributes to the recalcitrance of the plant cell wall toward degradation. Bacteroidetes, one of the main phyla in rumen and human gut microbiota, have been shown to encode polysaccharide utilization loci dedicated to the degradation of xylan. Here, we present the biochemical characterization of a xylanase encoded by a bacteroidetes strain isolated from the termite gut metagenome.

The functional screening of a Pseudacanthotermes militaris termite gut metagenomic library revealed an array of xylan-degrading enzymes, including P. militaris 25 (Pm25), a multimodular glycoside hydrolase family 10 (GH10). Sequence analysis showed details of the unusual domain organization of this enzyme. It consists of one catalytic domain, which is intercalated by two carbohydrate binding modules (CBMs) from family 4. The genes upstream of the genes encoding Pm25 are susC-susD-unk, suggesting Pm25 is a Xyn10C-like enzyme belonging to a polysaccharide utilization locus. The majority of Xyn10C-like enzymes shared the same interrupted domain architecture and were vastly distributed in different xylan utilization loci found in gut Bacteroidetes, indicating the importance of this enzyme in glycan acquisition for gut microbiota. To understand its unusual multimodularity and the possible role of the CBMs, a detailed characterization of the full-length Pm25 and truncated variants was performed. Results revealed that the GH10 catalytic module is specific toward the hydrolysis of xylan. Ligand binding results indicate that the GH10 module and the CBMs act independently, whereas the tandem CBM4s act synergistically with each other and improve enzymatic activity when assayed on insoluble polysaccharides. In addition, we show that the UNK protein upstream of Pm25 is able to bind arabinoxylan. Altogether, these findings contribute to a better understanding of the potential role of Xyn10C-like proteins in xylan utilization systems of gut bacteria.

IMPORTANCE Xylan is the major hemicellulosic polysaccharide in cereals and contributes to the recalcitrance of the plant cell wall toward degradation. Members of the Bacteroidetes, one of the main phyla in rumen and human gut microbiota, have been shown to encode polysaccharide utilization loci dedicated to the degradation of xylan. Here, we present the biochemical characterization of a xylanase encoded by a Bacteroidetes strain isolated from the termite gut metagenome. This xylanase is a multimodular enzyme, the sequence of which is interrupted by the insertion of two CBMs from family 4. Our results show that this enzyme resembles homologues that were shown to be important for xylan degradation in rumen or human diet and show that the CBM insertion in the middle of the sequence seems to be a common feature in xylan utilization systems. This study shed light on our understanding of xylan degradation and plant cell wall deconstruction, which can be applied to several applications in food, feed, and bioeconomy.

DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network

MOTIVATION

Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-range interactions among residues, which have been proven to improve the prediction performance. However, how to simultaneously model the local and global interactions to further improve domain boundary prediction is still a challenging problem.

RESULTS

This article employs a hybrid deep learning method that combines convolutional neural network and gate recurrent units' models for domain boundary prediction. It not only captures the local and non-local interactions, but also fuses these features for prediction. Additionally, we adopt balanced Random Forest for classification to deal with high imbalance of samples and high dimensions of deep features. Experimental results show that our proposed approach (DNN-Dom) outperforms existing machine-learning-based methods for boundary prediction. We expect that DNN-Dom can be useful for assisting protein structure and function prediction.

AVAILABILITY AND IMPLEMENTATION

The method is available as DNN-Dom Server at http://isyslab.info/DNN-Dom/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ThreaDomEx: a unified platform for predicting continuous and discontinuous protein domains by multiple-threading and segment assembly

We develop a hierarchical pipeline, ThreaDomEx, for both continuous domain (CD) and discontinuous domain (DCD) structure predictions. Starting from a query sequence, ThreaDomEx first threads it through the PDB to identify multiple structure templates, where a profile of domain conservation score (DC-score) is derived for domain-segment assignment. To further detect DCDs that consist of separated segments along the sequence, a boundary-clustering algorithm is used to refine the DCD-linker locations. In case that the templates do not contain DCDs, a domain-segment assembly process, guided by symmetry comparison, is applied for further DCD detections. ThreaDomEx was tested a set of 1111 proteins and achieved a normalized domain overlap score of 89.3% compared to experimental data, which is significantly higher than other state-of-the-art methods. It also recalls 26.7% of DCDs with 72.7% precision on the proteins for which threading failed to detect any DCDs. The server provides facilities for users to interactively refine the domain models by adjusting DC-score threshold, deleting and adding domain linkers, and assembling domain segments, which are particularly helpful for the hard targets for which current methods have a low accuracy while human-expert knowledge and experimental insights can be used for refining models. ThreaDomEX server is available at http://zhanglab.ccmb.med.umich.edu/ThreaDomEx.