The H5N1-NS1 protein affects the host cell cycle and apoptosis through interaction with the host lncRNA PIK3CD-AS2.

Long noncoding RNAs (lncRNAs) are involved in the host antiviral response, but how host lncRNAs interact with viral proteins remains unclear. The NS1 protein of avian influenza viruses can affect the interferon-dependent expression of several host lncRNAs, but the exact mechanism is unknown. To further investigate the molecular mechanism and functions of NS1 proteins and host lncRNAs, we performed RNA-immunoprecipitation sequencing assays on A549 cells transfected with the H5N1-NS1 gene.

Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors.

Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking structures of complexes generated by AutoDock Vina, we integrate interaction features and ligand features, and employ support vector regression to develop a target-specific scoring function for hDHODH (TSSF-hDHODH). The Pearson correlation coefficient values of TSSF-hDHODH in the cross-validation and external validation are 0.

Construction of highly active and stable recombinant nattokinase by engineered bacteria and computational design.

Nattokinase (NK) is an enzyme that has been recognized as a new potential thrombolytic drug due to its strong thrombolytic activity. However, it is difficult to maintain the enzyme activity of NK during high temperature environment of industrial production. In this study, we constructed six NK mutants with potential for higher thermostability using a rational protein engineering strategy integrating free energy-based methods and molecular dynamics (MD) simulation.

CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning.

Virus‒host protein‒lncRNA interaction (VHPLI) predictions are critical for decoding the molecular mechanisms of viral pathogens and host immune processes. Although VHPLI interactions have been predicted in both plants and animals, they have not been extensively studied in viruses. For the first time, we propose a new deep learning-based approach that consists mainly of a convolutional neural network and bidirectional long and short-term memory network modules in combination with transfer learning named CBIL‒VHPLI to predict viral-host protein‒lncRNA interactions.

Effects of the Nonstructural Protein-Nucleolar and Coiled-Body Phosphoprotein 1 Protein Interaction on rRNA Synthesis Through Telomeric Repeat-Binding Factor 2 Regulation Under Nucleolar Stress.

To investigate the effects and underlying molecular mechanisms of the interaction between the non-structural protein 1 (NS1) and nucleolar and coiled-body phosphoprotein 1 (NOLC1) on rRNA synthesis through nucleolar telomeric repeat-binding factor 2 (TRF2) under nucleolar stress in avian influenza A virus infection. The analysis of TRF2 ties into the exploration of ribosomal protein L11 (RPL11) and mouse double minute 2 (MDM2) because TRF2 has been found to interact with NOLC1, and the RPL11-MDM2 pathway plays an important role in nucleolar regulation and cellular processes. Both human embryonic kidney 293T cells and human lung adenocarcinoma A549 cells were transfected with the plasmids pCAGGS-HA and pCAGGS-HA-NS1, respectively.

Study on the transport and internalisation mechanism of dietary supplement nattokinase in the small intestine using animal and Caco-2 cell monolayer models.

Maintaining proper blood flow is critical to promoting good health. Nattokinase is a serine protease from that has significant thrombolytic activity, but its mechanism as a dietary supplement to prevent thrombosis through intestinal absorption and transport is still unclear.The purpose of this study is to study the transport and internalisation mechanism of NK in the small intestine using animal models and Caco-2 cell monolayer models.

Construction of an oxidative stress-related lncRNAs signature to predict prognosis and the immune response in gastric cancer.

Oxidative stress, as a characteristic of cellular aerobic metabolism, plays a crucial regulatory role in the development and metastasis of gastric cancer (GC). Long noncoding RNAs (lncRNAs) are important regulators in GC development. However, research on the prognostic patterns of oxidative stress-related lncRNAs (OSRLs) and their functions in the immune microenvironment is currently insufficient.

The preparation of polyclonal antibody against chlordimeform and establishment of detection by indirect competitive ELISA.

Unlabelled: Chlordimeform is a chemical pesticide that is highly carcinogenic and toxic. The purpose of this study was to establish an enzyme-linked immunosorbent assay (ELISA) method for the detection of chlordimeform in aquaculture and fish farming.

Methods: Chlordimeform was coupled with bovine serum albumin (BSA) and ovalbumin (OVA) as carrier proteins.

Using Network Distance Analysis to Predict lncRNA-miRNA Interactions.

LncRNA-miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA-miRNA associations at large scale, and computational prediction methods are limited. In this study, we developed a network distance analysis model for lncRNA-miRNA association prediction (NDALMA).

Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods.

The ability of chemicals to enter the blood-brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been developed, they have insufficient accuracy (ACC) and sensitivity (SEN). To improve performance, ensemble models were built to predict the BBB permeability of compounds.

MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints.

An important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In this research, we used an advanced graph convolutional neural network (GCNN) architecture to identify the molecular representation and develop predictive models based on these representations.

Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints.

Reproductive toxicity endpoints are a significant safety concern in the assessment of the adverse effects of chemicals in drug discovery. Computational models that can accurately predict a chemical's toxic potential are increasingly pursued to replace traditional animal experiments. Thus, ensemble learning models were built to predict the reproductive toxicity of compounds.

Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation.

Background: Early diabetes screening can effectively reduce the burden of disease. However, natural population-based screening projects require a large number of resources. With the emergence and development of machine learning, researchers have started to pursue more flexible and efficient methods to screen or predict type 2 diabetes.

Interaction of avian influenza virus NS1 protein and nucleolar and coiled-body phosphoprotein 1.

Nonstructural protein 1 (NS1) is a non-structural protein of avian influenza virus. It can interact with a variety of proteins of the host cells, enhancing the expression of viral proteins and changing the growth and metabolism of the host cells, thereby enhancing the virus' pathogenicity and virulence. To investigate whether there are more host proteins that can interact with NS1 during viral infection, T7-phage display system was used to screen human lung cell cDNA library for proteins that could interact with NS1.