TransGEM: a molecule generation model based on Transformer with gene expression data.

MOTIVATION: It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. RESULTS: We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/hzauzqy/TransGEM.

Metabolites of pathogenic microorganisms database (MPMdb) and its seed metabolite applications.

Seed metabolites are the combination of essential compounds required by an organism across various potential environmental conditions. The seed metabolites screening framework based on the network topology approach can capture important biological information of species. This study aims to identify comprehensively the relationship between seed metabolites and pathogenic bacteria. A large-scale data set was compiled, describing the seed metabolite sets and metabolite sets of 124,192 pathogenic strains from 34 genera, by constructing genome-scale metabolic models. The enrichment analysis method was used to screen the specific seed metabolites of each species/genus of pathogenic bacteria. The metabolites of pathogenic microorganisms database (MPMdb) (http://qyzhanglab.hzau.edu.cn/MPMdb/) was established for browsing, searching, predicting, or downloading metabolites and seed metabolites of pathogenic microorganisms. Based on the MPMdb, taxonomic and phylogenetic analyses of pathogenic bacteria were performed according to the function of seed metabolites and metabolites. The results showed that the seed metabolites could be used as a feature for microorganism chemotaxonomy, and they could mirror the phylogeny of pathogenic bacteria. In addition, our screened specific seed metabolites of pathogenic bacteria can be used not only for further tapping the nutritional resources and identifying auxotrophies of pathogenic bacteria but also for designing targeted bactericidal compounds by combining with existing antimicrobial agents.IMPORTANCEMetabolites serve as key communication links between pathogenic microorganisms and hosts, with seed metabolites being crucial for microbial growth, reproduction, external communication, and host infection. However, the large-scale screening of metabolites and the identification of seed metabolites have always been the main technical bottleneck due to the low throughput and costly analysis. Genome-scale metabolic models have become a recognized research paradigm to investigate the metabolic characteristics of species. The developed metabolites of pathogenic microorganisms database in this study is committed to systematically predicting and identifying the metabolites and seed metabolites of pathogenic microorganisms, which could provide a powerful resource platform for pathogenic bacteria research.

Transporter proteins knowledge graph construction and its application in drug development.

Transporters are the main determinant for pharmacokinetics characteristics of drugs, such as absorption, distribution, and excretion of drugs in humans. However, it is difficult to perform drug transporter validation and structure analysis of membrane transporter proteins by experimental methods. Many studies have demonstrated that knowledge graphs (KG) could effectively excavate potential association information between different entities. To improve the effectiveness of drug discovery, a transporter-related KG was constructed in this study. Meanwhile, a predictive frame (AutoInt_KG) and a generative frame (MolGPT_KG) were established based on the heterogeneity information obtained from the transporter-related KG by the RESCAL model. Natural product Luteolin with known transporters was selected to verify the reliability of the AutoInt_KG frame, its ROC-AUC (1:1), ROC-AUC (1:10), PR-AUC (1:1), PR-AUC (1:10) are 0.91, 0.94, 0.91 and 0.78, respectively. Subsequently, the MolGPT_KG frame was constructed to implement efficient drug design based on transporter structure. The evaluation results showed that the MolGPT_KG could generate novel and valid molecules and that these molecules were further confirmed by molecular docking analysis. The docking results showed that they could bind to important amino acids at the active site of the target transporter. Our findings will provide rich information resources and guidance for the further development of the transporter-related drugs.

Baicalin attenuates LPS-induced alveolar type II epithelial cell A549 injury by attenuation of the FSTL1 signaling pathway via increasing miR-200b-3p expression.

In China, baicalin is the main active component of Scutellaria baicalensis, which has been used in the treatment of inflammation-related diseases, such as inflammation-induced acute lung injury. However, its specific mechanism remains unclear. This study examined the protective effect of baicalin on LPS-induced inflammation injury of alveolar epithelial cell line A549 and explored its protective mechanism. Compared with the LPS-induced group, the proliferation inhibition rates of alveolar type II epithelial cell line A549 intervened by different concentrations of baicalin decreased significantly, as did the levels of inflammatory factors IL-6, IL-1ß, prostaglandin 2 and TNF-α in the supernatant. The expression levels of inflammatory proteins inducible NO synthase (iNOS), NF-κB65, phosphorylated ERK (p-ERK1/2), and phosphorylated c-Jun N-terminal kinase (p-JNK1) significantly decreased, as did the protein expression of follistatin-like protein 1 (FSTL1). In contrast, expression of miR-200b-3p significantly increased in a dose-dependent manner. These results suggested that baicalin could significantly inhibit the expression of inflammation-related proteins and improve LPS-induced inflammatory injury in alveolar type II epithelial cells. The mechanism may be related to the inhibition of ERK/JNK inflammatory pathway activation by increasing the expression of miR-200b-3p. Thus, FSTL1 is the regulatory target of miR-200b-3p.

Anticancer effects of ginsenoside Rg3 (Review).

Cancer is a life-threatening disease with an alarmingly increased annual mortality rate globally. Although various therapies are employed for cancer, the final effect is not satisfactory. Chemotherapy is currently the most commonly used treatment option. However, the unsatisfactory efficacy, severe side-effects and drug resistance hinder the therapeutic efficacy of chemotherapeutic drugs. There is increasing evidence indicating that ginsenoside Rg3, a naturally occurring phytochemical, plays an important role in the prevention and treatment of cancer. The suggested mechanisms mainly include the induction of apoptosis, and the inhibition of proliferation, metastasis and angiogenesis, as well as the promotion of immunity. In addition, ginsenoside Rg3 can be used as an adjuvant to conventional cancer therapies, improving the efficacy and/or reducing adverse effects via synergistic activities. Ginsenoside Rg3 may be a widely applied natural medicine against cancer. To date however, there is no systematic summary available of the anticancer effects of ginsenoside Rg3. Therefore, in this review, all available literature over the past 10 years was reviewed and discussed in order to facilitate further research of ginsenoside Rg3.

[Clinical significance of detecting partial cytokines in serum of patients with pulmonary tuberculosis by cytometric bead array].

AIM: To explore the clinical significance of cytometric bead array(CBA) in detecting partial cytokines(IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ)in the serum from patients with different types of pulmonary tuberculosis. METHODS: The levels of six cytokines (IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ) in the serum from 84 cases of patients with pulmonary tuberculosis(46 cases of active pulmonary tuberculosis patients, 38 cases of inactive pulmonary tuberculosis patients) and 30 cases of normal person, were detected by BD CBA Flex Set kit. RESULTS: The levels of IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ in pulmonary tuberculosis group were significantly higher than those in control group (P<0.01 or P<0.05.Compared with the inactive pulmonary tuberculosis group, the levels of serum IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ in the active pulmonary tuberculosis group were significantly higher (P<0.01 or P<0.05).Serum IL-4 and TNF-α levels in active pulmonary tuberculosis group, inactive pulmonary tuberculosis group and the control group had not significantly different. CONCLUSION: Six cytokines in serum can be detected quickly and sensitively in batches at one time by CBA. IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ play an important role in the immune pathogenesis of pulmonary tuberculosis.