In silico design of novel probes for the atypical opioid receptor MRGPRX2

The primate-exclusive MRGPRX2 G protein-coupled receptor (GPCR) has been suggested to modulate pain and itch. Despite putative peptide and small molecule MRGPRX2 agonists, selective nanomolar potency probes have not yet been reported. To identify a MRGPRX2 probe, we first screened 5,695 small molecules and found many opioid compounds activated MRGPRX2, including (−)- and (+)-morphine, hydrocodone, sinomenine, dextromethorphan and the prodynorphin-derived peptides, dynorphin A, dynorphin B, and α- and β-neoendorphin. We used these to select for mutagenesis-validated homology models and docked almost 4 million small molecules. From this docking, we predicted ZINC-3573, which represents a potent MRGPRX2-selective agonist, showing little activity against 315 other GPCRs and 97 representative kinases, and an essentially inactive enantiomer. ZINC-3573 activates endogenous MRGPRX2 in a human mast cell line inducing degranulation and calcium release. MRGPRX2 is a unique atypical opioid-like receptor important for modulating mast cell degranulation, which can now be specifically modulated with ZINC-3573

The EII(Glc) Protein Is Involved in Glucose-Mediated Activation of Escherichia coli gapA and gapB-pgk Transcription

The Escherichia coli gapB gene codes for a protein that is very similar to bacterial glyceraldehyde-3-phosphate dehydrogenases (GAPDH). In most bacteria, the gene for GAPDH is located upstream of the pgk gene encoding 3-phosphoglycerate kinase (PGK). This is the case for gapB. However, this gene is poorly expressed and encodes a protein with an erythrose 4-phosphate dehydrogenase activity (E4PDH). The active GAPDH is encoded by the gapA gene. Since we found that the nucleotide region upstream of the gapB open reading frame is responsible for part of the PGK production, we analyzed gapB promoter activity in vivo by direct measurement of the mRNA levels by reverse transcription. We showed the presence of a unique transcription promoter, gapB P0, with a cyclic AMP (cAMP) receptor protein (CRP)-cAMP binding site centered 70.5 bp upstream of the start site. Interestingly, the gapB P0 promoter activity was strongly enhanced when glucose was used as the carbon source. In these conditions, deletion of the CRP-cAMP binding site had little effect on promoter gapB P0 activity. In contrast, abolition of CRP production or of cAMP biosynthesis (crp or cya mutant strains) strongly reduced promoter gapB P0 activity. This suggests that in the presence of glucose, the CRP-cAMP complex has an indirect effect on promoter gapB P0 activity. We also showed that glucose stimulation of gapB P0 promoter activity depends on the expression of enzyme II(Glc) (EII(Glc)), encoded by the ptsG gene, and that the gapA P1 promoter is also activated by glucose via the EII(Glc) protein. A similar glucose-mediated activation, dependent on the EII(Glc) protein, was described by others for the pts operon. Altogether, this shows that when glucose is present in the growth medium expression of the E. coli genes required for its uptake (pts) and its metabolism (gapA and gapB-pgk) are coordinately activated by a mechanism dependent upon the EII(Glc) protein

Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana”

The continuous rise of the COVID-19 Omicron cases despite the vaccination program available has been progressing worldwide. To mitigate the COVID-19 contraction, different contact tracing applications have been utilized such as Thai Chana from Thailand. This study aimed to predict factors affecting the perceived usability of Thai Chana by integrating the Protection Motivation Theory and Technology Acceptance Theory considering the System Usability Scale, utilizing deep learning neural network and random forest classifier. A total of 800 respondents were collected through convenience sampling to measure different factors such as understanding COVID-19, perceived severity, perceived vulnerability, perceived ease of use, perceived usefulness, attitude towards using, intention to use, actual system use, and perceived usability. In total, 97.32% of the deep learning neural network showed that understanding COVID-19 presented the most significant factor affecting perceived usability. In addition, random forest classifier produced a 92% accuracy with a 0.00 standard deviation indicating that understanding COVID-19 and perceived vulnerability led to a very high perceived usability while perceived severity and perceived ease of use also led to a high perceived usability. The findings of this study could be considered by the government to promote the usage of contact tracing applications even in other countries. Finally, deep learning neural network and random forest classifier as machine learning algorithms may be utilized for predicting factors affecting human behavior in technology or system acceptance worldwide