PRR-HyPred: A two-layer hybrid framework to predict pattern recognition receptors and their families by employing sequence encoded optimal features.

Pattern recognition receptors (PRRs) recognize distinct features on the surface of pathogens or damaged cells and play key roles in the innate immune system. PRRs are divided into various families, including Toll-like receptors, retinoic acid-inducible gene-I-like receptors, nucleotide oligomerization domain-like receptors, and C-type lectin receptors. As these are implicated in host health and several diseases, their accurate identification is indispensable for their functional characterization and targeted therapeutic approaches. Here, we construct PRR-HyPred, a novel two-layer hybrid framework in which the first layer predicts whether a given sequence is PRR or non-PRR using a support vector machine, and in the second, the predicted PRR sequence is assigned to a specific family using a random forest-based classifier. Based on a 10-fold cross-validation test, PRR-HyPred achieved 83.4 % accuracy in the first layer and 95 % in the second, with Matthew's correlation coefficient values of 0.639 and 0.816, respectively. This is the first study that can simultaneously predict and classify PRRs into specific families. PRR-HyPred is available as a web portal at https://procarb.org/PRRHyPred/. We hope that it could be a valuable tool for the large-scale prediction and classification of PRRs and subsequently facilitate future studies.

Gastric Cancer and Viruses: A Fine Line between Friend or Foe.

Gastric cancer (GC) is a significant health concern worldwide, with a GLOBOCAN estimate of 1.08 million novel cases in 2020. It is the leading cause of disability-adjusted life years lost to cancer, with the fourth most common cancer in males and the fifth most common cancer in females. Strategies are pursued across the globe to prevent gastric cancer progression as a significant fraction of gastric cancers have been linked to various pathogenic (bacterial and viral) infections. Early diagnosis (in Asian countries), and non-invasive and surgical treatments have helped manage this disease with 5-year survival for stage IA and IB tumors ranging between 60% and 80%. However, the most prevalent aggressive stage III gastric tumors undergoing surgery have a lower 5-year survival rate between 18% and 50%. These figures point to a need for more efficient diagnostic and treatment strategies, for which the oncolytic viruses (OVs) appear to have some promise. OVs form a new therapeutic agent class that induces anti-tumor immune responses by selectively killing tumor cells and inducing systemic anti-tumor immunity. On the contrary, several oncogenic viruses have been shown to play significant roles in malignancy progression in the case of gastric cancer. Therefore, this review evaluates the current state of research and advances in understanding the dual role of viruses in gastric cancer.

Structure prediction of transferrin receptor protein 1 (TfR1) by homology modelling, docking, and molecular dynamics simulation studies.

Transferrin receptor protein 1 (TfR1) is an important molecule in anti-cancer therapy. Targeted delivery of such therapeutic compounds improves their cellular uptake and circulation time, thereby enhancing therapeutic efficacy. Drug designing is therefore used to engineer molecules with structures that facilitate specific interactions. However, this process requires a thorough knowledge of all the interactions, including the three-dimensional (3D) and quaternary structures (QS) of the interacting molecules. Since structural information is available for only a part of the full TfR1 sequence, in the present study, we predicted the whole structure of TfR1 using homology modelling, docking, and molecular dynamics simulations. Homology modelling is used to generate 3D structures of TfR1 using MODELLER, I-TASSER, and RaptorX programs. Verify3D and Rampage server evaluated the quality of the resultant models. According to this evaluation, the model built by the RaptorX server and validated by Verify3D (compatibility: 83.82%) had the highest number of residues (95.5%) within the favoured regions of the Ramachandran plot, making it the most reliable 3D protein structure for TfR1 compared with others. The QS of TfR1 was built using HADDOCK and SymmDock docking software, and the results were evaluated by the ligand root mean square deviation (l-RMSD) value computed using the ProFit software. This showed that both HADDOCK and SymmDock gave acceptable results. However, the HADDOCK result was more stable and closest to the native complex structure with disulfide bonds. Therefore, the HADDOCK complex was further refined using both SymmRef and GalaxyRefineComplex until the medium l-RMSD rank was reached. This QS was successfully verified using nanoscale molecular dynamics (NAMD) energy minimization. This model could pave the way for further functional, structural, and therapeutic studies on TfR1.

Identification and analysis of pathogenic nsSNPs in human LSP1 gene.

LSP1 (Lymphocyte-specific protein 1) protein plays an important role in neutrophil motility, fibrinogen matrix proteins adhesion, and trans-endothelial migration. Variation in the LSP1 gene is associated with leukemia and lymphomas in tumor cells of Hodgkin's disease and breast cancer. Despite extensive study on the human LSP1, a comprehensive analysis on the Single Nucleotide Polymorphism (SNPs) of the gene is not available. Therefore, it is of interest to identify, collect, store and analyze the SNPs of the LSP1 gene in relation to several known diseases. Hence, the SNP data (398 rsids) from dbSNP database was downloaded and mapped to the genomic coordinate of "NM_002339.2" transcript expressed by LSP1 (P33241). There were 300 nsSNPs with missense mutation in the dataset. Tools such as SIFT, PROVEAN, Condel, and PolyPhen-2 were further used to identify 29 highly deleterious or damaging on synonymous SNP (nsSNPs) for LSP1. These high confident damaging nsSNPs were further analyzed for disease association using SNPs and GO tool. SNPs of the gene such as nsSNPs C283R, G234R, Y328D and H325P showed disease association with high prevalence.

Control of ß-sitosterol biosynthesis under light and watering in desert plant Calotropis procera.

Most scientific studies on Calotropis procera refer to the plant as an important source of pharmaceutical compounds and its valuable benefits in medicine. One of the most important substances in this plant is the potential immunostimulant ß-sitosterol (BS) that acts in improving human health. This study focused on the effects of lighting before and after irrigation on the BS accumulation pathway namely steroid biosynthesis. Studying the enzymes in BS biosynthetic pathway indicated the upregulation at dawn and predusk of the SMT2 and SMO2 genes encoding sterol methyltransferase 2 and methylsterol monooxygenase, two key enzymes in BS accumulation in C. procera. The results almost indicated no regulation at the different time points of the CYP710A gene encoding sterol 22-desaturase, an enzyme that acts in depleting ß-sitosterol towards the biosynthesis of stigmasterol. RNA-Seq data was validated via quantitative RT-PCR and results were positive. The data of ultra-performance liquid chromatography-tandem mass spectrometry analysis with regard to BS accumulation also aligned with those of RNA-Seq analysis. We focused on the effects of light before and after watering on BS accumulation in C. procera. Our results show that BS accumulation is high at dawn in both dehydrated and well-watered condition. While, the BS was dramatically decrease at midday in well-watered plants. This increase/decrease in BS content is correlated with rates of expression of SMT 2 gene. This gene is a key convertor between the different branches in the cardiac glycoside biosynthesis. Accordingly, it could be suggested that BS (or one of the descendent product) may play an important role in C. procera tolerance to drought/light intensity conditions.