Analysis of Genetic Polymorphism and the Identification of the mi-RNA Binding Sites of the Hypoxia Related Genes in Indian Breeds of Cattle

Background: There are certain cattle breeds which are adaptable to the temperate type environmental conditions of high altitude regions of Himalayas. The genome of such cattle have signature of adaptability in the genes related to the hypoxia. Therefore, the current study was undertaken on three hypoxia related genes (EGLN2, EGLN3 and EPAS1) in four breeds of cattle adapted to the diverse agro-climatic conditions of high (Ladakhi and Siri) and low (Hallikar and Kankrej) altitude.Methods: The genotyping of the samples was carried out by using 777 K BovineHD BeadChip (Illumina) at Agri genome Pvt. Ltd (Kerala) and the in-silico analysis of the samples was carried out at ICAR-NBAGR during 2019-2020. The SNPs underlying the genes were evaluated for the gene and genotypic frequencies at nine SNP loci residing in these three candidate genes (EGLN2, EGLN3 and EPAS1). The diversity parameters for these SNPs were assessed by GenAlEx 6.2 software and Minor Allele Frequency (MAF) differences among the breeds were calculated by Duncan’s Multiple Range Test (DMRT) using SAS software. The mi-RNA binding sites in the 3’UTR region of genes were identified by TargetScan software.Result: Higher level of polymorphism was obtained in the Ladakhi and Siri breeds of cattle of high altitude/ cold adapted region than Hallikar and Kankrej of plain and hot arid/ semi-arid region. Several mi-RNA binding sites were obtained in the 3’UTR region of these 3 genes by Target Scan software. The polymorphism obtained in these candidate genes can be utilized in the markers assisted selection of the animals more adapted to the extreme cold and high altitude region for their genetic improvement as well as designing tools for the therapy of the diseases prevalent at high altitude.

DrugPred_RNA – Structure-Based Druggability Predictions for RNA Binding Sites

RNA is an emerging target for drug discovery. However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predicator DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both, RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered sensible predictions for selected RNA binding sites. In addition, the majority of drug-like ligands contained in a data set of RNA pockets were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational changes in the binding site and can contribute to direct drug discovery efforts for RNA targets.

Binding of Campylobacter jejuni FliW Adjacent to the CsrA RNA-Binding Pockets Modulates CsrA Regulatory Activity

Campylobacter jejuni CsrA is an mRNA-binding, post-transcriptional regulator that controls many metabolic- and virulence-related characteristics of this important pathogen. In contrast to E. coli CsrA, whose activity is modulated by binding to small non-coding RNAs (sRNAs), C. jejuni CsrA activity is controlled by binding to the CsrA antagonist FliW. In this study, we identified the FliW binding site on CsrA. Deletion of the C-terminus of C. jejuni CsrA, which is extended relative to sRNA-binding CsrA proteins, abrogated FliW binding. Bacterial two-hybrid experiments were used to assess the interaction of FliW with wild-type CsrA and mutants thereof, in which every amino acid was individually mutated. Two CsrA mutations (V51A and N55A) resulted in a significant decrease in FliW binding. The V51A and N55A mutants also showed a decrease in CsrA-FliW complex formation, as assessed by size-exclusion chromatography and surface plasmon resonance. These residues were highly conserved in bacterial species containing CsrA orthologs whose activities are predicted to be regulated by FliW. The location of FliW binding was immediately adjacent to the two RNA-binding sites of the CsrA homodimer, suggesting the model that FliW binding to CsrA modulates its ability to bind to its mRNA targets either by steric hindrance, electrostatic repulsion, or by altering the overall structure of the RNA-binding sites.

CSBPI_Site：Multi-Information Sources of Features to RNA Binding Sites Prediction

Background: RNA-binding proteins establish posttranscriptional gene regulation by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. The immunoprecipitation experiments could identify interaction between RNA and proteins, but they are limited due to the experimental environment and material. Therefore, it is essential to construct computational models to identify the function sites. Objective: Although some computational methods have been proposed to predict RNA binding sites, the accuracy could be further improved. Moreover, it is necessary to construct a dataset with more samples to design a reliable model. Here we present a computational model based on multi-information sources to identify RNA binding sites. Method: We construct an accurate computational model named CSBPI_Site, based on xtreme gradient boosting. The specifically designed 15-dimensional feature vector captures four types of information (chemical shift, chemical bond, chemical properties and position information). Results: The satisfied accuracy of 0.86 and AUC of 0.89 were obtained by leave-one-out cross validation. Meanwhile, the accuracies were slightly different (range from 0.83 to 0.85) among three classifiers algorithm, which showed the novel features are stable and fit to multiple classifiers. These results showed that the proposed method is effective and robust for noncoding RNA binding sites identification. Conclusion: Our method based on multi-information sources is effective to represent the binding sites information among ncRNAs. The satisfied prediction results of Diels-Alder riboz-yme based on CSBPI_Site indicates that our model is valuable to identify the function site.

DrugPred_RNA – Structure-Based Druggability Predictions for RNA Binding Sites

RNA is an emerging target for drug discovery.However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predicator DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both, RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered sensible predictions for selected RNA binding sites. Further, the majority of drug-like ligands contained in a data set of RNA-containing pockets were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational changes in the binding site and can contribute to direct drug discovery efforts for RNA targets

DrugPred_RNA – Structure-Based Druggability Predictions for RNA Binding Sites

RNA is an emerging target for drug discovery.However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predicator DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both, RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered sensible predictions for selected RNA binding sites. Further, the majority of drug-like ligands contained in a data set of RNA-containing pockets were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational changes in the binding site and can contribute to direct drug discovery efforts for RNA targets