Identification of highly conserved surface-exposed peptides of spike protein for multiepitope vaccine design against emerging omicron variants: An immunoinformatic approach.

The COVID-19 pandemic, originating in Wuhan in 2019, was caused by SARS-CoV-2, leading to significant global fatalities. Despite the development of vaccines, the virus mutates, creating variants that evade vaccine-induced immunity. To address SARS-CoV-2's evolving nature, a multiepitope vaccine was developed using immunoinformatics approach, specifically targeting the Omicron variant's spike protein. This vaccine includes six CD8 + and eleven CD4 + epitopes selected for their immunogenicity, non-toxicity, and significant conservation among former Variants of Concern (VOCs) and Variants of Interest (VOIs), such as Alpha, Beta, Gamma, Delta, Lambda, Mu, R1, and Zeta, as well as current Variants Under Monitoring (VUMs) like XBB.1.5, XBB.1.16, EG.5, BA.2.86, and JN.1. Notably, certain epitopes like ELLHAPATV and PYRVVVLSFELLHAP were fully conserved across all tested variants in the spike protein's receptor binding domain (RBD). Others, such as NATRFASVYAWNRKR, were fully conserved in all former VOCs and VOIs and 93.33 % in current VUMs, while ERDISTEIYQAGNKP was entirely conserved in current VUMs within the RBD region. The study went on to model, refine, and validate the vaccine prototype's tertiary structure. Docking experiments and molecular dynamic simulations revealed robust and stable interactions with Toll-like receptor 4. Cloning and codon optimization confirmed successful expression in E. coli. Subsequently, the immunological reaction of the multiepitope vaccine demonstrated that the three-time administration of the prototype significantly enhanced the antibody response while decreasing the number of antigens. The designed vaccine's epitopes showed significant combined global population coverage of 100 % with 89.75 % for CD8 + and 99.98 % for CD4 + epitopes and conservation across SARS-CoV-2 variants especially in current monitoring omicron subvariants, supporting its broader applicability and potential efficacy. Although, this promising vaccine candidate needs to undergo clinical trials to determine its effectiveness in neutralising SARS-CoV-2.

Machine learning model for predicting Major Depressive Disorder using RNA-Seq data: optimization of classification approach.

Considering human brain disorders, Major Depressive Disorder (MDD) is seen as a lethal disease in which a person goes to the extent of suicidal behavior. Physical detection of MDD patients is less precise but machine learning can aid in improved classification of disease. The present research included three RNA-seq data classes to classify DEGs and then train key gene data using a random forest machine learning method. The three classes in the sample are 29 CON (sudden death healthy control), 21 MDD-S (a Major Depressive Disorder Suicide) being included in the second group, and 9 MDD (non-suicides MDD) which are included in the third group. With PCA analysis, 99 key genes were obtained. 47.1% data variability is given by these 99 genes. The model training of 99 genes indicated improved classification. The RF classification model has an accuracy of 61.11% over test data and 97.56% over train data. It was also noticed that the RF method offered greater accuracy than the KNN method. 99 genes were annotated using DAVID and ClueGo packages. Some of the important pathways and function observed in the study were glutamatergic synapse, GABA receptor activation, long-term synaptic depression, and morphine addiction. Out Of 99 genes, four genes, namely DLGAP1, GNG2, GRIA1, and GRIA4, were found to be predominantly involved in the glutamatergic synapse pathway. Another substantial link was observed in the GABA receptor activation involving the following two genes, GABBR2 and GNG2. Also, the genes found responsible for long-term synaptic depression were GRIA1, MAPT, and PTEN. There was another finding of morphine addiction which comprises three genes, namely GABBR2, GNG2, and PDE4D. For massive datasets, this approach will act as the gold standard. The cases of CON, MDD, and MDD-S are physically distinct. There was dysregulation in the expression level of 12 genes. The 12 genes act as a possible biomarker for Major Depressive Disorder and open up a new path for depressed subjects to explore further.

Transcriptomics and sequencing analysis of gene expression profiling for major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a common psychiatric disorder characterized by constant sadness and a lack of interest in work and social interactions. Maintaining the transcriptome levels via the controlled regulation of mRNA processing and transport is essential to alleviating MDD. Various molecular phenotypes such as aberrant RNA splicing and stability are identified as critical determinants of MDD. AIM: This study aims to compare the mRNA expression profiles between major depressive disorder non-suicide (MDD), major depressive disorder suicide (MDD-S), and control groups using RNA-Seq. MATERIALS AND METHODS: A transcriptomics and sequencing analysis of gene expression profiling was conducted in 9 patients with MDD, 10 patients with MDD-S, and 10 control patients. RESULTS: A comparison of the sample groups revealed that the PRKACB gene was upregulated in patients with MDD. At the same time, GRM3, DLGAP1, and GRIA2 were downregulated in these patients-these genes are majorly involved in the glutamatergic pathway. Five genes (GRIA1, CAMK2D, PPP3CA, MAPK10, and PPP2R2A) of the dopaminergic pathway were downregulated in patients with the MDD-S condition when compared with the MDD and control groups. Cholinergic synapses were altered in patients with MDD when compared to the control group due to the presence of dysregulated genes (KCNQ5, PLCB4, ADCY9, CAMK2D, PIK3CA, and GNG2). CONCLUSION: The results provide a new understanding of the etiology of depression in humans and identify probable depression-associated biomarkers.

Comparative evolutionary analysis of cell cycle proteins networks in fission and budding yeast.

Fission yeast and budding yeast are the two distantly related species with common ancestors. Various studies have shown significant differences in metabolic networks and regulatory networks. Cell cycle regulatory proteins in both species have differences in structural as well as in functional organization. Orthologous proteins in cell cycle regulatory protein networks seem to play contemporary role in both species during the evolution but little is known about non-orthologous proteins. Here, we used system biology approach to compare topological parameters of orthologous and non-orthologous proteins to find their contributions during the evolution to make an efficient cell cycle regulation. Observed results have shown a significant role of non-orthologous proteins in fission yeast in maintaining the efficiency of cell cycle regulation with less number of proteins as compared to budding yeast.

Protein interaction network analysis--approach for potential drug target identification in Mycobacterium tuberculosis.

In host-parasite diseases like tuberculosis, non-homologous proteins (enzymes) as drug target are first preference. Most potent drug target can be identified among large number of non-homologous protein through protein interaction network analysis. In this study, the entire promising dimension has been explored for identification of potential drug target. A comparative metabolic pathway analysis of the host Homo sapiens and the pathogen M. tuberculosis H37Rv has been performed with three level of analysis. In first level, the unique metabolic pathways of M. tuberculosis have been identified through its comparative study with H. sapiens and identification of non-homologous proteins has been done through BLAST similarity search. In second level, choke-point analysis has been performed with identified non-homologous proteins of metabolic pathways. In third level, two type of analysis have been performed through protein interaction network. First analysis has been done to find out the most potential metabolic functional associations among all identified choke point proteins whereas second analysis has been performed to find out the functional association of high metabolic interacting proteins to pathogenesis causing proteins. Most interactive metabolic proteins which have highest number of functional association with pathogenesis causing proteins have been considered as potential drug target. A list of 18 potential drug targets has been proposed which are various stages of progress at the TBSGC and proposed drug targets are also studied for other pathogenic strains. As a case study, we have built a homology model of identified drug targets histidinol-phosphate aminotransferase (HisC1) using MODELLER software and various information have been generated through molecular dynamics which will be useful in wetlab structure determination. The generated model could be further explored for insilico docking studies with suitable inhibitors.

Structure prediction of dihydroflavonol 4- reductase and anthocyanidin synthase from spinach.

Spinach is an important dietary vegetable associated with beneficial health effects. Flavonoids have various biological activities such as antioxidant, antibacterial, and anticancer effect Flavonoid including anthocyanin provides brilliant and colored pigments in different plant tissues. Anthocyanidin synthase and dihydroflavonol 4-reductase are responsible for anthocyanin biosynthesis. They contributed in plant protection against UV-B radiation, microbial and herbivore pathogens. A 3D structures of anthocyanidin synthase and dihydroflavonol 4-reductase from spinach are constructed in this study through homology modeling. The homology modeling is done by using the MODELLER 9v7 software. The energy of models was minimized by applying molecular mechanics method. The root mean square deviation (RMSD) for C atoms between the template and the homology-modeled structures was estimated by CE program. The final models were assessed by PROCHECK and WHATCHECK which showed that the final refined models are reliable.

Modeling signaling pathways leading to wrinkle formation: identification of the skin aging target.

BACKGROUND: In the present scenario, wrinkle formation, prominent sign of skin ageing, is one of the most demanding areas of research. This burgeoning research demand to reduce, delay and restore the effects of skin ageing has led to the study of various signaling pathways leading to wrinkle formation. Wrinkles appear on skin due to influence of intrinsic and extrinsic factors on mitogenic reactions and signal transduction pathways. AIM: The aim of the present study is to analyze each protein involved in the signaling pathway leading to dilapidation of collagen and an attempt has been made to compare different signal transduction pathways to identify a common target for skin ageing. METHODS: In the present work, bioinformatics tools have been used to extract information from already existing experimental data. The statistical techniques are used for further analysis and make useful predictions for skin ageing. RESULTS: Stressors like UV irradiation, osmotic stress and heat shock have been reported to activate epidermal growth factor receptor, interleukin 1 receptor, tumor necrosis factor receptor, platelet-derived growth factor receptor and platelet activation factor receptor signaling pathways, which lead to the production of matrix metalloproteinases, collagen degradation and, consequently, wrinkle formation. When all the five signaling pathways were modeled, the c-jun part of the AP-1 transcription factor was found to be a common intermediate protein involved in all the signaling cascades. Moreover, it shows differential expression in the skin on response to stressors. CONCLUSION: We proposed c-jun to be the most potent target for drug designing against wrinkle formation.

PINAT1.0: protein interaction network analysis tool.

UNLABELLED: Cellular processes are regulated by interaction of various proteins i.e. multiprotein complexes and absences of these interactions are often the cause of disorder or disease. Such type of protein interactions are of great interest for drug designing. In host-parasite diseases like Tuberculosis, non-homologous proteins as drug target are first preference. Most potent drug target can be identifying among large number of non-homologous protein through protein interaction network analysis. Drug target should be those non-homologous protein which is associated with maximum number of functional proteins i.e. has highest number of interactants, so that maximum harm can be caused to pathogen only. In present work, Protein Interaction Network Analysis Tool (PINAT) has been developed to identification of potential protein interaction for drug target identification. PINAT is standalone, GUI application software made for protein-protein interaction (PPI) analysis and network building by using co-evolutionary profile. PINAT is very useful for large data PPI study with easiest handling among available softwares. PINAT provides excellent facilities for the assembly of data for network building with visual presentation of the results and interaction score. The software is written in JAVA and provides reliability through transparency with user. AVAILABILITY: PINAT is available at www.manit.ac.in/pinat.

Model based design of inhibitors for c-jun.

Literature shows that various molecular cascades are activated by stress, UV rays and pollutants leading to wrinkle formation of the skin. These cascades start from five types of receptors (EGFR, PDGFR, PAFR, IL1R, TNFRB) and terminate with the production of matrix metalloproteinase's, which degrades collagen leading to wrinkle formation. Signaling pathway leading to wrinkle formation showed that c-jun is involved in these cascades. Therefore, c-jun is the preferential choice for inhibition to reduce the intensity of collagen degradation. Hence, the 3D structure of c-jun was modeled using segment based homology modeling by MODELLER 9v5. Evaluation of the constructed model was done by PROCHECK, WHAT CHECK and through RMSD/RMSF calculations. Ligands for the inhibitory sites were designed using LIGANDSCOUT. The interaction study of ligand and receptor was performed by AUTODOCK. A library of analogues was constructed for three known inhibitory sites. The receptor-analogue study was performed using the software MOLEGRO Virtual Docker. The analogues constructed from the designed novel reference ligands showed good binding with the receptor binding sites. It should be noted that these predicted data should be validated using suitable assays for further consideration.