Pharmacoinformatics and molecular docking reveal potential drug candidates against Schizophrenia to target TAAR6.

Schizophrenia (SZ) is a complex disabling disorder that leads to the mental disability and afflicts 1% of the world's total population and placed in top ten medical disorders. In current work, bioinformatics analyses were carried out on Trace amine (TA)-associated receptor 6 (TAAR6) to recognize the potential drugs and compounds against SZ. Comparative modeling and threading-based approaches were utilized for the structure prediction of TAAR6. Fifty-nine predicted structures were evaluated by various model assessment techniques and final model having only eight amino acids in the outlier region and 98.5% overall quality factor was chosen for further pharmacoinformatics and molecular docking analyses. From an extensive literature review, 11 Food and Drug Administration (FDA) approved drugs were analyzed by computational techniques and Aripiprazole was found as the most effective drug against SZ by targeting TAAR6. Here, we report five novel molecules which exhibited the highest binding affinity, effective drug properties, and interestingly, observed better results than the approved selected drugs against SZ by targeting TAAR6. The docking analyses revealed that Arg-92, Trp-98, Gln-191, Thr-192, Ala-290, Cys-291, Tyr-293, and Glu-294 residues were observed as critical interacting residues in receptor-ligand interactions. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, Lipinski rule of five, highest binding affinity coupled with virtual screening (VS), and pharmacophore modeling approach illustrated that aripiprazole (-8.6 kcal/mol) and TAAR6_0094 (-9.3 kcal/mol) are potential inhibitors for targeting TAAR6. It is suggested that schizophrenic patients have to use Aripiprazole for the medication of SZ by targeting TAAR6 and develop effective therapies by utilizing scrutinized novel compound.

Mitochondrial E3 ligase MARCH5 regulates FUNDC1 to fine-tune hypoxic mitophagy.

Mitophagy is an essential process for mitochondrial quality control and turnover. It is activated by two distinct pathways, one dependent on ubiquitin and the other dependent on receptors including FUNDC1. It is not clear whether these pathways coordinate to mediate mitophagy in response to stresses, or how mitophagy receptors sense stress signals to activate mitophagy. We find that the mitochondrial E3 ligase MARCH5, but not Parkin, plays a role in regulating hypoxia-induced mitophagy by ubiquitylating and degrading FUNDC1. MARCH5 directly interacts with FUNDC1 to mediate its ubiquitylation at lysine 119 for subsequent degradation. Degradation of FUNDC1 by MARCH5 expression desensitizes mitochondria to hypoxia-induced mitophagy, whereas knockdown of endogenous MARCH5 significantly inhibits FUNDC1 degradation and enhances mitochondrial sensitivity toward mitophagy-inducing stresses. Our findings reveal a feedback regulatory mechanism to control the protein levels of a mitochondrial receptor to fine-tune mitochondrial quality.

Immunoinformatics and molecular docking studies reveal potential epitope-based peptide vaccine against DENV-NS3 protein.

Dengue, still a "Neglected Tropical Disease" is somehow injustice and remains uncontrolled globally. World Health Organization (2012-2020) reported that the world's half population is living in dengue-affected regions. Therefore, effective drug candidates or promising vaccines are urgently needed to control the dengue. It is an acute febrile disease caused by mosquito borne dengue viruses (DENVs) which belong to the genus Flavivirus with four serotypes. In present work, immunoinformatics approach was utilized to predict the antigenic epitopes of dengue proteins for the development of DENV vaccine. B-cell and cytotoxic T-lymphocyte epitopes were predicted for NS3 dengue protein. Docking complexes of 17 antigenic B-cell epitopes of various lengths and 4 CTL epitopes with antigenic sites were investigated followed by binding interaction analyses of top predicted peptides with MHC-I HLA-A2 molecule. These predicted epitopes with antigenic amino acids might present a preliminary set of peptides for future vaccine development against DENV.

Structural, phylogenetic and docking studies of D-amino acid oxidase activator (DAOA), a candidate schizophrenia gene.

BACKGROUND: Schizophrenia is a neurodegenerative disorder that occurs worldwide and can be difficult to diagnose. It is the foremost neurological disorder leading to suicide among patients in both developed and underdeveloped countries. D-amino acid oxidase activator (DAOA), also known as G72, is directly implicated in the glutamateric hypothesis of schizophrenia. It activates D-amino acid oxidase, which oxidizes D-serine, leading to modulation of the N-methyl-D-aspartate receptor. METHODS: MODELLER (9v10) was utilized to generate three dimensional structures of the DAOA candidate gene. The HOPE server was used for mutational analysis. The Molecular Evolutionary Genetics Analysis (MEGA5) tool was utilized to reconstruct the evolutionary history of the candidate gene DAOA. AutoDock was used for protein-ligand docking and Gramm-X and PatchDock for protein-protein docking. RESULTS: A suitable template (1ZCA) was selected by employing BLASTp on the basis of 33% query coverage, 27% identity and E-value 4.9. The Rampage evaluation tool showed 91.1% favored region, 4.9% allowed region and 4.1% outlier region in DAOA. ERRAT demonstrated that the predicted model had a 50.909% quality factor. Mutational analysis of DAOA revealed significant effects on hydrogen bonding and correct folding of the DAOA protein, which in turn affect protein conformation. Ciona was inferred as the outgroup. Tetrapods were in their appropriate clusters with bifurcations. Human amino acid sequences are conserved, with chimpanzee and gorilla showing more than 80% homology and bootstrap value based on 1000 replications. Molecular docking analysis was employed to elucidate the binding mode of the reported ligand complex for DAOA. The docking experiment demonstrated that DAOA is involved in major amino acid interactions: the residues that interact most strongly with the ligand C28H28N3O5PS2 are polar but uncharged (Gln36, Asn38, Thr 122) and non-polar hydrophobic (Ile119, Ser171, Ser21, Ala31). Protein-protein docking simulation demonstrated two ionic bonds and one hydrogen bond involving DAOA. Lys-7 of the receptor protein interacted with Lys-163 and Asp-2037. Tyr-03 interacted with Arg-286 of the ligand protein and formed a hydrogen bond. CONCLUSION: The predicted interactions might serve to inhibit the disease-related allele. It is assumed that current bioinformatics methods will contribute significantly to identifying, analyzing and curing schizophrenia. There is an urgent need to develop effective drugs for schizophrenia, and tools for examining candidate genes more accurately and efficiently are required.

Tumor necrosis factor receptor superfamily 10B (TNFRSF10B): an insight from structure modeling to virtual screening for designing drug against head and neck cancer.

BACKGROUND: Head and neck cancer (HNC) belongs to a group of heterogeneous disease with distinct patterns of behavior and presentation. TNFRSF10B, a tumor suppressor gene mapped on chromosome 8. Mutation in candidate gene is responsible for the loss of chromosome p arm which is frequently observed in head and neck tumors. TNFRSF10B inhibits tumor formation through apoptosis but deregulation encourages metastasis, migration and invasion of tumor cell tissues. RESULTS: Structural modeling was performed by employing MODELLER (9v10). A suitable template [2ZB9] was retrieved from protein databank with query coverage and sequence identity of 84% and 30% respectively. Predicted Model evaluation form Rampage revealed 93.2% residues in favoured region, 5.7% in allowed region while only 1 residue is in outlier region. ERRAT and ProSA demonstrated 51.85% overall quality with a -1.08 Z-score of predicted model. Molecular Evolutionary Genetics Analysis (MEGA 5) tool was executed to infer an evolutionary history of TNFRSF10B candidate gene. Orthologs and paralogs [TNFRSF10A & TNFRSF10D] protein sequences of TNFRSF10B gene were retrieved for developed ancestral relationship. Topology of tree presenting TNFRSF10A gene considered as outgroup. Human and gorilla shared more than 90% similarities with conserved amino acid sequence. Virtual screening approach was appliedfor identification of novel inhibitors. Library (Mcule) was screened for novel inhibitors and utilized the scrutinized lead compounds for protein ligand docking. Screened lead compounds were further investigated for molecular docking studies. STRING server was employed to explore protein-protein interactions of TNFRSF10B target protein. TNFSF10 protein showed highest 0.999 confidence score and selected protein-protein docking by utilizing GRAMM-X server. In-silico docking results revealed I-58, S-90 and A-62 as most active interacting residues of TNFRSF10B receptor protein with R-130, S-156 and R-130 of TNFSF10B ligand protein. CONCLUSION: Current research may provide a backbone for understanding structural and functional insights of TNFRSF10B protein. The designed novel inhibitors and predicted interactions might serve to inhibit the disease. Effective in-vitro potent ligands are required which will be helpful in future to design a drug to against Head and neck cancer disease. There is an urgent need for affective drug designing of head and neck cancer and computational tools for examining candidate genes more efficiently and accurately are required.

Comprehensive in silico analyses of flavonoids elucidating the drug properties against kidney disease by targeting AIM2.

Kidney disorders are among the most common diseases and there is a scarcity of effective treatments for chronic kidney disease. There has been a progressive improvement in specific flavonoids for protective effects against kidney diseases. Flavonoids inhibit the regulatory enzymes to control inflammation-related diseases. In the present study, a hybrid approach of molecular docking analyses and molecular dynamic simulation was followed by principal component analyses and a dynamics cross-correlation matrix. In the present study, the top-ranked five flavonoids were reported, and the maximum binding affinity was observed against AIM2. Molecular docking analyses revealed that Glu_186, Phe_187, Lys_245, Glu_248, Ile_263, and Asn_265 are potent residues against AIM2 for ligand-receptor interactions. Extensive in silico analyses suggested that procyanidin is a potential molecule against AIM2. Moreover, the site-directed mutagenesis for the reported interacting residues of AIM2 could be important for further in vitro analyses. The observed novel results based on extensive computational analyses may be significant for potential drug design against renal disorders by targeting AIM2.

Pharmacological Progress of Mitophagy Regulation.

With the advancement in novel drug discovery, biologically active compounds are considered pharmacological tools to understand complex biological mechanisms and the identification of potent therapeutic agents. Mitochondria boast a central role in different integral biological processes and mitochondrial dysfunction is associated with multiple pathologies. It is, therefore, prudent to target mitochondrial quality control mechanisms by using pharmacological approaches. However, there is a scarcity of biologically active molecules, which can interact with mitochondria directly. Currently, the chemical compounds used to induce mitophagy include oligomycin and antimycin A for impaired respiration and acute dissipation of mitochondrial membrane potential by using CCCP/FCCP, the mitochondrial uncouplers. These chemical probes alter the homeostasis of the mitochondria and limit our understanding of the energy regulatory mechanisms. Efforts are underway to find molecules that can bring about selective removal of defective mitochondria without compromising normal mitochondrial respiration. In this report, we have tried to summarize and status of the recently reported modulators of mitophagy.

In silico elucidation of potential drug targets against oxygenase domain of Human eNOS Dysfunction.

Nitric Oxide (NO) signaling pathway plays a vital role in various physiological and pathophysiological processes including vasodilation, neurogenesis, inflammation, translation and protein regulation. NO signaling pathway is associated with various diseases such as cardiovascular diseases, vision impairment, hypertension and Alzheimer's disease. Human Endothelial Nitric Oxide Synthase (eNOS) bound with calcium regulatory protein (calmodulin (CaM)) to produce NO which initiates cGMP pathway. The current study employs to screen the novel compounds against human eNOS independent of calcium regulatory protein (CaM). The current effort emphasized that the deficiency of CaM leads to dysfunction of cGMP signaling pathway. In this work, a hybrid approach of high-throughput virtual screening and comparative molecular docking studies followed by molecular dynamic simulation analyses were applied. The screening of top ranked two novel compounds against eNOS were reported that showed effective binding affinity, retrieved through the DrugBank and ZINC database libraries. Comparative molecular docking analyses revealed that Val-104, Phe-105, Gln-247, Arg-250, Ala-266, Trp-330, Tyr-331, Pro-334, Ala-335, Val-336, Tyr-357, Met-358, Thr-360, Glu-361, Ile-362, Arg-365, Asn-366, Asp-369, Arg-372, Trp-447 and Tyr-475 are potent residues for interactional studies. High-throughput virtual screening approach coupled with molecular dynamic simulation and drug likeness rules depicted that ZINC59677432 and DB00456 are potent compounds to target eNOS. In conclusion, the proposed compounds are potent against eNOS based on extensive in silico analyses. Overall, the findings of this study may be helpful to design therapeutic targets against eNOS.

Genetic insights into obesity: in silico identification of pathogenic SNPs in MBOAT4 gene and their structural molecular dynamics consequences.

Membrane Bound O-Acyltransferase Domain-Containing 4 (MBOAT4) protein catalyzes ghrelin acylation, leading to prominent ghrelin activity, hence characterizing its role as an anti-obesity target. We extracted 625 exonic SNPs from the ENSEMBL database and one phenotype-based missense mutation associated with obesity (A46T) from the HGMD (Human Gene Mutation Database). These were differentiated on deleterious missense SNPs of the MBOAT4 gene through MAF (minor allele frequency: <0.01) cut-off criteria in relation to some bioinformatics-based supervised machine learning tools. We found 8 rare-coding and harmful missense SNPs. The consensus classifier (PredictSNP) tool predicted that the SNP (G57S, C: rs561065025) was the most pathogenic. Several trained in silico algorithms have predicted decreased protein stability [ΔΔG (kcal/mol)] function in the presence of these rare-coding pathogenic mutations in the MBOAT4 gene. Then, a stereochemical quality check (i.e. validation and assessment) of the 3D model was performed, followed by a blind cavity docking approach, used to search for druggable cavities and molecular interactions with citrus flavonoids of the Rutaceae family, ranked with energetic estimations. Significant interactions with Phloretin 3',5'-Di-C-Glucoside were also observed at R304, W306, N307, A311, L314 and H338 with (iGEMDOCK: -95.82 kcal/mol and AutoDock: -7.80 kcal/mol). The RMSD values and other variables of MD simulation analyses on this protein further validated its significant interactions with the above flavonoids. The MBOAT4 gene and its molecular interactions could serve as an interventional future anti-obesity target. The current study's findings will benefit future prospects for large population-based studies and drug development, particularly for generating personalized medicine.Communicated by Ramaswamy H. Sarma.

Synthesis and characterization of copper oxide nanoparticles: its influence on corn (Z. mays) and wheat (Triticum aestivum) plants by inoculation of Bacillus subtilis.

Nanotechnology is now playing an emerging role in green synthesis in agriculture as nanoparticles (NPs) are used for various applications in plant growth and development. Copper is a plant micronutrient; the amount of copper oxide nanoparticles (CuONPs) in the soil determines whether it has positive or adverse effects. CuONPs can be used to grow corn and wheat plants by combining Bacillus subtilis. In this research, CuONPs were synthesized by precipitation method using different precursors such as sodium hydroxide (0.1 M) and copper nitrate (Cu(NO3)2) having 0.1 M concentration with a post-annealing method. The NPs were characterized through X-ray diffraction (XRD), scanning electron microscope (SEM), and ultraviolet (UV) visible spectroscopy. Bacillus subtilis is used as a potential growth promoter for microbial inoculation due to its prototrophic nature. The JAR experiment was conducted, and the growth parameter of corn (Z. mays) and wheat (Triticum aestivum) was recorded after 5 days. The lab assay evaluated the germination in JARs with and without microbial inoculation under CuONP stress at different concentrations (25 and 50 mg). The present study aimed to synthesize CuONPs and systematically investigate the particle size effects of copper (II) oxide (CuONPs) (< 50 nm) on Triticum aestivum and Z. mays. In our results, the XRD pattern of CuONPs at 500 °C calcination temperature with monoclinic phase is observed, with XRD peak intensity slightly increasing. The XRD patterns showed that the prepared CuONPs were extremely natural, crystal-like, and nano-shaped. We used Scherrer's formula to calculate the average size of the particle, indicated as 23 nm. The X-ray diffraction spectrum of synthesized materials and SEM analysis show that the particles of CuONPs were spherical in nature. The results revealed that the synthesized CuONPs combined with Bacillus subtilis used in a field study provided an excellent result, where growth parameters of Z. Mays and Triticum aestivum such as root length, shoot length, and plant biomass was improved as compared to the control group.

Recent Trends in Computer-aided Drug Design for Anti-cancer Drug Discovery.

Cancer is considered one of the deadliest diseases globally, and continuous research is being carried out to find novel potential therapies for myriad cancer types that affect the human body. Researchers are hunting for innovative remedies to minimize the toxic effects of conventional therapies being driven by cancer, which is emerging as pivotal causes of mortality worldwide. Cancer progression steers the formation of heterogeneous behavior, including self-sustaining proliferation, malignancy, and evasion of apoptosis, tissue invasion, and metastasis of cells inside the tumor with distinct molecular features. The complexity of cancer therapeutics demands advanced approaches to comprehend the underlying mechanisms and potential therapies. Precision medicine and cancer therapies both rely on drug discovery. In vitro drug screening and in vivo animal trials are the mainstays of traditional approaches for drug development; however, both techniques are laborious and expensive. Omics data explosion in the last decade has made it possible to discover efficient anti-cancer drugs via computational drug discovery approaches. Computational techniques such as computer-aided drug design have become an essential drug discovery tool and a keystone for novel drug development methods. In this review, we seek to provide an overview of computational drug discovery procedures comprising the target sites prediction, drug discovery based on structure and ligand-based design, quantitative structure-activity relationship (QSAR), molecular docking calculations, and molecular dynamics simulations with a focus on cancer therapeutics. The applications of artificial intelligence, databases, and computational tools in drug discovery procedures, as well as successfully computationally designed drugs, have been discussed to highlight the significance and recent trends in drug discovery against cancer. The current review describes the advanced computer-aided drug design methods that would be helpful in the designing of novel cancer therapies.

In silico designing of multiepitope-based-peptide (MBP) vaccine against MAPK protein express for Alzheimer's disease in Zebrafish.

Understanding the role of the mitogen-activated protein kinases (MAPKs) signalling pathway is essential in advancing treatments for neurodegenerative disorders like Alzheimer's. In this study, we investigate in-silico techniques involving computer-based methods to extract the MAPK1 sequence. Our applied methods enable us to analyze the protein's structure, evaluate its properties, establish its evolutionary relationships, and assess its prevalence in populations. We also predict epitopes, assess their ability to trigger immune responses, and check for allergenicity using advanced computational tools to understand their immunological properties comprehensively. We apply virtual screening, docking, and structure modelling to identify promising drug candidates, analyze their interactions, and enhance drug design processes. We identified a total of 30 cell-targeting molecules against the MAPK1 protein, where we selected top 10 CTL epitopes (PAGGGPNPG, GGGPNPGSG, SAPAGGGPN, AVSAPAGGG, AGGGPNPGS, ATAAVSAPA, TAAVSAPAG, ENIIGINDI, INDIIRTPT, and NDIIRTPTI) for further evaluation to determine their potential efficacy, safety, and suitability for vaccine design based on strong binding potential. The potential to cover a large portion of the world's population with these vaccines is substantial-88.5 % for one type and 99.99 % for another. In exploring the molecular docking analyses, we examined a library of compounds from the ZINC database. Among them, we identified twelve compounds with the lowest binding energy. Critical residues in the MAPK1 protein, such as VAL48, LYS63, CYS175, ASP176, LYS160, ALA61, LEU165, TYR45, SER162, ARG33, PRO365, PHE363, ILE40, ASN163, and GLU42, are pivotal for interactions with these compounds. Our result suggests that these compounds could influence the protein's behaviour. Moreover, our docking analyses revealed that the predicted peptides have a strong affinity for the MAPK1 protein. These peptides form stable complexes, indicating their potential as potent inhibitors. This study contributes to the identification of new drug compounds and the screening of their desired properties. These compounds could potentially help reduce the excessive activity of MAPK1, which is linked to Alzheimer's disease.

Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches.

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

The multifaceted regulation of mitophagy by endogenous metabolites.

Owing to the dominant functions of mitochondria in multiple cellular metabolisms and distinct types of regulated cell death, maintaining a functional mitochondrial network is fundamental for the cellular homeostasis and body fitness in response to physiological adaptations and stressed conditions. The process of mitophagy, in which the dysfunctional or superfluous mitochondria are selectively engulfed by autophagosome and subsequently degraded in lysosome, has been well formulated as one of the major mechanisms for mitochondrial quality control. To date, the PINK1-PRKN-dependent and receptors (including proteins and lipids)-dependent pathways have been characterized to determine the mitophagy in mammalian cells. The mitophagy is highly responsive to the dynamics of endogenous metabolites, including iron-, calcium-, glycolysis-TCA-, NAD+-, amino acids-, fatty acids-, and cAMP-associated metabolites. Herein, we summarize the recent advances toward the molecular details of mitophagy regulation in mammalian cells. We also highlight the key regulations of mammalian mitophagy by endogenous metabolites, shed new light on the bidirectional interplay between mitophagy and cellular metabolisms, with attempting to provide a perspective insight into the nutritional intervention of metabolic disorders with mitophagy deficit.Abbreviations: acetyl-CoA: acetyl-coenzyme A; ACO1: aconitase 1; ADCYs: adenylate cyclases; AMPK: AMP-activated protein kinase; ATM: ATM serine/threonine kinase; BCL2L1: BCL2 like 1; BCL2L13: BCL2 like 13; BNIP3: BCL2 interacting protein 3; BNIP3L: BCL2 interacting protein 3 like; Ca2+: calcium ion; CALCOCO2: calcium binding and coiled-coil domain 2; CANX: calnexin; CO: carbon monoxide; CYCS: cytochrome c, somatic; DFP: deferiprone; DNM1L: dynamin 1 like; ER: endoplasmic reticulum; FKBP8: FKBP prolyl isomerase 8; FOXO3: forkhead box O3; FTMT: ferritin mitochondrial; FUNDC1: FUN14 domain containing 1; GABA: γ-aminobutyric acid; GSH: glutathione; HIF1A: hypoxia inducible factor 1 subunit alpha; IMMT: inner membrane mitochondrial protein; IRP1: iron regulatory protein 1; ISC: iron-sulfur cluster; ITPR2: inositol 1,4,5-trisphosphate type 2 receptor; KMO: kynurenine 3-monooxygenase; LIR: LC3 interacting region; MAM: mitochondria-associated membrane; MAP1LC3: microtubule associated protein 1 light chain 3; MFNs: mitofusins; mitophagy: mitochondrial autophagy; mPTP: mitochondrial permeability transition pore; MTOR: mechanistic target of rapamycin kinase; NAD+: nicotinamide adenine dinucleotide; NAM: nicotinamide; NMN: nicotinamide mononucleotide; NO: nitric oxide; NPA: Niemann-Pick type A; NR: nicotinamide riboside; NR4A1: nuclear receptor subfamily 4 group A member 1; NRF1: nuclear respiratory factor 1; OPA1: OPA1 mitochondrial dynamin like GTPase; OPTN: optineurin; PARL: presenilin associated rhomboid like; PARPs: poly(ADP-ribose) polymerases; PC: phosphatidylcholine; PHB2: prohibitin 2; PINK1: PTEN induced kinase 1; PPARG: peroxisome proliferator activated receptor gamma; PPARGC1A: PPARG coactivator 1 alpha; PRKA: protein kinase AMP-activated; PRKDC: protein kinase, DNA-activated, catalytic subunit; PRKN: parkin RBR E3 ubiquitin protein ligase; RHOT: ras homolog family member T; ROS: reactive oxygen species; SIRTs: sirtuins; STK11: serine/threonine kinase 11; TCA: tricarboxylic acid; TP53: tumor protein p53; ULK1: unc-51 like autophagy activating kinase 1; VDAC1: voltage dependent anion channel 1.

The Life Cycle and in silico Elucidation of Non-structural Replicating Proteins of HCV Through a Pharmacoinformatics Approach.

BACKGROUND: Hepatitis C virus (HCV) is an enveloped and positive-stranded RNA virus that is a major causative agent of chronic liver diseases worldwide. HCV has become the main cause of liver transplantations and there is no effective drug for all hepatitis genotypes. Elucidation of the life cycle and non-structural proteins of HCV, involved in viral replication, are attractive targets for the development of antiviral drugs.. METHODS: In this work, pharmacoinformatics approaches coupled with docking analyses were applied on HCV non-structural proteins to identify the novel potential hits and HCV drugs. Molecular docking analyses were carried out on HCV-approved drugs, followed by the ligandbased pharmacophore generation to screen the antiviral libraries for novel potential hits. RESULTS: Virtual screening technique has top-ranked five novel compounds (ZINC00607900, ZINC03635748, ZINC03875543, ZINC04097464, and ZINC12503102) along with their least binding energies (-8.0 kcal/mol, -6.1 kcal/mol, -7.5 kcal/mol, -7.4 kcal/mol, and -7.3 kcal/mol, respectively) and stability with the non-structural proteins target. CONCLUSION: These promising hits exhibited better absorption and ADMET properties as compared to the selected drug molecules. These potential compounds extracted from in silico approach may be significant in drug design and development against Hepatitis and other liver diseases.

Epitope-based peptide vaccine design and elucidation of novel compounds against 3C like protein of SARS-CoV-2.

Coronaviruses (CoVs) are positive-stranded RNA viruses with short clubs on their edges. CoVs are pathogenic viruses that infect several animals and plant organisms, as well as humans (lethal respiratory dysfunctions). A noval strain of CoV has been reported and named as SARS-CoV-2. Numerous COVID-19 cases were being reported all over the World. COVID-19 and has a high mortality rate. In the present study, immunoinformatics techniques were utilized to predict the antigenic epitopes against 3C like protein. B-cell epitopes and Cytotoxic T-lymphocyte (CTL) were designed computationally against SARS-CoV-2. Multiple Sequence Alignment (MSA) of seven complete strains (HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, SARS-CoV, MERS-CoV, and SARS-CoV-2) was performed to elucidate the binding domain and interacting residues. MHC-I binding epitopes were evaluated by analyzing the binding affinity of the top-ranked peptides having HLA molecule. By utilizing the docked complexes of CTL epitopes with antigenic sites, the binding relationship and affinity of top-ranked predicted peptides with the MHC-I HLA protein were investigated. The molecular docking analyses were conducted on the ZINC database library and twelve compounds having least binding energy were scrutinized. In conclusion, twelve CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, SEDMLNPNY, LSQTGIAV, VLDMCASLK, LTQDHVDIL, TTLNDFNLV, CTSEDMLNP, TTITVNVLA, YNGSPSGVY, and SMQNCVLKL) were identified against SARS-CoV-2.

Epidemiological studies of sleep disorder in educational community of Pakistani population, its major risk factors and associated diseases.

Sleep is one of the most important functions of the life. The disturbance in sleep or quality of sleep leads to several dysfunctions of the human body. This study aimed to investigate the prevalence of sleep disorders, their possible risk factors and their association with other health problems. The data was collected from the educational community of the Pakistani population. The Insomnia Severity Index (ISI) was used to evaluate the insomnia and the sleep apnea was evaluated through a simple questionnaire method. The blood samples were collected to perform significant blood tests for clinical investigations. Current research revealed that the individuals in the educational community had poor sleep quality. A total of 1998 individuals from the educational community were surveyed, 1584 (79.28%) of whom had a sleep disorders, including insomnia (45.20%) and sleep apnea (34.08%). The measured onset of age for males and females was 30.35 years and 31.07 years respectively. The Clinical investigations showed that the sleep had significant impact on the hematology of the patients. Higher levels of serum uric acid and blood sugar were recorded with a sleep disorder. The individuals of the educational community were using the sleeping pills. The other associated diseases were mild tension, headaches, migraines, depression, diabetes, obesity, and myopia. The use of beverage, bad mood, medical condition, mental stress, disturbed circadian rhythms, workload and extra use of smartphone were major risk factors of sleep disorders. It was concluded that the insomnia was more prevalent than the sleep apnea. Furthermore, life changes events were directly linked with disturbance of sleep. Tension, depression, headaches, and migraine were more associated with sleep disorders than all other health issues.

Anti-aging Natural Compounds and their Role in the Regulation of Metabolic Pathways Leading to Longevity.

Aging is an unavoidable process, leading to cell senescence due to physiochemical changes in an organism. Anti-aging remedies have always been of great interest since ancient times. The purpose of anti-aging activities is to increase the life span and the quality of life. Anti-aging activities are primarily involved in the therapies of age-related disorders such as Parkinson's Disease (PD), Alzheimer's Disease (AD), cardiovascular diseases, cancer, and chronic obstructive pulmonary diseases. These diseases are triggered by multiple factors that are involved in numerous molecular pathways including telomere shortening, NF-κB pathway, adiponectin receptor pathway, insulin, and IGF signaling pathway, AMPK, mTOR, and mitochondria dysfunction. Natural products are known as effective molecules to delay the aging process through influencing metabolic pathways and thus ensure an extended lifespan. These natural compounds are being utilized in drug design and development through computational and high throughput techniques for effective pro-longevity drugs. A comprehensive study on natural compounds demonstrating their anti-aging activities along with databases of natural products for drug designing was executed and summarized in this review article.

Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening.

BACKGROUND: Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2. METHODS: In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis. RESULTS: The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant. CONCLUSION: Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

Determine the Potential Epitope Based Peptide Vaccine Against Novel SARS-CoV-2 Targeting Structural Proteins Using Immunoinformatics Approaches.

Coronaviruses (CoVs) belong to the Coronaviridae-family. The genus Beta-coronaviruses, are enveloped positive strand RNA viruses with club-like spikes at the surface with a unique replication process and a large RNA genome (∼25 kb). CoVs are known as one of the major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Recently, a new strain of coronavirus has been identified and named as SARS-CoV-2. A large number of COVID-19 (disease caused by SARS-CoV-2) cases are being diagnosed all over the World especially in China (Wuhan). COVID-19 showed high mortality rate exponentially, however, not even a single effective cure is being introduced yet against COVID-19. In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against COVID-19 for the development of a coronavirus peptide vaccine. Cytotoxic T-lymphocyte (CTL) and B-cell epitopes were predicted for SARS-CoV-2 coronavirus structural proteins (Spikes, Membrane, Envelope, and Nucleocapsid). The docking complexes of the top 10 epitopes having antigenic sites were analyzed led by binding affinity and binding interactional analyses of top ranked predicted peptides with the MHC-I HLA molecule. The predicted peptides may have potential to be used as peptide vaccine against COVID-19.