Network medicine in ovarian cancer: topological properties to drug discovery.

Network medicine provides network theoretical tools, methods and properties to study underlying laws governing human interactome to identify disease states and disease complexity leading to drug discovery. Within this framework, we investigated the topological properties of ovarian cancer network (OCN) and the roles of hubs to understand OCN organization to address disease states and complexity. The OCN constructed from the experimentally verified genes exhibits fractal nature in the topological properties with deeply rooted functional communities indicating self-organizing behavior. The network properties at all levels of organization obey one parameter scaling law which lacks centrality lethality rule. We showed that $\langle k\rangle $ can be taken as a scaling parameter, where, power law exponent can be estimated from the ratio of network diameters. The betweenness centrality $C_B$ shows two distinct behaviors one shown by high degree hubs and the other by segregated low degree nodes. The $C_B$ power law exponent is found to connect the exponents of distributions of high and low degree nodes. OCN showed the absence of rich-club formation which leads to the missing of a number of attractors in the network causing formation of weakly tied diverse functional modules to keep optimal network efficiency. In OCN, provincial and connector hubs, which includes identified key regulators, take major responsibility to keep the OCN integrity and organization. Further, most of the key regulators are found to be over expressed and positively correlated with immune infiltrates. Finally, few potential drugs are identified related to the key regulators.

A Comprehensive Metagenome Study Identifies Distinct Biological Pathways in Asthma Patients: An In-Silico Approach.

Asthma is a multifactorial disease with phenotypes and several clinical and pathophysiological characteristics. Besides innate and adaptive immune responses, the gut microbiome generates Treg cells, mediating the allergic response to environmental factors and exposure to allergens. Because of the complexity of asthma, microbiome analysis and other precision medicine methods are now widely regarded as essential elements of efficient disease therapy. An in-silico pipeline enables the comparative taxonomic profiling of 16S rRNA metagenomic profiles of 20 asthmatic patients and 15 healthy controls utilizing QIIME2. Further, PICRUSt supports downstream gene enrichment and pathway analysis, inferring the enriched pathways in a diseased state. A significant abundance of the phylum Proteobacteria, Sutterella, and Megamonas is identified in asthma patients and a diminished genus Akkermansia. Nasal samples reveal a high relative abundance of Mycoplasma in the nasal samples. Further, differential functional profiling identifies the metabolic pathways related to cofactors and amino acids, secondary metabolism, and signaling pathways. These findings support that a combination of bacterial communities is involved in mediating the responses involved in chronic respiratory conditions like asthma by exerting their influence on various metabolic pathways.

Systematic analysis to identify novel disease indications and plausible potential chemical leads of glutamate ionotropic receptor NMDA type subunit 1, GRIN1.

Schizophrenia is a mental illness affecting the normal lifestyle of adults and early adolescents incurring major symptoms as jumbled speech, involvement in everyday activities eventually got reduced, patients always struggle with attention and memory, reason being both the genetic and environmental factors responsible for altered brain chemistry and structure, resulting in schizophrenia and associated orphan diseases. The network biology describes the interactions among genes/proteins encoding molecular mechanisms of biological processes, development, and diseases. Besides, all the molecular networks, protein-protein Interaction Networks have been significant in distinguishing the pathogenesis of diseases and thereby drug discovery. The present meta-analysis prioritizes novel disease indications viz. rare and orphan diseases associated with target Glutamate Ionotropic Receptor NMDA Type Subunit 1, GRIN1 using text mining knowledge-based tools. Furthermore, ZINC database was virtually screened, and binding conformation of selected compounds was performed and resulted in the identification of Narciclasine (ZINC04097652) and Alvespimycin (ZINC73138787) as potential inhibitors. Furthermore, docked complexes were subjected to MD simulation studies which suggests that the identified leads could be a better potential drug to recuperate schizophrenia.

Inhibitory mechanism of an antifungal drug, caspofungin against amyloid ß peptide aggregation: Repurposing via neuroinformatics and an experimental approach.

The multifactorial neurological condition called Alzheimer's disease (AD) primarily affects elderly individuals. Despite the calamitous consequences of AD, curative strategies for a regimen to apply remain inadequate as several factors contribute to AD etiology. Drug repurposing is an advance strategy prior to drug discovery as various effective drugs perform through alteration of multiple targets, and the present "poly-pharmacology" can be a curative approach to complex disorders. AD's multifactorial behavior actively encourages the hypothesis for a drug design approach focused on drug repurposing. In this study, we discovered that an antifungal drug, Caspofungin (CAS) is a potent Aß aggregation inhibitor that displays significantly reduced toxicity associated with AD. Drug reprofiling and REMD simulations demonstrated that CAS interacts with the ß-sheet section, known as Aß amyloid fibrils hotspot. CAS leads to destabilization of ß-sheet and, conclusively, in its devaluation. Later, in vitro experiments were acquired in which the fibrillar volume was reduced for CAS-treated Aß peptide. For the first time ever, this study has determined an antifungal agent as the Aß amyloid aggregation's potent inhibitor. Several efficient sequence-reliant potent inhibitors can be developed in future against the amyloid aggregation for different amyloid peptide by the processing and conformational optimization of CAS.

Oxidative Products of Curcumin Rather Than Curcumin Bind to Helicobacter Pylori Virulence Factor VacA and Are Required to Inhibit Its Vacuolation Activity.

Curcumin is a hydrophobic polyphenol derived from turmeric with potent anti-oxidant, anti-microbial, anti-inflammatory and anti-carcinogenic effects. Curcumin is degraded into various derivatives under in vitro and in vivo conditions, and it appears that its degradation may be responsible for the pharmacological effects of curcumin. The primary risk factor for the cause of gastric cancer is Helicobacter pylori (H. pylori). A virulence factor vacuolating cytotoxic A (VacA) is secreted by H. pylori as a 88 kDa monomer (p88), which can be fragmented into a 33 kDa N-terminal domain (p33) and a 55 kDa C-terminal domain (p55). Recently it has been reported that curcumin oxidation is required to inhibit the activity of another major H.pylori toxin CagA. We performed molecular docking of curcumin and its oxidative derivatives with p33 and p55 domains of VacA. Further, we have examined the effect of the oxidation of curcumin on the vacuolation activity of VacA protein. We observed the binding of curcumin to the p55 domain of VacA at five different sites with moderate binding affinities. Curcumin did not bind to p33 domain of VacA. Remarkably, cyclobutyl cyclopentadione and dihydroxy cyclopentadione, which are oxidized products of curcumin, showed a higher binding affinity with VacA protein at all sites except one as compared to parent curcumin itself. However, cyclobutyl cyclopentadione showed a significant binding affinity for the active site 5 of the p55 protein. Active site five (312-422) of p55 domain of VacA plays a crucial role in VacA-mediated vacuole formation. Invitro experiments showed that curcumin inhibited the vacuolation activity of H. pylori in human gastric cell line AGS cells whereas acetyl and diacetyl curcumin, which cannot be oxidized, failed to inhibit the vacuolation in AGS cells after H. pylori infection. Here our data showed that oxidation is essential for the activity of curcumin in inhibiting the vacuolation activity of H. pylori. Synthesis of these oxidized curcumin derivatives could potentially provide new therapeutic drug molecules for inhibiting H. pylori-mediated pathogenesis.

Comparative assessment of the therapeutic drug targets of C. botulinum ATCC 3502 and C. difficile str. 630 using in silico subtractive proteomics approach.

Growing antimicrobial resistance of the pathogens against multiple drugs posed a serious threat to the human health worldwide. This fueled the need of identifying the novel therapeutic targets that can be used for developing new class of the drugs. Recently, there is a substantial rise in the rate of Clostridium infections as well as in the emergence of virulent and antibiotic resistant strains. Hence, there is an urgent need for the identification of potential therapeutic targets and the development of new drugs for the treatment and prevention of Clostridium infections. In the present study, a combinatorial approach involving systems biology and comparative genomics strategy was tested against Clostridium botulinum ATCC 3502 and Clostridium difficile str. 630 pathogens, to render potential therapeutic target at qualitative and quantitative level. This resulted in the identification of five common (present in both the pathogens, 34 in C. botulinum ATCC 3502 and 42 in C. difficile str. 630) drug targets followed by virtual screening-based identification of potential inhibitors employing molecular docking and molecular dynamics simulations. The identified targets will provide a solid platform for the designing of novel wide-spectrum lead compounds capable of inhibiting their catalytic activities against multidrug-resistant Clostridium in the near future.

Pyrazinamide drug resistance in RpsA mutant (∆438A) of Mycobacterium tuberculosis: Dynamics of essential motions and free-energy landscape analysis.

Pyrazinamide is an essential first-line antitubercular drug which plays pivotal role in tuberculosis treatment. It is a prodrug that requires amide hydrolysis by mycobacterial pyrazinamidase enzyme for conversion into pyrazinoic acid (POA). POA is known to target ribosomal protein S1 (RpsA), aspartate decarboxylase (PanD), and some other mycobacterial proteins. Spontaneous chromosomal mutations in RpsA have been reported for phenotypic resistance against pyrazinamide. We have constructed and validated 3D models of the native and Δ438A mutant form of RpsA protein. RpsA protein variants were then docked to POA and long range molecular dynamics simulations were carried out. Per residue binding free-energy calculations, free-energy landscape analysis, and essential dynamics analysis were performed to outline the mechanism underlying the high-level PZA resistance conferred by the most frequently occurring deletion mutant of RpsA. Our study revealed the conformational modulation of POA binding site due to the disruptive collective modes of motions and increased conformational flexibility in the mutant than the native form. Residue wise MMPBSA decomposition and protein-drug interaction pattern revealed the difference of energetically favorable binding site in the wild-type (WT) protein in comparison with the mutant. Analysis of size and shape of minimal energy landscape area delineated higher stability of the WT complex than the mutant form. Our study provides mechanistic insights into pyrazinamide resistance in Δ438A RpsA mutant, and the results arising out of this study will pave way for design of novel and effective inhibitors targeting the resistant strains of Mycobacterium tuberculosis.

Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach.

BACKGROUND: Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance. METHODS: The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network. RESULTS: The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest. CONCLUSIONS: The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.

Simulation Based Investigation of Deleterious nsSNPs in ATXN2 Gene and Its Structural Consequence Toward Spinocerebellar Ataxia.

Spinocerebellar degeneration, termed as ataxia is a neurological disorder of central nervous system, characterized by limb in-coordination and a progressive gait. The patient also demonstrates specific symptoms of muscle weakness, slurring of speech, and decreased vibration senses. Expansion of polyglutamine trinucleotide (CAG) within ATXN2 gene with 35 or more repeats, results in spinocerebellar ataxia type-2. Protein ataxin-2 coded by ATXN2 gene has been reported to have a crucial role in translation of the genetic information through sequestering the histone acetyl transferases (HAT) resulting in a state of hypo-acetylation. In the present study, we have evaluated the outcome for 122 non synonymous single nucleotide polymorphisms (nsSNPs) reported within ATXN2 gene through computational tools such as SIFT, PolyPhen 2.0, PANTHER, I-mutant 2.0, Phd-SNP, Pmut, MutPred. The apo and mutant (L305V and Q339L) form of structures for the ataxin-2 protein were modeled for gaining insights toward 3D spatial arrangement. Further, molecular dynamics simulations and structural analysis were performed to observe the brunt of disease associated nsSNPs toward the strength and secondary properties of ataxin-2 protein structure. Our results showed that, L305V is a highly deleterious and disease causing point substitution. Analysis based on RMSD, RMSF, Rg, SASA, number of hydrogen bonds (NH bonds), covariance matrix trace, projection analysis for eigen vector demonstrated a significant instability and conformation along with rise in mutant flexibility values in comparison to the apo form of ataxin-2 protein. The study provides a blue print of computational methodologies to examine the ataxin-blend SNPs. J. Cell. Biochem. 119: 499-510, 2018. © 2017 Wiley Periodicals, Inc.

Inhibition of C298S mutant of human aldose reductase for antidiabetic applications: Evidence from in silico elementary mode analysis of biological network model.

Human aldose reductase (hAR) is the key enzyme in sorbitol pathway of glucose utilization and is implicated in the etiology of secondary complications of diabetes, such as, cardiovascular complications, neuropathy, nephropathy, retinopathy, and cataract genesis. It reduces glucose to sorbitol in the presence of NADPH and the major cause of diabetes complications could be the change in the osmotic pressure due to the accumulation of sorbitol. An activated form of hAR (activated hAR or ahAR) poses a potential obstacle in the development of diabetes drugs as hAR-inhibitors are ineffective against ahAR. The therapeutic efficacy of such drugs is compromised when a large fraction of the enzyme (hAR) undergoes conversion to the activated ahAR form as has been observed in the diabetic tissues. In the present study, attempts have been made to employ systems biology strategies to identify the elementary nodes of human polyol metabolic pathway, responsible for normal metabolic states, followed by the identification of natural potent inhibitors of the activated form of hAR represented by the mutant C298S for possible antidiabetic applications. Quantum Mechanical Molecular Mechanical docking strategy was used to determine the probable inhibitors of ahAR. Rosmarinic acid was found as the most potent natural ahAR inhibitor and warrants for experimental validation in the near future.

Aspartate-ß-semialdeyhyde dehydrogenase as a potential therapeutic target of Mycobacterium tuberculosis H37Rv: Evidence from in silico elementary mode analysis of biological network model.

The emergence of multi-drug resistant strains and co-occurrence of tuberculosis with HIV creates a major burden to the human health globally. Failure of primary antibacterial therapy necessitates the identification of new mycobacterial drugs. In this study, a comprehensive analysis involving bottom-up systems biology approach was applied wherein we have identified potential therapeutic targets of Mycobacterium tuberculosis infections. Our study prioritized M. tuberculosis therapeutic targets (aspartate-ß-semialdeyhde dehydrogenase [ASD], dihydrodipicolinate reductase and diaminopimelate decarboxylase) based on flux and elementary mode analysis using direct mathematical modeling of the relevant metabolic pathways. Molecular docking and simulation studies of the priority target (ie, ASD) revealed the therapeutic potential of the selected natural products (Huperzine A, Rosmarinic acid, and Curcumin) based ASD inhibitors. The study highlights the crucial role of systems biology in conjunction with molecular interaction (docking) for probing novel leads against an increasingly resistant pathogen, M. tuberculousis.

In silico identification of molecular mimics involved in the pathogenesis of Clostridium botulinum ATCC 3502 strain.

Bacterial pathogens invade and disrupt the host defense system by means of protein sequences structurally similar at global and local level both. The sharing of homologous sequences between the host and the pathogenic bacteria mediates the infection and defines the concept of molecular mimicry. In this study, various computational approaches were employed to elucidate the pathogenicity of Clostridium botulinum ATCC 3502 at genome-wide level. Genome-wide study revealed that the pathogen mimics the host (Homo sapiens) and unraveled the complex pathogenic pathway of causing infection. The comparative 'omics' approaches helped in selective screening of 'molecular mimicry' candidates followed by the qualitative assessment of the virulence potential and functional enrichment. Overall, this study provides a deep insight into the emergence and surveillance of multidrug resistant C. botulinum ATCC 3502 caused infections. This is the very first report identifying C. botulinum ATCC 3502 proteome enriched similarities to the human host proteins and resulted in the identification of 20 potential mimicry candidates, which were further characterized qualitatively by sub-cellular organization prediction and functional annotation. This study will provide a variety of avenues for future studies related to infectious agents, host-pathogen interactions and the evolution of pathogenesis process.

In silico identification of immunodominant B-cell and T-cell epitopes of non-structural proteins of Usutu Virus.

Usutu Virus (USUV; flavivirus) is a re-emerging pathogen invading the territories of European countries, Asia, and Africa. It is a mosquito-borne zoonotic virus with a bi-directional transmission route from animal to human and vice versa, and causes neurological disorders such as meningoencephalitis in bats, Homo sapiens, birds and horses. Due to limited availability of information about USUV and its deleterious effects on neural cells causing neurologic impairments, it becomes imperative to study this virus in detail to equip ourselves with a solution beforehand. The current study aims to identify immunodominant peptides that could be exploited in future for designing global peptide vaccine for combating the infections caused by USUV. In this study, an immunoinformatics approach was applied to evaluate the immunogenicity of 7 non-structural proteins and determined 64 continuous B-cell epitopes, numerous probable discontinuous B-cell epitopes, 64 MHC Class-I binders, 126 MHC class-II binders and 52 promiscuous binders with a maximum population coverage of 98.55%(MHC Class-I binder ofYP_164815.1 NS4a) and 81.81% (MHC Class-II binders of YP_164812.1 NS2a, YP_164813.1 NS2b, YP_164814.1 NS3, YP_164817.1 NS4b, YP_164818.1 NS5). Further, studies involving experimental validation of these predicted epitopes is warranted to ensure the potential of B-cells and T-cells stimulation for their effective use as vaccine candidates, and as diagnostic agents against USUV.

Mechanistic Principles Behind Molecular Mechanism of Rifampicin Resistance in Mutant RNA Polymerase Beta Subunit of Mycobacterium tuberculosis.

Evolution of drug-resistant Mycobacterium strains threatens the TB treatment and control programs globally. Rifampicin (RIF) is an important first line antitubercular drug. Resistance to Rifampicin is caused mainly by mutations in its target RNA polymerase beta subunit protein (RpoB). RpoB contains a Rifampicin resistance determining region (RRDR) and has several potent sites for mutations. In this study, we have investigated mutations of a single site (H451) to eight different amino acids, involved in RIF resistance. Long-term molecular dynamics simulations were performed on wild type (WT) and mutant protein structures and various structural analysis were carried out to elucidate the dynamic behavior of WT and mutant forms. Essential dynamics uncovered the difference in conformational flexibility and collective modes of motions between WT and mutants. MMPBSA calculations and interaction pattern analysis revealed the binding site relocation in some mutants. This study presents an exhaustive analysis of RIF binding to the WT and mutant RpoB and clearly highlights structural mechanism for differences in stable binding of Rifampicin with WT than the mutant targets. J. Cell. Biochem. 118: 4594-4606, 2017. © 2017 Wiley Periodicals, Inc.

Investigating the potential of Juglans regia phytoconstituents for the treatment of cervical cancer utilizing network biology and molecular docking approach.

The fourth most frequent type of cancer in women and the leading cause of mortality for females worldwide is cervical cancer. Traditionally, medicinal plants have been utilized to treat various illnesses and ailments. The molecular docking method is used in the current study to look into the phytoconstituents of Juglans regia's possible anticancer effects on cervical cancer target proteins. This work uses the microarray dataset analysis of GSE63678 from the NCBI Gene Expression Omnibus database to find differentially expressed genes. Furthermore, protein-protein interactions of differentially expressed genes were constructed using network biology techniques. The top five hub genes (IGF1, FGF2, ESR1, MYL9, and MYH11) are then determined by computing topological parameters with Cytohubba. In addition, molecular docking research was performed on Juglans regia phytocompounds that were extracted from the IMPPAT database versus hub genes that had been identified. Utilizing molecular dynamics, simulation confirmed that prioritized docked complexes with low binding energies were stable.

Drug repurposing against galectin-3 using simulation-based studies.

The protein galectin, which binds to carbohydrates and is involved in a number of therapeutic processes including cell proliferation, inflammatory responses, apoptosis, etc., has been discovered as a potential therapeutic target. Galectin-3 is a stable biomarker that exhibits both increased and decreased expression in a variety of illnesses and infections, regardless of sex, age, or body mass index. The goal of the current study is to apply bioinformatics techniques to examine the possibility of cardiovascular medications to inhibit Galectin-3-related biological activities. Unsupervised clustering techniques, molecular docking, and guided molecular dynamics (MD) simulation were used to create a computational pipeline that was used to screen potential chemical compounds from a library of chemical compounds with related molecular fingerprints. Utilizing input factors such as gene expression, mode of action, and chemical descriptors, clustering enables prioritization of medicinal molecules. Twenty-four compounds were screened and repurposed against Galectin-3 utilizing molecular docking as part of the cluster-facilitated virtual screening technique. The polar interactions that Arg144, Glu184, Arg162, His158, and Asn174 have with Bufalin, Cymarin, and Ouabalin have the highest binding affinities, according to docking studies. Studies using MD simulations confirm the tested compounds' ability to inhibit Galectin-3. Galactin-3 targeted experimental and in vivo animal model-based validation studies using Bufalin, Cymarin, and Ouabalin are also necessary.Communicated by Ramaswamy H. Sarma.

Anti Mtb Medicinal Plants Database (AMMPDB): A curated database of Indian anti-tubercular medicinal plants.

The utilization of medicinal plants for their therapeutic properties has long been a key component of Indian culture. Unique medicinal characteristics can be found in the phytochemicals that are extracted from these plants. Globally, tuberculosis (TB) burden and management are challenged due to the emergence of new resistant strains of Mycobacterium tuberculosis (Mtb). This highlights the importance of new drug molecules from diverse sources as well as their innovative management options. In this context, the present study formulated an Anti Mtb medicinal plant database (AMMPDB Ver. 1.1), a manually curated database of native Indian medicinal plants that reported anti-tubercular (anti-TB) activities and their potential therapeutic phytochemicals. This is the first-ever freely accessible digital repository. The current version of the database provides users, with information regarding 118 native Indian anti-tubercular medicinal plants and their 3374 phytochemicals. The database provides the following information: Taxonomical ID, botanical description, vernacular names, conservation status, geographical distribution maps, IC-50 value, phytochemical details which include - name, Compound ID, Synonyms, location in plant part, 2D, 3D structures (as per the availability), and their medicinal uses reported in the literature. The tools section of the database is equipped with sequentially catalogued and hyperlinked open-access tools utilized for computational drug designing. A case study has been incorporated under the contributors section to validate the tools section and the phytochemicals of the database. AMMPDB Ver 1.1 will be serviceable to research in computational drug designing and discovery with effectiveness and ease. Database URL: https://www.ammpdb.com/.

Proteomic Characterization and Target Identification Against Streptococcus mutans Under Bacitracin Stress Conditions Using LC-MS and Subtractive Proteomics.

The aim of the present study, is to identify potential targets against the highly pathogenic bacteria Streptococcus mutans that causes dental caries as well as the deadly infection of endocarditis. The powerful and highly sensitive technique of liquid chromatography-mass spectrometry (LC-MS/MS) identified 321 proteins of S. mutans when grown under stressful conditions induced by the antibiotic bacitracin. These 321 proteins were subjected to the insilico method of subtractive proteomics to screen out potential targets by utilizing different analyses like CD-HIT, non-homologous sequence screening, KEGG pathway, essentiality screening, gut-flora non-homology, and codon usage analysis. A database of essential proteins was employed to find sequence homology of non-paralogous proteins to determine proteins which are essential for bacterial survival. Cellular localization analysis of the selected proteins was done to localize them inside the cell along with physico-chemical characterization and druggability analysis. Using computational tools, 22 proteins out of 321, that are functionally distinguishable from their human counterparts and passed the criterion of a potential therapeutic candidate were identified. The selected proteins comprise central energy metabolic proteins, virulence factors, proteins of the sortase family, and essentiality factors. The presented analyses identified proteins of the sortase family, which appear as key therapeutic targets against caries infection. These proteins regulate a number of virulence factors, thus can be simultaneously inhibited to obstruct multiple virulence pathways.

Multiple epitope-based vaccine prediction against SARS-CoV-2 spike glycoprotein.

The global emergence of novel coronavirus disease and its rapid global expansion over a short span of time require effective countermeasures to combat it. Development of a specific vaccine can induce an optimal antibody response, thus providing immunity against it. Our study proposes a detailed and comprehensive immunoinformatic approach that can be applied to the currently available coronavirus protein data in the online server for vaccine candidate development. We have identified the receptor binding domain (RBD) of structural spike protein (S1) as a potential target for immunity against COVID- 19 infection. Epitope prediction illustrated cytotoxic T-cell epitopes, helper T-cell epitopes, and B-cell epitopes associated with the target protein. These were joined through specific linkers along with adjuvant beta-defensin located at the N-terminal to create a multi epitope subunit vaccine (MESV). The specificity in the binding of the devised vaccine candidate to the TLR-3 immune cell receptor was evaluated via molecular docking interaction studies. Good docking score combined with robust interactions in the binding cavity certified the stringency of the engineered vaccine. Molecular dynamics simulation data showed minimal variation of the root-mean square deviations (RMSDs) and root-mean-square fluctuations (RMSFs) which confirmed the interaction stability. These results obtained from various in-silico experiments indicate the potency of this vaccine candidate as a probable therapeutic agent against COVID-19. Vaccination strategies targeting conserved epitope-based immune response would be beneficial in providing cross protection across beta-coronaviruses, and such vaccines would be resistant to the ever-evolving viruses.Communicated by Ramaswamy H. Sarma.

Molecular detection and cloning of thermostable hemolysin gene from Aeromonas hydrophila.

Aeromonas hydrophila is a major bacterial pathogen associated with hemorrhagic septicemia in aquatic and terrestrial animals including humans. There is an urgent need to develop molecular and immunological assays for rapid, specific and sensitive diagnosis. A new set of primers has been designed for detection of thermostable hemolysin (TH) gene (645 bp) from A. hydrophila, and sensitivity limit for detection of TH gene was 5 pg. The TH gene was cloned, sequenced and analyzed. The G+C content was 68.06%; and phylogeny was constructed using TH protein sequences which had significant homology with those for thermostable and other hemolysins present in several bacterial pathogens. In addition, we have predicted the four and eight T-cell epitopes for MHC class I and II alleles, respectively. These results provide new insight for TH protein containing antigenic epitopes that can be used in immunoassays and also designing of thermostable vaccines.