Bioinformatics based structural characterization of glucose dehydrogenase (gdh) gene and growth promoting activity of Leclercia sp. QAU-66

Investigating the anti-cancer compounds from Calliandra harrisii for precision medicine in pancreatic cancer via in-silico drug design and GC-MS analysis

Pancreatic cancer is a fatal illness caused by mutations in multiple genes. Pancreatic cancer damages the organ that helps in digestion, resulting in symptoms including fatigue, bloating, and nausea. The use of medicinal plants has been crucial in the treatment of numerous disorders. The medicinal plant Calliandra Harrisi has been widely exploited for its possibilities in biology and medicine. The current study aimed to assess the biopotential of biologically active substances against pancreatic cancer. The GC-MS data of these phytochemicals from Calliandra Harrisi were further subjected to computational approaches with pancreatic cancer genes to evaluate their potential as therapeutic candidates. Molecular docking analysis revealed that N-[Carboxymethyl] maleamic acid is the leading molecule responsible for protein denaturation inhibition, having the highest binding affinity of 6.8 kJ/mol among all other compounds with KRAS inflammatory proteins. Furthermore, ADMET analysis and Lipinski's rule validation were also performed revealing its higher absorption in the gastrointestinal tract. The results of the hepatotoxicity test demonstrated that phytochemicals are non-toxic, safe to use, and do not cause necrosis, fibrosis, or vacuolar degeneration even at excessive levels. Calliandra Harrisi has phytoconstituents that have a variety of pharmacological uses in consideration.

Analyzing VEGFA/VEGFR1 Interaction: Application of the Resonant Recognition Model-Stockwell Transform Method to Explore Potential Therapeutics for Angiogenesis-Related Diseases

The interaction between vascular endothelial growth factor A (VEGFA) and VEGF receptor 1(VEGFR1) is a central focus for drug development in pathological angiogenesis, where aberrant angiogenesis underlies various anomalies necessitating therapeutic intervention. Identifying hotspots of these proteins is crucial for developing new therapeutics. Although machine learning techniques have succeeded significantly in prediction tasks, they struggle to pinpoint hotspots linked to angiogenic activity accurately. This study involves the collection of diverse VEGFA and VEGFR1 protein sequences from various species via the UniProt database. Electron-ion interaction Potential (EIIP) values were assigned to individual amino acids and transformed into frequency-domain representations using discrete Fast Fourier Transform (FFT). A consensus spectrum emerged by consolidating FFT data from multiple sequences, unveiling specific characteristic frequencies. Subsequently, the Stockwell Transform (ST) was employed to yield the hotspots. The Resonant Recognition Model (RRM) identified a characteristic frequency of 0.128007 with an associated wavelength of 1570 nm and RRM-ST identified hotspots for VEGFA (Human 36, 46, 48, 67, 71, 74, 82, 86, 89, 93) and VEGFR1 (Human 224, 259, 263, 290, 807, 841, 877, 881, 885, 892, 894, 909, 913, 1018, 1022, 1026, 1043). These findings were cross-validated by Hotspots Wizard 3.0 webserver and Protein Data Bank (PDB). The study proposes using a 1570 nm wavelength for photo bio modulation to boost VEGFA/VEGFR1 interaction in the condition that is needed. It also aims to reduce VEGFA/VEGFR2 interaction, limiting harmful angiogenesis in conditions like diabetic retinopathy. Also, the identified hotspots assist in designing agonistic or antagonistic peptides tailored to specific medical requirements with abnormal angiogenesis.

An immunoinformatic approach for developing a multi-epitope subunit vaccine against Monkeypox virus

An in-silico approach was implemented to develop a multi-epitope subunit vaccine construct against the recent outbreak of the Monkeypox virus. The contribution of 10 different antigenic proteins based on their antigenicity led to the selection of 10 HTL, 9 CTL, and 6 BCL epitopes. The construct was further investigated for its allergenicity, antigenicity, and physio-chemical properties using servers such as AllerTOP and Allergen FP, VaxiJen and ANTIGENPro, and ProtParam respectively. The secondary structure of the vaccine was predicted using the SOPMA server followed by I-TASSER for the 3D structure. After refinement and validation of structural stability of the modelled vaccine, a molecular docking assay was implemented to study the interaction of the known TLR4 receptor with that of the constructed vaccine using the ClusPro server. The docked vaccine and TLR4 receptor were studied using the molecular dynamics (MD) simulation to validate the stability of the complex. After codon optimization the cDNA was constructed and in-silico cloning of the vaccine construct was carried out. The vaccine was also subjected to computational immune assay which predicted a powerful immune response against the Monkeypox virus validating that the developed multi-epitope vaccine construct can be a potent vaccine candidate.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00220-5.

Computation-Guided Rational Design of Cysteine-Less Protein Variants in Engineered hCGL

The engineered human cystathionine-γ-lyase (hCGL) resulting in enhanced activity toward both cysteine and cystine unveils a potential robust antitumor activity. However, the presence of cysteine residues has the potential to induce oligomerization or incorrect disulfide bonding, which may decrease the bioavailability of biopharmaceuticals. Through a meticulous design process targeting the cysteine residues within engineered hCGL, a set of potential beneficial mutants were obtained by virtual screening employing Rosetta and ABACUS. Experimental measurements have revealed that most of the mutants showed increased activity toward both substrates l-Cys and CSSC. Furthermore, mutants C109V and C229D demonstrated Tm value increases of 8.2 and 1.8 °C, respectively. After an 80 min incubation at 60 °C, mutant C229D still maintained high residual activity. Unexpectedly, mutant C109V, displaying activity approximately 2-fold higher than the activity of wild type (WT) for both substrates, showed disappointing instability in plasma, which suggests that computational design still requires further consideration. Analysis of their structure and molecular dynamics (MD) simulation revealed the impact of hydrophobic interaction, hydrogen bonds, and near-attack conformation (NAC) stability on activity and stability. This study acquired information about mutants that exhibit enhanced activity or thermal resistance and serve as valuable guidance for subsequent specific cysteine modifications.

Prediction of interactomic hub genes in PBMC cells in type 2 diabetes mellitus, dyslipidemia, and periodontitis

BACKGROUND AND OBJECTIVE

In recent years, the complex interplay between systemic health and oral well-being has emerged as a focal point for researchers and healthcare practitioners. Among the several important connections, the convergence of Type 2 Diabetes Mellitus (T2DM), dyslipidemia, chronic periodontitis, and peripheral blood mononuclear cells (PBMCs) is a remarkable example. These components collectively contribute to a network of interactions that extends beyond their domains, underscoring the intricate nature of human health. In the current study, bioinformatics analysis was utilized to predict the interactomic hub genes involved in type 2 diabetes mellitus (T2DM), dyslipidemia, and periodontitis and their relationships to peripheral blood mononuclear cells (PBMC) by machine learning algorithms.

MATERIALS AND METHODS

Gene Expression Omnibus datasets were utilized to identify the genes linked to type 2 diabetes mellitus(T2DM), dyslipidemia, and Periodontitis (GSE156993).Gene Ontology (G.O.) Enrichr, Genemania, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for analysis for identification and functionalities of hub genes. The expression of hub D.E.G.s was confirmed, and an orange machine learning tool was used to predict the hub genes.

RESULT

The decision tree, AdaBoost, and Random Forest had an A.U.C. of 0.982, 1.000, and 0.991 in the R.O.C. curve. The AdaBoost model showed an accuracy of (1.000). The findings imply that the AdaBoost model showed a good predictive value and may support the clinical evaluation and assist in accurately detecting periodontitis associated with T2DM and dyslipidemia. Moreover, the genes with p-value < 0.05 and A.U.C.>0.90, which showed excellent predictive value, were thus considered hub genes.

CONCLUSION

The hub genes and the D.E.G.s identified in the present study contribute immensely to the fundamentals of the molecular mechanisms occurring in the PBMC associated with the progression of periodontitis in the presence of T2DM and dyslipidemia. They may be considered potential biomarkers and offer novel therapeutic strategies for chronic inflammatory diseases.

In Silico Prediction of Functional SNPs Interrupting Antioxidant Defense Genes in Relation to COVID-19 Progression

The excessive production of reactive oxygen species and weakening of antioxidant defense system play a pivotal role in the pathogenesis of different diseases. Extensive differences observed among individuals in terms of affliction with cancer, cardiovascular disorders, diabetes, bacterial, and viral infections, as well as response to treatments can be partly due to their genomic variations. In this work, we attempted to predict the effect of SNPs of the key genes of antioxidant defense system on their structure, function, and expression in relation to COVID-19 pathogenesis using in silico tools. In addition, the effect of SNPs on the target site binding efficiency of SNPs was investigated as a factor with potential to change drug response or susceptibility to COVID-19. According to the predicted results, only six missense SNPs with minor allele frequency (MAF) ≥ 0.1 in the coding region of genes GPX7, GPX8, TXNRD2, GLRX5, and GLRX were able to strongly affect their structure and function. Our results predicted that 39 SNPs with MAF ≥ 0.1 led to the generation or destruction of miRNA-binding sites on target antioxidant genes from GPX, PRDX, GLRX, TXN, and SOD families. The results obtained from comparing the expression profiles of mild vs. severe COVID-19 patients using GEO2R demonstrated a significant change in the expression of approximately 250 miRNAs. The binding efficiency of 21 of these miRNAs was changed due to the elimination or generation of target sites in these genes. Altogether, this study reveals the fundamental role of the SNPs of antioxidant defense genes in COVID-19 progression and susceptibility of individuals to this virus. In addition, different responses of COVID-19 patients to antioxidant defense system enhancement drugs may be due to presence of these SNPs in different individuals.

An Aedes-Anopheles Vaccine Candidate Supplemented with BCG Epitopes Against the Aedes and Anopheles Genera to Overcome Hypersensitivity to Mosquito Bites

BACKGROUND

Skeeter syndrome is a severe local allergic response to mosquito bites that is accompanied by considerable inflammation and, in some cases, a systemic response like fever. People with the syndrome develop serious allergies, ranging from rashes to anaphylaxis or shock. The few available studies on mosquito venom immunotherapy have utilized whole-body preparations and small sample sizes. Still, owing to their little success, vaccination remains a promising alternative as well as a permanent solution for infections like Skeeter's.

METHODS

This study, therefore, illustrated the construction of an epitope-based vaccine candidate against Skeeter Syndrome using established immunoinformatic techniques. We selected three species of mosquitoes, Anopheles melas, Anopheles funestus, and Aedes aegypti, to derive salivary antigens usually found in mosquito bites. Our construct was also supplemented with bacterial epitopes known to elicit a strong TH1 response and suppress TH2 stimulation that is predicted to reduce hypersensitivity against the bites.

RESULTS

A quality factor of 98.9496, instability index of 38.55, aliphatic index of 79.42, solubility of 0.934747, and GRAVY score of -0.02 indicated the structural (tertiary and secondary) stability, thermostability, solubility, and hydrophilicity of the construct, respectively. The designed Aedes-Anopheles vaccine (AAV) candidate was predicted to be flexible and less prone to deformability with an eigenvalue of 1.5911e-9 and perfected the human immune response against Skeeter (hypersensitivity) and many mosquito-associated diseases as we noted the production of 30,000 Th1 cells per mm3 with little (insignificant production of Th2 cells. The designed vaccine also revealed stable interactions with the pattern recognition receptors of the host. The TLR2/vaccine complex interacted with a free energy of - 1069.2 kcal/mol with 26 interactions, whereas the NLRP3/vaccine complex interacted with a free energy of - 1081.2 kcal/mol with 16 molecular interactions.

CONCLUSION

Although being a pure in-silico study, the in-depth analysis performed herein speaks volumes of the potency of the designed vaccine candidate predicting that the proposition can withstand rigorous in-vitro and in-vivo clinical trials and may proceed to become the first preventative immunotherapy against mosquito bite allergy.

In silico elucidation for the identification of potential phytochemical against ACE-II inhibitors

CONTEXT

The present study aims to investigate the therapeutic potential of phytocompounds derived from Annona reticulata leaves for the treatment of hypertension, utilizing computational methodologies. Gaining a comprehensive understanding of the molecular interactions between neophytadiene and γ-sitosterol holds significant importance in the advancement of innovative therapeutic approaches. This study aims to examine the inhibitory effects of neophytadiene and γ-sitosterol using molecular docking and dynamics simulations. Additionally, we will evaluate their stability and predict their drug-like properties as well as their ADME/toxicity profiles. Neophytadiene and γ-sitosterol have a substantial binding affinity with 1O8A, as shown by the docking study. The stability of the complexes was confirmed through molecular dynamics simulations, while distinct clusters were identified using PCA. These findings suggest the presence of potential stabilizers. The drug-likeness and ADME/toxicity predictions revealed positive characteristics, such as efficient absorption rates, limited distribution volume and non-hazardous profiles. The neophytadiene and γ-sitosterol exhibit potential as hypertension medication options. Computational investigations reveal that these compounds exhibit high affinity for binding, stability and favourable pharmacokinetic properties. The results of this study lay the groundwork for additional experimental verification and highlight the promising prospects of utilizing natural compounds in the field of pharmaceutical research.

METHODS

Target proteins (1O8A) were used to perform molecular docking with representative molecules. Stability, conformational changes and binding energies were assessed through molecular dynamics simulations lasting 100 ns. Principal component analysis (PCA) was utilized to analyze molecular dynamics (MD) simulation data, to identify potential compounds that could stabilize the main protease. The safety and pharmacokinetic profiles of the compounds were evaluated through drug-likeness and ADME/toxicity predictions.

Evaluation of the inhibitory potential of bioactive compounds against SARS-CoV-2 by in silico approach

CONTEXT

The COVID-19 (coronavirus disease 19) pandemic brought on by the SARS-CoV-2 outbreak (severe acute respiratory syndrome coronavirus 2) has stimulated the exploration of various available chemical compounds that could be used to treat the infection. This has driven numerous researchers to investigate the antiviral potential of several bioactive compounds from medicinal plants due to their reduced adverse effects compared to chemicals. Some of the bioactive compounds used in folklore treatment strategies are reported as effective inhibitors against the proliferative and infective cycles of SARS-CoV-2. The secondary metabolites from plants are generally used to treat various diseases due to their intact medicinal properties. The present study analyzes the inhibitory potential of phytochemicals from medicinal plants like Sphaeranthus indicus, Lantana camara, and Nelumbo nucifera against SARS-CoV-2 by molecular docking.

METHODS

Ten druggable protein targets from SARS-CoV-2 are docked against the phytochemicals from the selected medicinal plants. The phytocompounds astragalin, isoquercetin, and 5-hydroxy-7-methoxy-6-c-glycosy flavone were found to have lower binding energy depicting their inhibitive potential compared with the reported inhibitors that are used in the treatment of SARS-CoV-2 infection. The phytocompounds found to have the least binding energy were selected for further analyses. To assess the compounds' potential as drugs, their ADMET characteristics were also examined. Sphaeranthus indicus, Lantana camara, and Nelumbo nucifera six possible compounds were separately screened for ADME and toxicity characteristics; then, the results were analyzed. To assess the impact of the phytocompound binding on the dynamics of SARS-CoV-2 ribonuclease protein NSP15, microsecond-level all atomistic molecular dynamics simulations were performed, and their dynamics were analyzed. Microsecond-level molecular dynamics simulations of both the ligands complexed with NSP15 revealed that the ligand induces allosteric effects on NSP15, which could lead to destabilization of NSP15 hexameric interface and loss of RNA binding. The low binding energy exhibited by the phytochemicals from Lantana camera, Sphaeranthus indicus, and Nelumbo nucifera against the protein targets of SARS-CoV-2 showed inhibitory potential by the selected molecules. Their predicted interference of the enzymes involved in the molecular mechanisms aiding the proliferation of SARS-CoV-2 indicated the inhibitive ability of the phytochemicals.

AI-driven covalent drug design strategies targeting main protease (mpro) against SARS-CoV-2: structural insights and molecular mechanisms

The emergence of new SARS-CoV-2 variants has raised concerns about the effectiveness of COVID-19 vaccines. To address this challenge, small-molecule antivirals have been proposed as a crucial therapeutic option. Among potential targets for anti-COVID-19 therapy, the main protease (Mpro) of SARS-CoV-2 is important due to its essential role in the virus's life cycle and high conservation. The substrate-binding region of the core proteases of various coronaviruses, including SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV), could be used for the generation of new protease inhibitors. Various drug discovery methods have employed a diverse range of strategies, targeting both monomeric and dimeric forms, including drug repurposing, integrating virtual screening with high-throughput screening (HTS), and structure-based drug design, each demonstrating varying levels of efficiency. Covalent inhibitors, such as Nirmatrelvir and MG-101, showcase robust and high-affinity binding to Mpro, exhibiting stable interactions confirmed by molecular docking studies. Development of effective antiviral drugs is imperative to address potential pandemic situations. This review explores recent advances in the search for Mpro inhibitors and the application of artificial intelligence (AI) in drug design. AI leverages vast datasets and advanced algorithms to streamline the design and identification of promising Mpro inhibitors. AI-driven drug discovery methods, including molecular docking, predictive modeling, and structure-based drug repurposing, are at the forefront of identifying potential candidates for effective antiviral therapy. In a time when COVID-19 potentially threat global health, the quest for potent antiviral solutions targeting Mpro could be critical for inhibiting the virus.Communicated by Ramaswamy H. Sarma.

Characterization, modeling, and anticancer activity of L.arginase production from marine Bacillus licheniformis OF2

BACKGROUND

L-arginase, is a powerful anticancer that hydrolyzes L-arginine to L-ornithine and urea. This enzyme is widely distributed and expressed in organisms like plants, fungi, however very scarce from bacteria. Our study is based on isolating, purifying, and screening the marine bacteria that can produce arginase.

RESULTS

The highest arginase producing bacteria will be identified by using microbiological and molecular biology methods as Bacillus licheniformis OF2. Characterization of arginase is the objective of this study. The activity of enzyme was screened, and estimated beside partial sequencing of arginase gene was analyzed. In silico homology modeling was applied to generate the protein's 3D structure, and COACH and COFACTOR were applied to determine the protein's binding sites and biological annotations based on the I-TASSER structure prediction. The purified enzyme was undergone an in vitro anticancer test.

CONCLUSIONS

L-arginase demonstrated more strong anti-cancer cells with an IC50 of 21.4 ug/ml in a dose-dependent manner. L-arginase underwent another investigation for its impact on the caspase 7 and BCL2 family of proteins (BCL2, Bax, and Bax/Bcl2). Through cell arrest in the G1/S phase, L-arginase signals the apoptotic cascade, which is supported by a flow cytometry analysis of cell cycle phases.

Identification of most damaging nsSNPs in human CCR6 gene: In silico analyses

Single nucleotide polymorphisms in CCR6 (C-C chemokine receptor type 6) gene have been found to be the possible cause of many diseases like rheumatoid arthritis, psoriasis, lupus nephritis and systemic sclerosis and other autoimmune diseases. Therefore, identification of structurally and functionally important polymorphisms in CCR6 is important in order to study its potential malfunctioning and discovering therapeutic targets. Several bioinformatics tools were used to identify most damaging nsSNPs that might be vital for CCR6 structure and function. The in silico tools included PROVEAN, SIFT, SNP&GO and PolyPhen2 followed by I-Mutant MutPred and ConSurf. Phyre2 and I-TASSER were used for protein 3-D Modelling while gene-gene interaction was predicted by STRING and GeneMANIA. Our study suggested that three nsSNPs rs1376162684, rs751102128 and rs1185426631 are the most damaging in CCR6 gene while 7 missense SNPs rs1438637216, rs139697820, rs768420505, rs1282264186, rs1394647982, rs769360638 and rs1263402382 are found to revert into stop codons. Prediction of post-transcriptional modifications highlighted the significance of rs1376162684 because it effected potential phosphorylation site. Gene-gene interactions showed relation of CCR6 with other genes depicting its importance in several pathways and co-expressions. In future, studying diseases related to CCR6 should include investigation of these 10 nsSNPs. Being the first of its type, this study also proposes future perspectives that will help in precision medicines. For such purposes, CCR6 proteins from patients of autoimmune diseases should be explored. Animal models can also be of significance find out the effects of CCR6 in diseases.

Characterization of phosphate solubilizing bacteria isolated from heavy metal contaminated soils and their potential for lead immobilization

Phosphate solubilizing bacteria (PSB) were isolated from heavy metal contaminated soils and their potentials for lead (Pb) immobilization in soil were studied in this paper. 53 PSB strains were isolated and their minimum inhibitory concentrations (MICs) for lead were determined. The results showed that strains B3, B4, B10, F2-1 and L1-5 had relatively high lead resistant capabilities with MICs ranged from 0.5 to 8 mM, and were identified as species of Leclercia adecarboxylata and Pseudomonas putida by 16S rRNA. The most efficient PSB strains could solubilize 200 mg L^-1 of P, and phosphate solubilizing capacity of those PSBs was related to the concentrations of organic acids, acid phosphatase activity and pH. Furthermore, the growth pattern of L1-5 strain with the presence of Pb(NO₃)₂ was observed and a mechanism of biomineralization of lead ions in bacteria biomass was determined by FT-IR and XRD analyses. Results showed that high concentration of lead can inhibit the growth of PSBs, and L1-5 isolate can transform lead ions into lead hydroxyapatite and pyromorphite. The PSBs, which possessing the properties of immobilizing lead through phosphate solubilization and biomineralization could be exploited for bioremediation of Pb polluted soils in future.

Identification and in silico analysis of functional SNPs of human TAGAP protein: A comprehensive study

Genetic polymorphisms in TAGAP gene have been associated with many diseases including rheumatoid arthritis, multiple sclerosis and other autoimmune disorders. Identifying functional SNPs in such disease associated genes is an uphill task hence before planning larger population study, it is better to scrutinize putative functional SNPs. In this study we used various computational approaches to identify nsSNPs which are deleterious to the structure and/or function of TAGAP protein that might be causing these diseases. Computational analysis was performed by five different in silico tools including SIFT, PROVEAN, PolyPhen-2, PhD-SNP and SNPs&GO. The study concludes that mutations of Glycine → Glutamic Acid at position 120, Glycine → Tryptophan at position 141 and Valine → Methionine at position 151 are major mutations in native TAGAP protein which might contribute to its malfunction and ultimately causing disease. The study also proposed 3D structures of native TAGAP protein and its three mutants. Future studies should consider these nsSNPs as main target mutations in various diseases involving TAGAP malfunction. This is the first comprehensive study, where TAGAP gene variants were analyzed using in silico tools hence will be of great help while considering large scale studies and also in developing precision medicines for cure of diseases related to these polymorphisms. Furthermore, animal models of various autoimmune diseases and having these mutations might be of help in exploring their precise roles.

In silicofunctional and tumor suppressor role of hypothetical protein PCNXL2 with regulation of the Notch signaling pathway

Since the last decade, various genome sequencing projects have led to the accumulation of an enormous set of genomic data; however, numerous protein-coding genes still need to be functionally characterized. These gene products are called “hypothetical proteins”. The hypothetical protein pecanex-like protein 2 Homo sapiens (PCNXL2) is found to be mutated in colorectal carcinoma with microsatellite instability; therefore, annotation of the function of PCNXL2 in tumorigenesis is very important. In the present study, bioinformatics analysis of PCNXL2 was performed at the molecular level to assess its role in the progression of cancer for designing new anti-cancer drugs. The retrieved sequence of PCNXL2 was functionally and structurally characterized through the web tools Pfam, Batch CD (conserved domain) search, ExPASy, COACH and I-TASSER directed for pathway analysis and design to explore the intercellular interactions of PCNXL2 involved in cancer development. The present study has shown that PCNXL2 encodes multi-pass transmembrane proteins whose tumor suppressor function may involve regulating Notch signaling by transporting protons across the membrane to provide suitable membrane potential for γ secretase function, which may liberate the Notch intracellular domain NICD from the receptor to inside the cell. Furthermore, domain A of PCNXL2 may exhibit nuclear transport activity of NICD from the cytoplasm to the nucleus through interaction with a nuclear localization signal that may act as an activator for Notch signaling in the nucleus. Conclusively, the tumor suppressor role of PCNXL2 by regulation of the Notch signaling pathway and its functional and structural characteristics are important findings. However, further studies are required to validate the putative role of PCNXL2 as a cancer biomarker in cancer development.

Since the last decade, various genome sequencing projects have led to the accumulation of an enormous set of genomic data; however, numerous protein-coding genes still need to be functionally characterized.

Structural and functional annotation of hypothetical proteins of human adenovirus: prioritizing the novel drug targets

OBJECTIVE

Human adenoviruses are small double stranded DNA viruses that provoke vast array of human diseases. Next generation sequencing techniques increase genomic data of HAdV rapidly, which increase their serotypes. The complete genome sequence of human adenovirus shows that it contains large amount of proteins with unknown cellular or biochemical function, known as hypothetical proteins. Hence, it is indispensable to functionally and structurally annotate these proteins to get better understanding of the novel drug targets. The purpose was the characterization of 38 randomly retrieved hypothetical proteins through determination of their physiochemical properties, subcellular localization, function, structure and ligand binding sites using various sequence and structure based bioinformatics tools.

RESULTS

Function of six hypothetical proteins P03269, P03261, P03263, Q83127, Q1L4D7 and I6LEV1 were predicted confidently and then used further for structure analysis. We found that these proteins may act as DNA terminal protein, DNA polymerase, DNA binding protein, adenovirus E3 region protein CR1 and adenoviral protein L1. Functional and structural annotation leading to detection of binding sites by means of docking analysis can indicate potential target for therapeutics to defeat adenoviral infection.

Complete Genome Sequence Analysis of Enterobacter sp. SA187, a Plant Multi-Stress Tolerance Promoting Endophytic Bacterium

Enterobacter sp. SA187 is an endophytic bacterium that has been isolated from root nodules of the indigenous desert plant Indigofera argentea. SA187 could survive in the rhizosphere as well as in association with different plant species, and was able to provide abiotic stress tolerance to Arabidopsis thaliana. The genome sequence of SA187 was obtained by using Pacific BioScience (PacBio) single-molecule sequencing technology, with average coverage of 275X. The genome of SA187 consists of one single 4,429,597 bp chromosome, with an average 56% GC content and 4,347 predicted protein coding DNA sequences (CDS), 153 ncRNA, 7 rRNA, and 84 tRNA. Functional analysis of the SA187 genome revealed a large number of genes involved in uptake and exchange of nutrients, chemotaxis, mobilization and plant colonization. A high number of genes were also found to be involved in survival, defense against oxidative stress and production of antimicrobial compounds and toxins. Moreover, different metabolic pathways were identified that potentially contribute to plant growth promotion. The information encoded in the genome of SA187 reveals the characteristics of a dualistic lifestyle of a bacterium that can adapt to different environments and promote the growth of plants. This information provides a better understanding of the mechanisms involved in plant-microbe interaction and could be further exploited to develop SA187 as a biological agent to improve agricultural practices in marginal and arid lands.