In silico identification of vaccine candidates against Klebsiella oxytoca

Klebsiella oxytoca causes several diseases in immunocompromised as well as healthy individuals. Increasing resistance to a number of antibiotics makes treatment options limited. Prevention using vaccine could be an important solution to get rid of infections caused by Klebsiella oxytoca. In recent time, genome based approaches have contributed significantly in vaccine development. Our aim was to identify the most conserved and immunogenic antigens that can be considered as potential vaccine candidates. KEGG database was used to find out pathways unique to the bacteria. Subcellular localization of the protein sequences taken from the selected 36 pathways were predicted using PSORTb v3.0.2 and CELLO v2.5. Prediction of B cell epitope and the probability of the antigenicity were evaluated by using IEDB and Vaxijen respectively. BLASTp was done to find out the similarity of the selected proteins with the human proteome. Proteins failing to comply with the set parameters were filtered at each step. Finally, we identified 6 surface exposed proteins as potential vaccine candidates against Klebsiella oxytoca.     

Designing of precise vaccine construct against visceral leishmaniasis through predicted epitope ensemble: A contemporary approach

Visceral leishmaniasis (VL) caused by Leishmania donovani is a fatal parasitic disease affecting primarily the poor population in endemic countries. Increasing number of deaths as well as resistant to existing drugs necessitates the development of an effective vaccine for successful treatment of VL. The present study employed a combinatorial approach for designing monomer vaccine construct against L. donovani by applying forecasted B- and T- cell epitopes from 4 genome derived antigenic proteins having secretory signal peptides and glycophosphatidylinositol (GPI) anchors with ≤ 1 transmembrane helix. The forecasted population coverage of chosen T cell epitope ensemble (combined HLA class I and II) cover 99.14 % of world-wide human population. The predicted 3D structure of vaccine constructs (VC1/VC2) were modeled using homology modeling approach and docked to innate immune receptors TLR-2 and TLR-4 with respective docking energies −1231.4/−910.3 and −1119.4/−1476 kcal/mol. Overall, the aforementioned designed vaccine constructs were found appropriate for including in self-assembly protein nanoparticles (SAPN) for further study in developing cutting-edge precision vaccine against VL in short duration with cost-effective manner.     

Identification of novel vaccine candidates against carbapenem resistant Klebsiella pneumoniae: A systematic reverse proteomic approach

Klebsiella pneumoniae is declared as antibiotic resistant by WHO, with the critical urgency of developing novel antimicrobial therapeutics as drug resistance is the second most dangerous threat after terrorism. Besides many attempts still, there is no effective vaccine available against K. pneumoniae. By utilizing all the available proteomic data we prioritized the novel proteins ideal for vaccine development using bioinformatics tools and techniques. Among the huge data, eight proteins passed all the barriers and were considered ideal candidates for vaccine development. These include: copper silver efflux system outer membrane protein (CusC), outer membrane porin protein (OmpN), Fe⁺⁺ enterobactin transporter substrate binding protein (fepB), zinc transporter substrate binding protein (ZnuA), ribonuclease HI, tellurite resistant methyltransferase (the B), and two uncharacterized hypothetical proteins (WP_002918223 and WP_002892366). These proteins were also subjected to epitope analysis and were found best for developing subunit vaccine against K. pneumoniae. The study shows that the potential vaccine targets are sufficiently efficient being virulent, of outer membranous origin and can be proposed for the DNA third-generation vaccines development that would help to cope up infections caused by multidrug-resistant K. pneumoniae.     

Novel multi-epitope protein containing conserved epitopes from different Leishmania species as potential vaccine candidate: Integrated immunoinformatics and molecular dynamics approach

Leishmaniosis, caused by intracellular parasites of the genus Leishmania, has become a serious public health problem around the world, and for which there are currently extensive limitations. In this work, a theoretical model was proposed for the development of a multi-epitope vaccine. The protein GP63 of the parasite was selected for epitopes prediction, due to its important biological role for the infection process and abundance. IEDB tools were used to determine epitopes B and T in Leishmania braziliensis; besides, other conserved epitopes in three species were selected. To improve immunogenicity, 50S ribosomal protein L7 / L12 (ID: P9WHE3) was used as a domain of adjuvant in the assembly process. The folding arrangement of the vaccine was obtained through homologous modeling multi-template with MODELLER v9.21, and a Ramachandran plot analysis was done. Furthermore, physicochemical properties were described with the ProtParam tool and secondary structure prediction combining GOR-IV and SOPMA tools. Finally, a molecular dynamics simulation (50 ns) was performed to establish flexibility and conformational changes. The analysis of the results indicates high conservancy in the epitopes predicted among the four species. Moreover, Ramachandran plot, physicochemical parameters, and secondary structure prediction suggest a stable conformation of the vaccine, after a minimum conformational change that was evaluated with the free energy landscape. The conformational change does not drive any substantial change for epitope exposition on the surface. The vaccine proposed could be tested experimentally to guide new approaches in the development of pan-vaccines; vaccines with regions conserved in multiple species.     

Designing a potent L1 protein-based HPV peptide vaccine: A bioinformatics approach

BackgroundOncogenic human papilloma viruses (HPV) are the cause of various types of cancer, specifically cervical cancer. L1 protein is the main protein of HPV capsid which targeted in many vaccine-producing attempts. However, they have not enough coverage on the various high risk HPV types. Therefore, having a low cost potent HPV vaccine to protect against all members of the α-papillomaviridea family will be promising. In this study, L1 protein-based peptide vaccine was designed using immunoinformatics methods which provides physicochemical properties such as stability in room temperature, potential of antigenicity, non-allergic properties and no requirement with eukaryotic host system.ResultsThe designed vaccine has two HPV conserved epitopes with lengths 18 and 27 amino acids in all members of α-papillomaviridea. These peptides promote humoral and cellular immunity and INF-γ responses. In order to ensure strong induction of immune responses, Flagellin, a Toll like receptor 5(TLR-5) agonist, and a short synthetic toll like receptor 4 (TLR-4) agonist were also joined to the epitopes. Structure of the designed- vaccine was validated using Rampage and ERRAT and a high quality 3D structure of the vaccine protein was provided. Docking studies demonstrated an appropriate and stable interaction between the vaccine and TLR-5.ConclusionsThe vaccine is expected to have a high quality structure and suitable properties including high stability, solubility and a high potential to be expressed in E.coli. High potentiality of the vaccine in inducing humoral and cellular immune responses, may be considered as an anti-tumor vaccine.     

A structure-based design approach for the identification of novel inhibitors: application to an alanine racemase

We report a new structure-based strategy for the identification of novel inhibitors. This approach has been applied to Bacillus stearothermophilus alanine racemase (AlaR), an enzyme implicated in the biosynthesis of the bacterial cell wall. The enzyme catalyzes the racemization of l- and d-alanine using pyridoxal 5-phosphate (PLP) as a cofactor. The restriction of AlaR to bacteria and some fungi and the absolute requirement for d-alanine in peptidoglycan biosynthesis make alanine racemase a suitable target for drug design. Unfortunately, known inhibitors of alanine racemase are not specific and inhibit the activity of other PLP-dependent enzymes, leading to neurological and other side effects.This article describes the development of a receptor-based pharmacophore model for AlaR, taking into account receptor flexibility (i.e. a `dynamic' pharmacophore model). In order to accomplish this, molecular dynamics (MD) simulations were performed on the full AlaR dimer from Bacillus stearothermophilus (PDB entry, 1sft) with a d-alanine molecule in one active site and the non-covalent inhibitor, propionate, in the second active site of this homodimer. The basic strategy followed in this study was to utilize conformations of the protein obtained during MD simulations to generate a dynamic pharmacophore model using the property mapping capability of the LigBuilder program. Compounds from the Available Chemicals Directory that fit the pharmacophore model were identified and have been submitted for experimental testing.The approach described here can be used as a valuable tool for the design of novel inhibitors of other biomolecular targets.     

In silico epitope identification of unique multidrug resistance proteins from Salmonella Typhi for vaccine development

Typhoid fever is a multisystemic illness caused by Salmonella enterica serovars Typhi and is resistant to most antibiotics and drugs. The resistance is conferred through multidrug resistance (MDR) proteins, which efflux most antibiotics and other drugs. We predicted potential candidate B-cell and T-cell epitopes using bio- and immune-informatics tools in the 11 MDR proteins - EmrA, EmrB, EmrD, MdtA, MdtB, MdtC, MdtG, MdtH, MdtK, MdtL and TolC. The antigenic potential of the MDR proteins was calculated using VaxiJen server. The B-cell and T-cell epitopes of the MDR proteins were predicted using BCPred and ProPredI and ProPred respectively. The binding affinities of the predicted T-cell epitopes were estimated using T-epitope designer and MHCPred tools. 10, 7, 5, 12, 14, 21, 26, 3, 3 and 3 B-cell epitopes were identified in EmrA, EmrB, EmrD, TolC, MdtA, MdtB, MdtC, MdtG, MdtH and MdtL respectively. We predicted 9 T-cell epitopes - YVSRRAVQP (EmrA), FGVANAISI (EmrB), MVNSQVKQA and YQGGMVNSQ (TolC), WDRTNSHKL (MdtA), FLRNIPTAI (MdtB), YVEQLGVTG (MdtG), VKWMYAIEA (MdtH) and LAHTNTVTL (MdtL) capable of eliciting both humoral and adaptive immune responses. These T-cell epitopes specifically bind to HLA alleles - DRB1*0101 and DRB1*0401. This is the first report of epitope prediction in the MDR proteins of S. Typhi. Taken together, these results indicate the MDR proteins – EmrA, MdtA and TolC are the most suitable vaccine candidates for S. Typhi. The findings of our study on the MDR proteins prove to be useful in the development of peptide-based vaccine for the prevention and/or treatment of typhoid fever.     

A novel ATR-FTIR approach for characterisation and identification of ex situ immobilised species.

We demonstrate a novel method to analyse ex situ prepared protein chips by attenuated total reflection Fourier IR spectroscopy (ATR-FTIR), which circumvents tedious functionalisation steps of internal reflection elements (IREs), and simultaneously allows for complementary measurements by other analytical techniques. This concept is proven by utilising immobilised metal affinity capture (IMAC) chips containing about 10 mum thick films of copolymers coated with nitrilotriacetic acid (NTA) groups, which originally was manufactured for surface enhanced laser desorption ionisation (SELDI) spectrometry. Three immobilisation steps were analysed by ATR-FTIR spectroscopy: 1) NTA complexation with nickel(II) ions 2) binding of two histidine (His)-tagged synthetic peptides of 25 (25-His6) and 48 (48-His6) amino acids to the NTA-groups and 3) attachment of a ligand, mesyl amide, to the surface-bound 48-His6. Despite interference from H(2)O, both amide I and II were well resolved. Utilising peptide adsorption in the thick copolymer matrix yields a high saturation peptide concentration of approximately 100 mg mL(-1) and a dissociation constant of 116+/-11 muM, as determined by a detailed analysis of the Langmuir adsorption isotherm. The mesyl amide ligand was directly seen in the raw ATR-FTIR spectrum with specific peaks in the fingerprint region at 1172 and 1350 cm(-1). Several aspects of the fine structure of the amide I band of the peptide were analysed: influences from secondary structure, amino side chains and competing contamination product. We believe that this approach has great potential as a stand-alone or complementary analytical tool for determination of the chemical composition of functionalised surfaces. We emphasise further that with this approach no chemical treatment of IREs is needed; the chips can be regenerated and reused, and applied in other experimental set-ups.     

A novel approach for protein identification from complex cell proteome using modified peptide mass fingerprinting algorithm

A unique peptide based search algorithm for identification of protein mixture using PMF is proposed. The proposed search algorithm utilizes binary search and heapsort programs to generate frequency chart depicting the unique peptides corresponding to all proteins in a proteome. The use of binary search program significantly reduces the time for frequency chart preparation to ～2 s for a proteome comprising ～23 000 proteins. The algorithm was applied to a three‐protein mixture identification, host cell protein (HCP) analysis, and a simulation‐generated data set. It was found that the algorithm could identify at least one unique peptide of a protein even in the presence of fourfold higher concentration of another protein. In addition, two HCPs that are known to be difficult to remove were missed by MS/MS approach and were exclusively identified using the presented algorithm. Thus, the proposed algorithm when used along with standard proteomic approaches present avenues for enhanced protein identification efficiency, particularly for applications such as HCP analysis in biopharmaceutical research, where identification of low‐abundance proteins are generally not achieved due to dynamic range limitations between the target product and HCPs.     

DART-Orbitrap MS: a novel mass spectrometric approach for the identification of phenolic compounds in propolis

This is the first direct analysis in real-time mass spectrometry (DART-MS) study of propolis and a first study on the analysis of bee products using high-resolution DART-MS (DART-HRMS). Identification of flavonoids and other phenolic compounds in propolis using direct analysis in real-time coupling with Orbitrap mass spectrometry (DART-Orbitrap MS) was performed in the negative ion mode for minimizing the matrix effects, while the positive ion mode was used for the confirmation of selected compounds. Possible elemental formulae were suggested for marker components. The duration of one sample analysis by DART-MS analysis lasted ca. 30 s, and all benefits of high-resolution mass spectrometry were used upon data processing using the coupling of DART with the Orbitrap mass spectrometer. The possibility for scanning analysis of dried propolis extract spots on a planar porous surface was investigated in the heated gas flow of the DART ion source with adjustable angle. As an independent method, the approach of scanning analysis is of high interest and of future potential for confirmation of the results obtained from liquid sample analysis. Scanning analysis is highly promising for further development in the bioanalytical field due to the convenience of the storage and transportation of dried sample spots.     

A novel approach for dimension reduction of microarray

This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA + ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA + ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA + ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA + ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA + ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA + ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods.     

A unique approach to the synthesis of a dengue vaccine and the novel tetrasaccharide that results

An approach to the development of a dengue vaccine by synthesizing the hexasaccharide epitope on the viral surface is examined. The stereochemical and structural challenges include the synthesis of a β-mannoside bond. Synthesis of this bond is approached via a trisaccharide analogue portion of the epitope. A novel tetrasaccharide with a mannose–mannose anomeric linkage results in the course of the synthetic attempts.     

A novel approach for the synthesis of aryl amides

A novel and highly efficient approach for the synthesis of aryl amides in high yields by the reaction of carboxylic acids and isocyanides in methanol at ambient temperature is reported.     

A novel approach for identification and characterization of glycoproteins using a hybrid linear ion trap/FT-ICR mass spectrometer

Combining source collision-induced dissociation (CID) and tandem mass spectral acquisition in a pseudo-MS(3) experiment using a linear ion trap results in a highly selective and sensitive approach to identifying glycopeptide elution from a protein digest. The increased sensitivity is partially attributed to the nonselective nature of source CID, which allows simultaneous activation of all charge states and coeluting glycoforms generating greater ion abundance for the mass-to-charge (m/z) 204 and/or 366 oxonium ions. Unlike source CID alone, a pseudo-MS(3) approach adds selectivity while improving sensitivity by eliminating chemical noise during the tandem mass spectral acquisition of the oxonium ions in the linear ion trap. Performing the experiments in the hybrid linear ion trap/Fourier transform-ion cyclotron resonance (FT-ICR) enables subsequent high-resolution/high-mass accuracy full-scan mass spectra (MS) and parallel acquisition of MS/MS in the linear ion trap to be completed in 2 s directly following the pseudo-MS(3) scan to collate identification and characterization of glycopeptides in one experimental scan cycle. Analysis of bovine fetuin digest using the combined pseudo-MS(3), high-resolution MS, and data-dependent MS/MS events resulted in identification of four N-linked and two O-linked glycopeptides without enzymatic cleavage of the sugar moiety or release of the sialic acids before analysis. In addition, over 95% of the total protein sequence was identified in one analytical run.     

A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes

In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.     

A novel approach for introducing particulate matter into thermal plasmas: The triple-cathode arc

A coalesced high-intensity dc discharge is maintained between three cathodes and a single anode, stabilized by using resistors on each cathode leg. Jets of plasma gas are produced from either the cathode area or the anode area of the device. Cathode jets are generated by the self-induced pumping at the cathode tips and augmented by central gas injection. Arc voltage-current characteristics show classical convection-stabilized arc behavior. Anode heat transfer rates may be substantially increased by central gas injection toward the anode. Temperature fields in the coalesced, axially symmetric portion of the arc are determined spectrometrically and compared to those of a classical single-cathode free-burning arc.     

A novel correlation approach for prediction of natural gas compressibility factor

Gas compressibility factor (z-Factor) is one of the most important parameters in upstream and downstream calculations of petroleum industries. The importance of z-Factor cannot be overemphasized in oil and gas engineering calculations. The experimental measurements, Equations of State (EoS) and empirical correlations are the most common sources of z-Factor calculations. There are more than twenty correlations available with two variables for calculating the z-Factor from fitting in an EoS or just through fitting techniques. However, these correlations are too complex, which require initial value and more complicated and longer computations or have magnitude error. The purpose of this study is to develop a new accurate correlation to rapidly estimate z-Factor. Result of this correlation is compared with large scale of database and experimental data also. Proposed correlation has 1.660 of Absolute Percent Relative Error (E_(ABS)) versus Standing and Katz chart and has also 3.221 of E_(ABS) versus experimental data. The output of this correlation can be directly assumed or be used as an initial value of other implicit correlations. This correlation is valid for gas coefficient of isothermal compressibility (c_g) calculations also.