An In Silico Based Approach Towards the Characterization with Feature Identification and Analogy Modeling of Human Osteoclast Protein

International Journal of Peptide Research and Therapeutics - The osteoclast is a kind of bone cell that splits down the bone tissue. The osteoclast is responsible for bone disease. So, the...     

Designing a Fusion Protein Vaccine Against HCV: An In Silico Approach

Hepatitis C virus (HCV) infection is a major global issue that leads to serious liver disease such as chronic liver inflammation and hepatocellular carcinoma. At present, no approved vaccine is available for control or treatment of HCV infection. Therefore, the development of an efficient vaccine against HCV is an urgent need. Today, designing an effective vaccine against hepatitis C is one of the outmost propriety for researchers. Fusion protein vaccines containing the immunogen proteins and adjuvant molecules are able to stimulate both humoral and cellular responses that are crucial for eradicating HCV infection. Herein, in silico design of fusion forms of vaccine candidates against HCV, including flagellin (fliC) from Pseudomonas aeruginosa and NS5B antigen (NT300) from HCV was performed. First, two forms of fusion protein (NT300-fliC and fliC-NT300) were designed and analyzed using different bioinformatics tools. For this aim, the Iterative threading assembly refinement (I-TASSER) server was used for modeling the fusion forms of protein; namely, NT300-fliC and fliC-NT300, then the high-rank 3D model of fusion protein was selected, subsequently various physico-chemical, and structural parameters were examined bioinformatically. After the selection of the best construct (fliC-NT300), the interaction of flagellin part of vaccine with toll-like receptor 5 (TLR5) was evaluated via docking studies. Our results represented that based on data obtained from various servers, and the docking analyses of two constructs, fliC-NT300 fusion form showed better results than NT300-fliC. For this reason, the fliC-NT300 form was selected for further evaluations. In sum, structural and immunological computational studies showed that the fliC-NT300 can be introduced as a prophylactic or therapeutic candidate vaccine against the HCV, after the efficacy of that was confirmed via in vitro and in vivo assays.

     

Interaction of Catechu Dye with DNA: Spectroscopic and In Silico Approach

Catechin, a yellow colored molecule obtained from the wood of Acacia catechu was analyzed for its interaction with synthetic DNA duplexes using spectroscopic analysis. UV-Visible spectroscopic analysis revealed the non-intercalative binding mode. Fourier Transform Infrared spectroscopy (FTIR) analysis expose chemical shift indicated by various vibrational stretches and an increase in the intensity of base stacking was observed by Circular Dichroism (CD), respectively. This inference was further confirmed through nuclear staining technique and also in electrophoretic technique; the dye quenches the fluorescent intensity of ethidium bromide. The result of fluorescence spectroscopy was in concordance with the electrophoretic technique. In addition, the spectroscopic results were in accordance with the molecular docking studies of specific catechin compound from the catechu dye with CT-DNA. This kind of site specificity is a gain in the medicinal field as the drug can be DNA targeted for cancer therapeutics. The present work reveals that catechu dye has a noteworthy application in the field of medical bioscience.     

An Approach to In Silico Dissection of Bacterial Intelligence Through Selective Genomic Tools

All the genetic potential and the intelligence a bacteria can showcase in a given environment are embedded in its genome. In this study, we have presented systematic guidelines to understand a bacterial genome with the relevant set of in silico tools using a novel bacteria as an example. This study presents a multi-dimensional approach from genome annotation to tracing genes and their network of metabolism operating in an organism. It also shows how the sequence can be used to mine the enzymes and construction of its 3-dimensional structure so that its functional behavior can be predicted and compared. The discriminating algorithm allows analysis of the promoter region and provides the insight in the regulation of genes in spite of the similarity in its sequences. The ecological niche specific bacterial behavior and adapted altered physiology can be understood through the presence of secondary metabolite, antibiotic resistance genes, and viral genes; and it helps in the valorization of genetic information for developing new biological application/processes. This study provides an in silico work plan and necessary steps for genome analysis of novel bacteria without any rigorous wet lab experiments.     

An In Silico Agent-Based Model Demonstrates Reelin Function in Directing Lamination of Neurons during Cortical Development

The characteristic six-layered appearance of the neocortex arises from the correct positioning of pyramidal neurons during development and alterations in this process can cause intellectual disabilities and developmental delay. Malformations in cortical development arise when neurons either fail to migrate properly from the germinal zones or fail to cease migration in the correct laminar position within the cortical plate. The Reelin signalling pathway is vital for correct neuronal positioning as loss of Reelin leads to a partially inverted cortex. The precise biological function of Reelin remains controversial and debate surrounds its role as a chemoattractant or stop signal for migrating neurons. To investigate this further we developed an in silico agent-based model of cortical layer formation. Using this model we tested four biologically plausible hypotheses for neuron motility and four biologically plausible hypotheses for the loss of neuron motility (conversion from migration). A matrix of 16 combinations of motility and conversion rules was applied against the known structure of mouse cortical layers in the wild-type cortex, the Reelin-null mutant, the Dab1-null mutant and a conditional Dab1 mutant. Using this approach, many combinations of motility and conversion mechanisms can be rejected. For example, the model does not support Reelin acting as a repelling or as a stopping signal. In contrast, the study lends very strong support to the notion that the glycoprotein Reelin acts as a chemoattractant for neurons. Furthermore, the most viable proposition for the conversion mechanism is one in which conversion is affected by a motile neuron sensing in the near vicinity neurons that have already converted. Therefore, this model helps elucidate the function of Reelin during neuronal migration and cortical development.     

In Silico Modeling and Conformational Mobility of DNA Duplexes

The review is focused on issues of transferability of the context-sensitive conformational characteristics of DNA estimated from crystallographic structural data on the DNA in aqueous solution. The state of the art in molecular dynamics of charged biopolymers in aqueous solution is covered. Elaboration of expedient force fields and algorithms of calculating long-range electrostatic interactions and solving combined equations of atomic motion have made it possible to generate stable nanosecond trajectories of thermal atomic motion of the biopolymer in aqueous solution in the presence of counterions and salt ions over reasonable time. Tools for analyzing the atomic statistical trajectories of DNA duplexes in aqueous solution to infer context-dependent conformational dynamic characteristics are discussed together with advances in simulating the mechanisms of global axial bend in DNA duplexes. These techniques allow one to consecutively analyze relationships between the contextual composition of the duplex and the basic modes of essential motions, their amplitude and extent of fluctuation. Development of satisfactory methods for estimating the free energy of biopolymer conformations in solution permits qualitative assessment of the conformational thermodynamic stability of biopolymers and their complexes.     

Phytochemical Profiling and Pharmacological Evaluation of Leaf Extracts of Ruellia tuberosa L.: An In Vitro and In Silico Approach

The present study was designed to appraise the photoprotective, antioxidant, and antibacterial bioactivities of Ruellia tuberosa leaves extracts (RtPE, RtChl, RtEA, RtAc, RtMe, and RtHMe). The results showed that, RtHMe extracts of R. tuberosa was rich in total phenolic content, i. e., 1.60 mgGAE/g dry extract, while highest total flavonoid content was found in RtAc extract, i. e., 0.40 mgQE/g. RtMe showed effective antioxidant activity (%RSA: 58.16) at the concentration of 120 μL. RtMe, RtEA and RtHMe exhibited effective in vitro antibacterial activity against Gram-negative bacteria (E. coli). In silico docking studies revealed that paucifloside (−11.743 kcal/mol), indole-3-carboxaldehyde (−7.519 kcal/mol), nuomioside (−7.275 kcal/mol), isocassifolioside (−6.992 kcal/mol) showed best docking score against PDB ID 2EX8 [penicillin binding protein 4 (dacB) from Escherichia coli, complexed with penicillin-G], PDB ID 6CQA (E. coli dihydrofolate reductase protein complexed with inhibitor AMPQD), PDB ID 2Y2I [Penicillin-binding protein 1B in complex with an alkyl boronate (ZA3)] and PDB ID 2OLV (from S. aureus), respectively. Docked phytochemicals also showed good drug likeness properties.     

Size Does Matter: An Integrative In Vivo-In Silico Approach for the Treatment of Critical Size Bone Defects

Although bone has a unique restorative capacity, i.e., it has the potential to heal scarlessly, the conditions for spontaneous bone healing are not always present, leading to a delayed union or a non-union. In this work, we use an integrative in vivo - in silico approach to investigate the occurrence of non-unions, as well as to design possible treatment strategies thereof. The gap size of the domain geometry of a previously published mathematical model was enlarged in order to study the complex interplay of blood vessel formation, oxygen supply, growth factors and cell proliferation on the final healing outcome in large bone defects. The multiscale oxygen model was not only able to capture the essential aspects of in vivo non-unions, it also assisted in understanding the underlying mechanisms of action, i.e., the delayed vascularization of the central callus region resulted in harsh hypoxic conditions, cell death and finally disrupted bone healing. Inspired by the importance of a timely vascularization, as well as by the limited biological potential of the fracture hematoma, the influence of the host environment on the bone healing process in critical size defects was explored further. Moreover, dependent on the host environment, several treatment strategies were designed and tested for effectiveness. A qualitative correspondence between the predicted outcomes of certain treatment strategies and experimental observations was obtained, clearly illustrating the model''s potential. In conclusion, the results of this study demonstrate that due to the complex non-linear dynamics of blood vessel formation, oxygen supply, growth factor production and cell proliferation and the interactions thereof with the host environment, an integrative in silico-in vivo approach is a crucial tool to further unravel the occurrence and treatments of challenging critical sized bone defects.     

Epitope-Based Vaccine Target Screening against Highly Pathogenic MERS-CoV: An In Silico Approach Applied to Emerging Infectious Diseases

Middle East respiratory syndrome coronavirus (MERS-CoV) with pandemic potential is a major worldwide threat to public health. However, vaccine development for this pathogen lags behind as immunity associated with protection is currently largely unknown. In this study, an immunoinformatics-driven genome-wide screening strategy of vaccine targets was performed to thoroughly screen the vital and effective dominant immunogens against MERS-CoV. Conservancy and population coverage analysis of the epitopes were done by the Immune Epitope Database. The results showed that the nucleocapsid (N) protein of MERS-CoV might be a better protective immunogen with high conservancy and potential eliciting both neutralizing antibodies and T-cell responses compared with spike (S) protein. Further, the B-cell, helper T-cell and cytotoxic T lymphocyte (CTL) epitopes were screened and mapped to the N protein. A total of 15 linear and 10 conformal B-cell epitopes that may induce protective neutralizing antibodies were obtained. Additionally, a total of 71 peptides with 9-mer core sequence were identified as helper T-cell epitopes, and 34 peptides were identified as CTL epitopes. Based on the maximum HLA binding alleles, top 10 helper T-cell epitopes and CTL epitopes that may elicit protective cellular immune responses against MERS-CoV were selected as MERS vaccine candidates. Population coverage analysis showed that the putative helper T-cell epitopes and CTL epitopes could cover the vast majority of the population in 15 geographic regions considered where vaccine would be employed. The B- and T-cell stimulation potentials of the screened epitopes is to be further validated for their efficient use as vaccines against MERS-CoV. Collectively, this study provides novel vaccine target candidates and may prompt further development of vaccines against MERS-CoV and other emerging infectious diseases.     

Bevacizumab Antibody Affinity Maturation to Improve Ovarian Cancer Immunotherapy: In Silico Approach

International Journal of Peptide Research and Therapeutics - Ovarian cancer is one of the most lethal gynecologic cancers. The high mortality rate is due to lack of early symptoms and developing...     

Trypanothione Reductase: A Target Protein for a Combined In Vitro and In Silico Screening Approach

With the goal to identify novel trypanothione reductase (TR) inhibitors, we performed a combination of in vitro and in silico screening approaches. Starting from a highly diverse compound set of 2,816 compounds, 21 novel TR inhibiting compounds could be identified in the initial in vitro screening campaign against T. cruzi TR. All 21 in vitro hits were used in a subsequent similarity search-based in silico screening on a database containing 200,000 physically available compounds. The similarity search resulted in a data set containing 1,204 potential TR inhibitors, which was subjected to a second in vitro screening campaign leading to 61 additional active compounds. This corresponds to an approximately 10-fold enrichment compared to the initial pure in vitro screening. In total, 82 novel TR inhibitors with activities down to the nM range could be identified proving the validity of our combined in vitro/in silico approach. Moreover, the four most active compounds, showing IC₅₀ values of <1 μM, were selected for determining the inhibitor constant. In first on parasites assays, three compounds inhibited the proliferation of bloodstream T. brucei cell line 449 with EC₅₀ values down to 2 μM.     

Structural Modeling and In Silico Analysis of Human Superoxide Dismutase 2

Aging in the world population has increased every year. Superoxide dismutase 2 (Mn-SOD or SOD2) protects against oxidative stress, a main factor influencing cellular longevity. Polymorphisms in SOD2 have been associated with the development of neurodegenerative diseases, such as Alzheimer’s and Parkinson’s disease, as well as psychiatric disorders, such as schizophrenia, depression and bipolar disorder. In this study, all of the described natural variants (S10I, A16V, E66V, G76R, I82T and R156W) of SOD2 were subjected to in silico analysis using eight different algorithms: SNPeffect, PolyPhen-2, PhD-SNP, PMUT, SIFT, SNAP, SNPs&GO and nsSNPAnalyzer. This analysis revealed disparate results for a few of the algorithms. The results showed that, from at least one algorithm, each amino acid substitution appears to harmfully affect the protein. Structural theoretical models were created for variants through comparative modelling performed using the MHOLline server (which includes MODELLER and PROCHECK) and ab initio modelling, using the I-Tasser server. The predicted models were evaluated using TM-align, and the results show that the models were constructed with high accuracy. The RMSD values of the modelled mutants indicated likely pathogenicity for all missense mutations. Structural phylogenetic analysis using ConSurf revealed that human SOD2 is highly conserved. As a result, a human-curated database was generated that enables biologists and clinicians to explore SOD2 nsSNPs, including predictions of their effects and visualisation of the alignment of both the wild-type and mutant structures. The database is freely available at http://bioinfogroup.com/database and will be regularly updated.     

Selective Interactions of Zein Microspheres with Different Class of Drugs: An In Vitro and In Silico Analysis

In this study, we have evaluated the interactions of zein microspheres with different class of drugs (hydrophobic, hydrophilic, and amphiphilic) using in vitro and in silico analysis. Zein microspheres loaded with aceclofenac, metformin, and promethazine has been developed by solvent evaporation technique and analyzed for its compatibility. The physical characterization depicted the proper encapsulation of hydrophobic drug in the microspheres. The in vitro release study revealed the sustaining ability of the microspheres in the following order: hydrophobic > hydrophilic > amphiphilic. In silico analysis also confirmed the better binding affinity and greater interactions of hydrophobic drug with zein. The above results revealed that zein is more suitable for hydrophobic drugs in the development of sustained drug delivery systems using solvent evaporation technique. The study therefore envisages a scope for identifying the most suitable polymer for a sustained drug delivery system in accordance with the nature of the drug.KEY WORDS: hydrophilic drugs, hydrophobic drugs, in silico analysis, protein-drug interactions, solvent evaporation, zein microspheres     

GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions

Genome-scale metabolic reconstructions are typically validated by comparing in silico growth predictions across different mutants utilizing different carbon sources with in vivo growth data. This comparison results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment (GNG inconsistencies) or the model predicts no growth when the experiment reveals growth (NGG inconsistencies). Here we propose an optimization-based framework, GrowMatch, to automatically reconcile GNG predictions (by suppressing functionalities in the model) and NGG predictions (by adding functionalities to the model). We use GrowMatch to resolve inconsistencies between the predictions of the latest in silico Escherichia coli (iAF1260) model and the in vivo data available in the Keio collection and improved the consistency of in silico with in vivo predictions from 90.6% to 96.7%. Specifically, we were able to suggest consistency-restoring hypotheses for 56/72 GNG mutants and 13/38 NGG mutants. GrowMatch resolved 18 GNG inconsistencies by suggesting suppressions in the mutant metabolic networks. Fifteen inconsistencies were resolved by suppressing isozymes in the metabolic network, and the remaining 23 GNG mutants corresponding to blocked genes were resolved by suitably modifying the biomass equation of iAF1260. GrowMatch suggested consistency-restoring hypotheses for five NGG mutants by adding functionalities to the model whereas the remaining eight inconsistencies were resolved by pinpointing possible alternate genes that carry out the function of the deleted gene. For many cases, GrowMatch identified fairly nonintuitive model modification hypotheses that would have been difficult to pinpoint through inspection alone. In addition, GrowMatch can be used during the construction phase of new, as opposed to existing, genome-scale metabolic models, leading to more expedient and accurate reconstructions.