Evaluating the potential neurotoxicity of hexanedione isomers:an in vitro approach

2,5-hexanedione (2,5HD) is the neurotoxic metabolite of the aliphatic hydrocarbon n-Hexane. The isomers, 2,3-hexanedione (2,3HD) and 3,4-hexanedione (3,4HD) are used as food additives. Although the neurotoxicity of 2,5HD is well established, there are no human data of the possible toxicity of the 2,3- and 3,4- isomers. MTT and flow cytometry were utilised to determine the cytotoxicity of hexanedione isomers in neuroblastoma cells. The neuroblastoma cell lines SK-N-SH and SH-SY5Y are sufficiently neuron-like to provide preliminary assessment of the neurotoxic potential of these isomers, in comparison with toxicity towards human non-neuronal cells. Initial studies showed that 2,5HD was the least toxic in all cell lines at all times (4, 24 and 48h). Although considerably lower than for 2,5HD, in general the IC50s for the α isomers were not significantly different from each other and, besides 4h exposure, the SH-SY5Y cells were significantly more sensitive to 2,3HD and 3,4HD than the SK-N-SH cells. All three isomers caused varying degrees of apoptosis in the neuroblastoma lines, with 3,4HD more potent than 2,3HD. Flow cytometry highlighted cell cycle arrest indicative of DNA damage with 2,3- and 3,4HD. The toxicity of the isomers towards 3 non-neuronal cell lines (MCF7, HepG2 and CaCo-2) was assessed by MTT assay. All 3 hexanedione isomers proved to be cytotoxic in all non-neuronal cell lines at all time points. These data suggest cytotoxicity of 2,3- and 3,4HD (mM range), but it is difficult to define this as specific neurotoxicity in the absence of specific neurotoxic endpoints. However, the neuroblastomas were significantly more susceptible to the cytotoxic effects of the α hexanedione isomers at exposures of 4 and 24 hours, compared to non-neuronal lines. Finally, a mechanism of toxicity is suggested for the α HD isomers whereby inhibition of the oxoglutarate carrier (OGC) releases apoptosis inducing factor (AIF), causing apoptosis-like cell death.

In silico identification of novel G protein coupled receptors

G-protein coupled receptors (GPCRs) are a superfamily of membrane integral proteins responsible for a large number of physiological functions. Approximately 50% of marketed drugs are targeted toward a GPCR. Despite showing a high degree of structural homology, there is a large variance in sequence within the GPCR superfamily which has lead to difficulties in identifying and classifying potential new GPCR proteins. Here the various computational techniques that can be used to characterize a novel GPCR protein are discussed, including both alignment-based and alignment-free approaches. In addition, the application of homology modeling to building the three-dimensional structures of GPCRs is described.

Immunoinformatics and the in silico prediction of immunogenicity:an introduction

Immunoinformatics is the application of informatics techniques to molecules of the immune system. One of its principal goals is the effective prediction of immunogenicity, be that at the level of epitope, subunit vaccine, or attenuated pathogen. Immunogenicity is the ability of a pathogen or component thereof to induce a specific immune response when first exposed to surveillance by the immune system, whereas antigenicity is the capacity for recognition by the extant machinery of the adaptive immune response in a recall response. In thisbook, we introduce these subjects and explore the current state of play in immunoinformatics and the in silico prediction of immunogenicity.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

In silico prediction of peptide-MHC binding affinity using SVRMHC

The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.

In silico modelling of the interaction of flavonoids with human P-glycoprotein nucleotide-binding domain

A three-dimensional model of human ABCB1 nucleotide-binding domain (NBD) was developed by homology modelling using the high-resolution human TAP1 transporter structure as template. Interactions between NBD and flavonoids were investigated using in silico docking studies. Ring-A of unmodified flavonoid was located within the NBD P-loop with the 5-hydroxyl group involved in hydrogen bonding with Lys1076. Ring-B was stabilised by hydrophobic stacking interactions with Tyr1044. The 3-hydroxyl group and carbonyl oxygen were extensively involved in hydrogen bonding interactions with amino acids within the NBD. Addition of prenyl, benzyl or geranyl moieties to ring-A (position-6) and hydrocarbon substituents (O-n-butyl to O-n-decyl) to ring-B (position-4) resulted in a size-dependent decrease in predicted docking energy which reflected the increased binding affinities reported in vitro.

Immunoinformatics:predicting immunogenicity in silico

This volume both engages the reader and provides a sound foundation for the use of immunoinformatics techniques in immunology and vaccinology. It addresses databases, HLA supertypes, MCH binding, and other properties of immune systems. The book contains chapters written by leaders in the field and provides a firm background for anyone working in immunoinformatics in one easy-to-use, insightful volume.

New horizons in mouse immunoinformatics:reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide–protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).

Towards in silico prediction of immunogenic epitopes

As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments – data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures – offer hope for the future.

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

Efficient HIV-1 inhibition by a 16 nt-long RNA aptamer designed by combining in vitro selection and in silico optimisation strategies

The human immunodeficiency virus type-1 (HIV-1) genome contains multiple, highly conserved structural RNA domains that play key roles in essential viral processes. Interference with the function of these RNA domains either by disrupting their structures or by blocking their interaction with viral or cellular factors may seriously compromise HIV-1 viability. RNA aptamers are amongst the most promising synthetic molecules able to interact with structural domains of viral genomes. However, aptamer shortening up to their minimal active domain is usually necessary for scaling up production, what requires very time-consuming, trial-and-error approaches. Here we report on the in vitro selection of 64 nt-long specific aptamers against the complete 5' -untranslated region of HIV-1 genome, which inhibit more than 75% of HIV-1 production in a human cell line. The analysis of the selected sequences and structures allowed for the identification of a highly conserved 16 nt-long stem-loop motif containing a common 8 nt-long apical loop. Based on this result, an in silico designed 16 nt-long RNA aptamer, termed RNApt16, was synthesized, with sequence 5'-CCCCGGCAAGGAGGGG-3-'. The HIV-1 inhibition efficiency of such an aptamer was close to 85%, thus constituting the shortest RNA molecule so far described that efficiently interferes with HIV-1 replication.

AllerTOP v.2 - a server for in silico prediction of allergens

Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance - typically proteins - resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours (kNN). The best performing method was kNN with 85.3% accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (http://www.ddg-pharmfac.net/AllerTOP). © Springer-Verlag 2014.

A methodological evaluation and predictive in silico investigation into the multi-functionality of arginine in directly compressed tablets

The acceleration of solid dosage form product development can be facilitated by the inclusion of excipients that exhibit poly-/multi-functionality with reduction of the time invested in multiple excipient optimisations. Because active pharmaceutical ingredients (APIs) and tablet excipients present diverse densification behaviours upon compaction, the involvement of these different powders during compaction makes the compaction process very complicated. The aim of this study was to assess the macrometric characteristics and distribution of surface charges of two powders: indomethacin (IND) and arginine (ARG); and evaluate their impact on the densification properties of the two powders. Response surface modelling (RSM) was employed to predict the effect of two independent variables; Compression pressure (F) and ARG percentage (R) in binary mixtures on the properties of resultant tablets. The study looked at three responses namely; porosity (P), tensile strength (S) and disintegration time (T). Micrometric studies showed that IND had a higher charge density (net charge to mass ratio) when compared to ARG; nonetheless, ARG demonstrated good compaction properties with high plasticity (Y=28.01MPa). Therefore, ARG as filler to IND tablets was associated with better mechanical properties of the tablets (tablet tensile strength (σ) increased from 0.2±0.05N/mm2 to 2.85±0.36N/mm2 upon adding ARG at molar ratio of 8:1 to IND). Moreover, tablets' disintegration time was shortened to reach few seconds in some of the formulations. RSM revealed tablet porosity to be affected by both compression pressure and ARG ratio for IND/ARG physical mixtures (PMs). Conversely, the tensile strength (σ) and disintegration time (T) for the PMs were influenced by the compression pressure, ARG ratio and their interactive term (FR); and a strong correlation was observed between the experimental results and the predicted data for tablet porosity. This work provides clear evidence of the multi-functionality of ARG as filler, binder and disintegrant for directly compressed tablets.

Maximizing Hospitality Learning Outcomes: An Integrated Experiential In-Class Approach

This study explored the influence of an experiential, in-class approach to the hospitality curriculum as a means of increasing its efficiency and effectiveness. Specifically, the study provides an example of how hospitality faculty might utilize an experiential, in-class approach to integrate additional hospitality-specific content along with process and content issues for working in teams and team decision-making. The results of this study support the efficient and effective use of an experiential inclass teaching method. The value of this study is twofold: (1) it provides an initial test of this approach’s usefulness and (2) it provides a forum for continued conversations of how experiential approaches can be utilized to enhance and reinforce other hospitality content and managerial skills and to bridge the gap between vocational and liberal education outcomes.

Estudo in silico das interações da protease NS3-NS2B de DENV-2 com o inibidor peptídico Bz-nKRR-H com finalidades terapêuticas

Dengue virus is an important patogen that causes Dengue desease in all world, and belongs to Flavivirus gender. The virus consists of enveloped RNA with a single strand positive sense, 11Kb genome. The RNA is translated into a polyprotein precursor, wich is cleaved into 3 structural proteins (C, prM e E) and 7 non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B e NS5). The NS3 is a multifunctional protein, that besides to promote the polyprotein precursor cleavage, also have NTPase, helicase and RTPase activity. The NS3 needs a hydrophilic segment of 40 residues from the transmembrane NS2B protein (who acts like cofator) to realize this functions. Actually, there's no vacines available on the market, and the treatment are just symptomatic. The tetrapeptide inhibitor Bz-Nle-Lys-Arg-Arg-H (Ki de 5,8-7,0 M) was showed as a potent inhibitor μ for NS3prot in Dengue virus. That is a inteligent alternative to treat the dengue desease. The present work aimed analyse the interactions of the ligand bounded to the activity site to provid a clear and depth vision of that interaction. For this purpouse, it was conducted an in silico study, by using quantum mechanical calculations based on Density Functional Theory (DFT), with Generalized Gradient approximation (GGA) to describe the effects of exchange and correlation. The interaction energy of each amino acid belonging to the binding site to the ligand was calculated the using the method of molecular fragmentation with conjugated caps (MFCC). Besides energy, we calculated the distances, types of molecular interactions and atomic groups involved. The theoretical models used were satisfactory and show a more accurate description when the dielectric constant = 20 ε and 80 was used. The results demonstrate that the interaction energy of the system reached convergence at 13.5 A. Within a radius of 13,5A the most important residues were identified. Met49, Met84 and Asp81 perform interactions of hydrogen with the ligant. The Asp79 and Asp75 residues present high energy of attraction. Arg54, Arg85 and Lys 131 perform hydrogen interactions with the ligand, however, appear in BIRD graph having high repulsion energy with the inhibitor. The data also emphasizes the importance of residue Tyr161 and the involvement of the catalytic triad composed by Asp75, His51 and Ser135