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.

AllerTOP - a server for in silico prediction of allergens

Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.

In silico adjuvant design and validation

Adjuvants are substances that boost the protective immune response to vaccine antigens. The majority of known adjuvants have been identified through the use of empirical approaches. Our aim was to identify novel adjuvants with well-defined cellular and molecular mechanisms by combining a knowledge of immunoregulatory mechanisms with an in silico approach. CD4 + CD25 + FoxP3 + regulatory T cells (Tregs) inhibit the protective immune responses to vaccines by suppressing the activation of antigen presenting cells such as dendritic cells (DCs). In this chapter, we describe the identification and functional validation of small molecule antagonists to CCR4, a chemokine receptor expressed on Tregs. The CCR4 binds the chemokines CCL22 and CCL17 that are produced in large amounts by activated innate cells including DCs. In silico identified small molecule CCR4 antagonists inhibited the migration of Tregs both in vitro and in vivo and when combined with vaccine antigens, significantly enhanced protective immune responses in experimental models.

Role of the transgenic human thyrotropin receptor A-subunit in thyroiditis induced by A-subunit immunization and regulatory T cell depletion

Transgenic BALB/c mice that express intrathyroidal human thyroid stimulating hormone receptor (TSHR) A-subunit, unlike wild-type (WT) littermates, develop thyroid lymphocytic infiltration and spreading to other thyroid autoantigens after T regulatory cell (Treg) depletion and immunization with human thyrotropin receptor (hTSHR) adenovirus. To determine if this process involves intramolecular epitope spreading, we studied antibody and T cell recognition of TSHR ectodomain peptides (A–Z). In transgenic and WT mice, regardless of Treg depletion, TSHR antibodies bound predominantly to N-terminal peptide A and much less to a few downstream peptides. After Treg depletion, splenocytes from WT mice responded to peptides C, D and J (all in the A-subunit), but transgenic splenocytes recognized only peptide D. Because CD4+ T cells are critical for thyroid lymphocytic infiltration, amino acid sequences of these peptides were examined for in silico binding to BALB/c major histocompatibility complex class II (IA–d). High affinity subsequences (inhibitory concentration of 50% < 50 nm) are present in peptides C and D (not J) of the hTSHR and mouse TSHR equivalents. These data probably explain why transgenic splenocytes do not recognize peptide J. Mouse TSHR mRNA levels are comparable in transgenic and WT thyroids, but only transgenics have human A-subunit mRNA. Transgenic mice can present mouse TSHR and human A-subunit-derived peptides. However, WT mice can present only mouse TSHR, and two to four amino acid species differences may preclude recognition by CD4+ T cells activated by hTSHR-adenovirus. Overall, thyroid lymphocytic infiltration in the transgenic mice is unrelated to epitopic spreading but involves human A-subunit peptides for recognition by T cells activated using the hTSHR.

Identification of putative steroid-binding sites in human ABCB1 and ABCG2

Homology modelling was used to generate three-dimensional structures of the nucleotide-binding domains (NBDs) of human ABCB1 and ABCG2. Interactions between a series of steroidal ligands and transporter NBDs were investigated using an in silico docking approach. C-terminal ABCB1 NBD (ABCB1 NBD2) was predicted to bind steroids within a cavity formed partly by the P-Loop, Tyr1044 and Ile1050. The P-Loop within ABCG2 NBD was also predicted to be involved in steroid binding. No overlap between ATP- and RU-486-binding sites was predicted in either NBD, though overlaps between ATP- and steroid-binding sites were predicted in the vicinity of the P-Loop in both nucleotide-binding domains.

Phenotypic effect of mutations in evolving populations of RNA molecules

Background. The secondary structure of folded RNA sequences is a good model to map phenotype onto genotype, as represented by the RNA sequence. Computational studies of the evolution of ensembles of RNA molecules towards target secondary structures yield valuable clues to the mechanisms behind adaptation of complex populations. The relationship between the space of sequences and structures, the organization of RNA ensembles at mutation-selection equilibrium, the time of adaptation as a function of the population parameters, the presence of collective effects in quasispecies, or the optimal mutation rates to promote adaptation all are issues that can be explored within this framework. Results. We investigate the effect of microscopic mutations on the phenotype of RNA molecules during their in silico evolution and adaptation. We calculate the distribution of the effects of mutations on fitness, the relative fractions of beneficial and deleterious mutations and the corresponding selection coefficients for populations evolving under different mutation rates. Three different situations are explored: the mutation-selection equilibrium (optimized population) in three different fitness landscapes, the dynamics during adaptation towards a goal structure (adapting population), and the behavior under periodic population bottlenecks (perturbed population). Conclusions. The ratio between the number of beneficial and deleterious mutations experienced by a population of RNA sequences increases with the value of the mutation rate µ at which evolution proceeds. In contrast, the selective value of mutations remains almost constant, independent of µ, indicating that adaptation occurs through an increase in the amount of beneficial mutations, with little variations in the average effect they have on fitness. Statistical analyses of the distribution of fitness effects reveal that small effects, either beneficial or deleterious, are well described by a Pareto distribution. These results are robust under changes in the fitness landscape, remarkably when, in addition to selecting a target secondary structure, specific subsequences or low-energy folds are required. A population perturbed by bottlenecks behaves similarly to an adapting population, struggling to return to the optimized state. Whether it can survive in the long run or whether it goes extinct depends critically on the length of the time interval between bottlenecks. © 2010 Stich et al; licensee BioMed Central Ltd.

In vitro protective effect and antioxidant mechanism of Resveratrol induced by Dapsone Hydroxylamine in human cells

Dapsone (DDS) hydroxylamine metabolites cause oxidative stress- linked adverse effects in patients, such as methemoglobin formation and DNA damage. This study evaluated the ameliorating effect of the antioxidant resveratrol (RSV) on DDS hydroxylamine (DDSNHOH) mediated toxicity in vitro using human erythrocytes and lymphocytes. The antioxidant mechanism was also studied using in-silico methods. In addition, RSV provided intracellular protection by inhibiting DNA damage in human lymphocytes induced by DDS-NHOH. However, whilst pretreatment with RSV (10-1000 μM significantly attenuated DDS-NHOH-induced methemoglobinemia, but it was not only significantly less effective than methylene blue (MET), but also post-treatment with RSV did not reverse methemoglobin formation, contrarily to that observed with MET. DDS-NHOH inhibited catalase (CAT) activity and reactive oxygen species (ROS) generation, but did not alter superoxide dismutase (SOD) activity in erythrocytes. Pretreatment with RSV did not alter these antioxidant enzymes activities in erythrocytes treated with DDS-NHOH. Theoretical calculations using density functional theory methods showed that DDS-NHOH has a pro-oxidant effect, whereas RSV and MET have antioxidant effect on ROS. The effect on methemoglobinemia reversion for MET was significantly higher than that of RSV. These data suggest that the pretreatment with resveratrol may decrease heme-iron oxidation and DNA damage through reduction of ROS generated in cells during DDS therapy.

Innovative biomarkers for predicting type 2 diabetes mellitus:relevance to dietary management of frailty in older adults

Type 2 diabetes mellitus (T2DM) increases in prevalence in the elderly. There is evidence for significant muscle loss and accelerated cognitive impairment in older adults with T2DM; these comorbidities are critical features of frailty. In the early stages of T2DM, insulin sensitivity can be improved by a “healthy” diet. Management of insulin resistance by diet in people over 65 years of age should be carefully re-evaluated because of the risk for falling due to hypoglycaemia. To date, an optimal dietary programme for older adults with insulin resistance and T2DM has not been described. The use of biomarkers to identify those at risk for T2DM will enable clinicians to offer early dietary advice that will delay onset of disease and of frailty. Here we have used an in silico literature search for putative novel biomarkers of T2DM risk and frailty. We suggest that plasma bilirubin, plasma, urinary DPP4-positive microparticles and plasma pigment epithelium-derived factor merit further investigation as predictive biomarkers for T2DM and frailty risk in older adults. Bilirubin is screened routinely in clinical practice. Measurement of specific microparticle frequency in urine is less invasive than a blood sample so is a good choice for biomonitoring. Future studies should investigate whether early dietary changes, such as increased intake of whey protein and micronutrients that improve muscle function and insulin sensitivity, affect biomarkers and can reduce the longer term complication of frailty in people at risk for T2DM.

Variable mutation rates as an adaptive strategy in replicator populations

For evolving populations of replicators, there is much evidence that the effect of mutations on fitness depends on the degree of adaptation to the selective pressures at play. In optimized populations, most mutations have deleterious effects, such that low mutation rates are favoured. In contrast to this, in populations thriving in changing environments a larger fraction of mutations have beneficial effects, providing the diversity necessary to adapt to new conditions. What is more, non-adapted populations occasionally benefit from an increase in the mutation rate. Therefore, there is no optimal universal value of the mutation rate and species attempt to adjust it to their momentary adaptive needs. In this work we have used stationary populations of RNA molecules evolving in silico to investigate the relationship between the degree of adaptation of an optimized population and the value of the mutation rate promoting maximal adaptation in a short time to a new selective pressure. Our results show that this value can significantly differ from the optimal value at mutation-selection equilibrium, being strongly influenced by the structure of the population when the adaptive process begins. In the short-term, highly optimized populations containing little variability respond better to environmental changes upon an increase of the mutation rate, whereas populations with a lower degree of optimization but higher variability benefit from reducing the mutation rate to adapt rapidly. These findings show a good agreement with the behaviour exhibited by actual organisms that replicate their genomes under broadly different mutation rates. © 2010 Stich et al.

Design and synthesis of potential antimicrobial agents

Chorismate mutase is one of the essential enzymes in the shikimate pathway and is key to the survival of the organism Mycobacterium tuberculosis. The x-ray crystal structure of this enzyme from Mycobacterium tuberculosis was manipulated to prepare an initial set of in silico protein models of the active site. Known inhibitors of the enzyme were docked into the active site using the flexible ligand / flexible active site side chains approach implemented in CAChe Worksystem (Fujitsu Ltd). The resulting complexes were refined by molecular dynamics studies in explicit water using Amber 9. This yielded a further set of protein models that were used for additional rounds of ligand docking. A binding hypothesis was established for the enzyme and this was used to screen a database of commercially available drug-like compounds. From these results new potential ligands were designed that fitted appropriately into the active site and matched the functional groups and binding motifs founds therein. Some of these compounds and close analogues were then synthesized and submitted for biological evaluation. As a separate part of this thesis, analogues of very active anti-tuberculosis pyridylcarboxamidrazone were also prepared. This was carried out by the addition and the deletion of the substitutions from the lead compound thereby preparing heteroaryl carboxamidrazone derivatives and related compounds. All these compounds were initially evaluated for biological activity against various gram positive organisms and then sent to the TAACF (USA) for screening against Mycobacterium tuberculosis. Some of the new compounds proved to be at least as potent as the original lead compound but less toxic.

MHC class II binding prediction:a little help from a friend

Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

Characterisation of the plasma membrane subproteome of bloodstream form Trypanosoma brucei

Proteome analysis by conventional approaches is biased against hydrophobic membrane proteins, many of which are also of low abundance. We have isolated plasma membrane sheets from bloodstream forms of Trypanosoma brucei by subcellular fractionation, and then applied a battery of complementary protein separation and identification techniques to identify a large number of proteins in this fraction. The results of these analyses have been combined to generate a subproteome for the pellicular plasma membrane of bloodstream forms of T. brucei as well as a separate subproteome for the pellicular cytoskeleton. In parallel, we have used in silico approaches to predict the relative abundance of proteins potentially expressed by bloodstream form trypanosomes, and to identify likely polytopic membrane proteins, providing quality control for the experimentally defined plasma membrane subproteome. We show that the application of multiple high-resolution proteomic techniques to an enriched organelle fraction is a valuable approach for the characterisation of relatively intractable membrane proteomes. We present here the most complete analysis of a protozoan plasma membrane proteome to date and show the presence of a large number of integral membrane proteins, including 11 nucleoside/nucleobase transporters, 15 ion pumps and channels and a large number of adenylate cyclases hitherto listed as putative proteins.

Proteins accessible to immune surveillance show significant T-cell epitope depletion:implications for vaccine design

T cell activation is the final step in a complex pathway through which pathogen-derived peptide fragments can elicit an immune response. For it to occur, peptides must form stable complexes with Major Histocompatibility Complex (MHC) molecules and be presented on the cell surface. Computational predictors of MHC binding are often used within in silico vaccine design pathways. We have previously shown that, paradoxically, most bacterial proteins known experimentally to elicit an immune response in disease models are depleted in peptides predicted to bind to human MHC alleles. The results presented here, derived using software proven through benchmarking to be the most accurate currently available, show that vaccine antigens contain fewer predicted MHC-binding peptides than control bacterial proteins from almost all subcellular locations with the exception of cell wall and some cytoplasmic proteins. This effect is too large to be explained from the undoubted lack of precision of the software or from the amino acid composition of the antigens. Instead, we propose that pathogens have evolved under the influence of the host immune system so that surface proteins are depleted in potential MHC-binding peptides, and suggest that identification of a protein likely to contain a single immuno-dominant epitope is likely to be a productive strategy for vaccine design.

Multimodal characterisation of sensorimotor oscillations

The studies in this project have investigated the ongoing neuronal network oscillatory activity found in the sensorimotor cortex using two modalities: magnetoencephalography (MEG) and in vitro slice recordings. The results have established that ongoing sensorimotor oscillations span the mu and beta frequency region both in vitro and in MEG recordings, with distinct frequency profiles for each recorded laminae in vitro, while MI and SI show less difference in humans. In addition, these studies show that connections between MI and SI modulate the ongoing neuronal network activity in these areas. The stimulation studies indicate that specific frequencies of stimulation affect the ongoing activity in the sensorimotor cortex. The continuous theta burst stimulation (cTBS) study demonstrates that cTBS predominantly enhances the power of the local ongoing activity. The stimulation studies in this project show limited comparison between modalities, which is informative of the role of connectivity in these effects. However, independently these studies provide novel information on the mechanisms on sensorimotor oscillatory interaction. The pharmacological studies reveal that GABAergic modulation with zolpidem changes the neuronal oscillatory network activity in both healthy and pathological MI. Zolpidem enhances the power of ongoing oscillatory activity in both sensorimotor laminae and in healthy subjects. In contrast, zolpidem attenuates the “abnormal” beta oscillatory activity in the affected hemisphere in Parkinsonian patients, while restoring the hemispheric beta power ratio and frequency variability and thereby improving motor symptomatology. Finally we show that independent signals from MI laminae can be integrated in silico to resemble the aggregate MEG MI oscillatory signals. This highlights the usefulness of combining these two methods when elucidating neuronal network oscillations in the sensorimotor cortex and any interventions.