Physiologically based synthetic models of hepatic disposition

Current Physiologically based pharmacokinetic (PBPK) models are inductive. We present an additional, different approach that is based on the synthetic rather than the inductive approach to modeling and simulation. It relies on object-oriented programming A model of the referent system in its experimental context is synthesized by assembling objects that represent components such as molecules, cells, aspects of tissue architecture, catheters, etc. The single pass perfused rat liver has been well described in evaluating hepatic drug pharmacokinetics (PK) and is the system on which we focus. In silico experiments begin with administration of objects representing actual compounds. Data are collected in a manner analogous to that in the referent PK experiments. The synthetic modeling method allows for recognition and representation of discrete event and discrete time processes, as well as heterogeneity in organization, function, and spatial effects. An application is developed for sucrose and antipyrine, administered separately and together PBPK modeling has made extensive progress in characterizing abstracted PK properties but this has also been its limitation. Now, other important questions and possible extensions emerge. How are these PK properties and the observed behaviors generated? The inherent heuristic limitations of traditional models have hindered getting meaningful, detailed answers to such questions. Synthetic models of the type described here are specifically intended to help answer such questions. Analogous to wet-lab experimental models, they retain their applicability even when broken apart into sub-components. Having and applying this new class of models along with traditional PK modeling methods is expected to increase the productivity of pharmaceutical research at all levels that make use of modeling and simulation.

Different routes to pure food allergens: extraction, recombinant techniques and total chemical synthesis for obtaining the plant-food pan-allergen LTP

La richiesta di allergeni puri è in continuo aumento per scopi diagnostici, come standard per metodi di rilevamento e di quantificazione, per l'immunoterapia e per lo studio a livello molecolare dei meccanismi delle reazioni allergiche, al fine di facilitare lo sviluppo di possibili cure. In questa tesi di dottorato sono descritte diverse strategie per l’ottenimento di forme pure di non-specific Lipid Transfer Proteins (nsLTPs), le quali sono state riconosciute essere rilevanti allergeni alimentari in molti frutti e verdure comunemente consumati e sono state definite come modello di veri allergeni alimentari. Una LTP potenzialmente allergenica, non nota in precedenza, è stata isolata dalle mandorle, mentre una LTP dall’allergenicità nota contenuta nelle noci è stata prodotta mediante tecniche di DNA ricombinante. Oltre a questi approcci classici, metodi per la sintesi chimica totale di proteine sono stati applicati per la prima volta alla produzione di un allergene, utilizzando Pru p 3, la LTP prototipica e principale allergene della pesca nell'area mediterranea, come modello. La sintesi chimica totale di proteinepermette di controllarne completamente la sequenza e di studiare la loro funzione a livello atomico. La sua applicazione alla produzione di allergeni costituisce perciò un importante passo avanti nel campo della ricerca sulle allergie alimentari. La proteina Pru p 3 è stata prodotta nella sua intera lunghezza e sono necessari solo due passaggi finali di deprotezione per ottenere il target nella sua forma nativa. Le condizioni sperimentali per tali deprotezioni sono state messe a punto durante la produzione dei peptidi sPru p 3 (1-37) e sPru p 3 (38-91), componenti insieme l'intera proteina. Tecniche avanzate di spettrometria di massa sono state usate per caratterizzare tutti i composti ottenuti, mentre la loro allergenicità è stata studiata attraverso test immunologici o approcci in silico.

Multi-scale approaches to Food Sciences: computational QM and MM explorations at the microscopic level

L'obiettivo principale della politica di sicurezza alimentare è quello di garantire la salute dei consumatori attraverso regole e protocolli di sicurezza specifici. Al fine di rispondere ai requisiti di sicurezza alimentare e standardizzazione della qualità, nel 2002 il Parlamento Europeo e il Consiglio dell'UE (Regolamento (CE) 178/2002 (CE, 2002)), hanno cercato di uniformare concetti, principi e procedure in modo da fornire una base comune in materia di disciplina degli alimenti e mangimi provenienti da Stati membri a livello comunitario. La formalizzazione di regole e protocolli di standardizzazione dovrebbe però passare attraverso una più dettagliata e accurata comprensione ed armonizzazione delle proprietà globali (macroscopiche), pseudo-locali (mesoscopiche), ed eventualmente, locali (microscopiche) dei prodotti alimentari. L'obiettivo principale di questa tesi di dottorato è di illustrare come le tecniche computazionali possano rappresentare un valido supporto per l'analisi e ciò tramite (i) l’applicazione di protocolli e (ii) miglioramento delle tecniche ampiamente applicate. Una dimostrazione diretta delle potenzialità già offerte dagli approcci computazionali viene offerta nel primo lavoro in cui un virtual screening basato su docking è stato applicato al fine di valutare la preliminare xeno-androgenicità di alcuni contaminanti alimentari. Il secondo e terzo lavoro riguardano lo sviluppo e la convalida di nuovi descrittori chimico-fisici in un contesto 3D-QSAR. Denominata HyPhar (Hydrophobic Pharmacophore), la nuova metodologia così messa a punto è stata usata per esplorare il tema della selettività tra bersagli molecolari strutturalmente correlati e ha così dimostrato di possedere i necessari requisiti di applicabilità e adattabilità in un contesto alimentare. Nel complesso, i risultati ci permettono di essere fiduciosi nel potenziale impatto che le tecniche in silico potranno avere nella identificazione e chiarificazione di eventi molecolari implicati negli aspetti tossicologici e nutrizionali degli alimenti.

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.

A dynamic model of the hypoxia-inducible factor 1α (HIF-1α) network

Activation of the hypoxia-inducible factor (HIF) pathway is a critical step in the transcriptional response to hypoxia. Although many of the key proteins involved have been characterised, the dynamics of their interactions in generating this response remain unclear. In the present study, we have generated a comprehensive mathematical model of the HIF-1a pathway based on core validated components and dynamic experimental data, and confirm the previously described connections within the predicted network topology. Our model confirms previous work demonstrating that the steps leading to optimal HIF-1a transcriptional activity require sequential inhibition of both prolyl- and asparaginyl-hydroxylases. We predict from our model (and confirm experimentally) that there is residual activity of the asparaginyl-hydroxylase FIH (factor inhibiting HIF) at low oxygen tension. Furthermore, silencing FIH under conditions where prolyl-hydroxylases are inhibited results in increased HIF-1a transcriptional activity, but paradoxically decreases HIF-1a stability. Using a core module of the HIF network and mathematical proof supported by experimental data, we propose that asparaginyl hydroxylation confers a degree of resistance upon HIF-1a to proteosomal degradation. Thus, through in vitro experimental data and in silico predictions, we provide a comprehensive model of the dynamic regulation of HIF-1a transcriptional activity by hydroxylases and use its predictive and adaptive properties to explain counter-intuitive biological observations.

The antimicrobial action of pseudin-2

The amphibian antimicrobial peptide pseudin-2 is a peptide derived from the skin of the South-American frog Pseudis paradoxa (Olson et al., 2001). This peptide possesses tremendous potential as a therapeutic lead since it has been shown to possess both antimicrobial as well insulin-releasing properties (Olson et al., 2001; Abdel-Wahab et al., 2008). This study aimed to develop pseudin-2’s potential by understanding and improving its properties as an antimicrobial agent. The structure-function relationships of pseudin-2 were explored using a combination of in-vitro and in-silico techniques, with an aim to predict how the structure of the peptide may be altered in order to improve its efficacy. A library of pseudin-2 mutants was generated by randomizing codons at positions 10, 14 and 18 of a synthetic gene, using NNK saturation mutagenesis. Analysis of these novel peptides broadly confirmed, in line with literature precedent, that anti-microbial activity increases with increased positive charge. Specifically, 2 positively-charged residues at positions 10 and 14 and a hydrophobic at position 18 are preferred. However, substitution at position 14 with some polar, non-charged residues also created peptides with antimicrobial activity. Interestingly, the pseudin-2 analogue [10-E, 14-Q, 18-L] which is identical to pseudin-2, except that the residues at positions 10 and 14 are switched, showed no anti-microbial activity at all. Molecular dynamics simulations of pseudin-2 showed that the peptide possesses two equilibrium structures in a membrane environment: a linear and a kinked a-helix which both embed into the membrane at an angle. Biophysical characterization using circular dichroism spectroscopy confirmed that the peptide is helical within the membrane environment whilst linear dichroism established that the peptide has no defined orientation within the membrane. Collectively, these data indicate that Pseudin-2 exerts its antimicrobial activity via the carpet model.

SVRMHC prediction server for MHC-binding peptides

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

Harnessing bioinformatics to discover new vaccines

Vaccine design is highly suited to the application of in silico techniques, for both the discovery and development of new and existing vaccines. Here, we discuss computational contributions to epitope mapping and reverse vaccinology, two techniques central to the new discipline of immunomics. Also discussed are methods to improve the efficiency of vaccination, such as codon optimization and adjuvant discovery in addition to the identification of allergenic proteins. We also review current software developed to facilitate vaccine design.

VaxiJen:a server for prediction of protective antigens, tumour antigens and subunit vaccines

Background - Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. Results - Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen. Conclusion - VaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods.

Combining algorithms to predict bacterial protein sub-cellular location:parallel versus concurrent implementations

We describe a novel and potentially important tool for candidate subunit vaccine selection through in silico reverse-vaccinology. A set of Bayesian networks able to make individual predictions for specific subcellular locations is implemented in three pipelines with different architectures: a parallel implementation with a confidence level-based decision engine and two serial implementations with a hierarchical decision structure, one initially rooted by prediction between membrane types and another rooted by soluble versus membrane prediction. The parallel pipeline outperformed the serial pipeline, but took twice as long to execute. The soluble-rooted serial pipeline outperformed the membrane-rooted predictor. Assessment using genomic test sets was more equivocal, as many more predictions are made by the parallel pipeline, yet the serial pipeline identifies 22 more of the 74 proteins of known location.

Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties

Subunit vaccine discovery is an accepted clinical priority. The empirical approach is time- and labor-consuming and can often end in failure. Rational information-driven approaches can overcome these limitations in a fast and efficient manner. However, informatics solutions require reliable algorithms for antigen identification. All known algorithms use sequence similarity to identify antigens. However, antigenicity may be encoded subtly in a sequence and may not be directly identifiable by sequence alignment. We propose a new alignment-independent method for antigen recognition based on the principal chemical properties of protein amino acid sequences. The method is tested by cross-validation on a training set of bacterial antigens and external validation on a test set of known antigens. The prediction accuracy is 83% for the cross-validation and 80% for the external test set. Our approach is accurate and robust, and provides a potent tool for the in silico discovery of medically relevant subunit vaccines.