Environmental scanning electron microscope imaging of vesicle systems

The structural characteristics of liposomes have been widely investigated and there is certainly a strong understanding of their morphological characteristics. Imaging of these systems, using techniques such as freeze-fracturing methods, transmission electron microscopy, and cryo-electron imaging, has allowed us to appreciate their bilayer structures and factors that influence this. However, there are a few methods that study these systems in their natural hydrated state; commonly, the liposomes are visualized after drying, staining and/or fixation of the vesicles. Environmental scanning electron microscopy (ESEM) offers the ability to image a liposome in its hydrated state without the need for prior sample preparation. We were the first to use ESEM to study the liposomes and niosomes, and have been able to dynamically follow the hydration of lipid films and changes in liposome suspensions as water condenses onto, or evaporates from, the sample in real-time. This provides an insight into the resistance of liposomes to coalescence during dehydration, thereby providing an alternative assay for liposome formulation and stability.

Static energy analysis of MHC class I and class II peptide-binding affinity

Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.

The implementation of a design of experiments strategy to increase recombinant protein yields in yeast (review)

Biological processes are subject to the influence of numerous factors and their interactions, which may be non-linear in nature. In a recombinant protein production experiment, understanding the relative importance of these factors, and their influence on the yield and quality of the recombinant protein being produced, is an essential part of its optimisation. In many cases, implementing a design of experiments (DoE) approach has delivered this understanding. This chapter aims to provide the reader with useful pointers in applying a DoE strategy to improve the yields of recombinant yeast cultures.

The effect of antifoam addition on protein production yields

Pichia pastoris is a widely used host for recombinant protein production. The foaming associated with culturing it on a large scale is commonly prevented by the addition of chemical antifoaming agents or "antifoams." Unexpectedly, the addition of a range of antifoams to both shake flask and bioreactor cultures of P. pastoris has been shown to alter the total yield of the recombinant protein being produced. Possible explanations for this are that the presence of the antifoam increases the total amount of protein being produced and secreted per cell or that it increases the density of the culture. Antifoaming agents may therefore have specific effects on the growth and yield characteristics of recombinant cultures, in addition to their primary action as de-foamers.

Which yeast species shall I choose? Saccharomyces cerevisiae versus Pichia pastoris (review)

Having decided on yeast as a production host, the choice of species is often the first question any researcher new to the field will ask. With over 500 known species of yeast to date, this could pose a significant challenge. However, in reality, only very few species of yeast have been employed as host organisms for the production of recombinant proteins. The two most widely used, Saccharomyces cerevisiae and Pichia pastoris, are compared and contrasted here.

Optimising yeast as a host for recombinant protein production (review)

Having access to suitably stable, functional recombinant protein samples underpins diverse academic and industrial research efforts to understand the workings of the cell in health and disease. Synthesising a protein in recombinant host cells typically allows the isolation of the pure protein in quantities much higher than those found in the protein's native source. Yeast is a popular host as it is a eukaryote with similar synthetic machinery to the native human source cells of many proteins of interest, while also being quick, easy, and cheap to grow and process. Even in these cells the production of some proteins can be plagued by low functional yields. We have identified molecular mechanisms and culture parameters underpinning high yields and have consolidated our findings to engineer improved yeast cell factories. In this chapter, we provide an overview of the opportunities available to improve yeast as a host system for recombinant protein production.

The use of site-directed mutagenesis to study GPCRs

G protein coupled receptors (GPCRs) are highly flexible and dynamic proteins, which are able to interact with diverse ligands, effectors, and regulatory proteins. Site-directed mutagenesis (SDM) is a powerful tool for providing insight into how these proteins actually work, both in its own right and when used in conjunction with information provided by other techniques such as crystallography or molecular modelling. Mutagenesis has been used to identify and characterise a myriad of functionally important residues, motifs and domains within the GPCR architecture, and to identify aspects of similarity and differences between the major families of GPCRs. This chapter presents the necessary information for undertaking informative SDM of these proteins. Whilst this is relevant to protein structure/function studies in -general, specific pitfalls and protocols suited to investigating GPCRs in particular will be highlighted.

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.

Entrapment of plasmid DNA vaccines into liposomes by dehydration/rehydration

Intramuscular injection of naked plasmid DNA is known (1-3) to elicit humoral and cell-mediated immune responses against the encoded antigen. It is thought (2,3) that immunity follows DNA uptake by muscle cells, leading to the expression and extracellular release of the antigen which is then taken up by antigen presenting cells (APC). In addition, it is feasible that some of the injected DNA is taken up directly by APC. Disadvantages (1-3) of naked DNA vaccination include: uptake of DNA by only a minor fraction of muscle cells, exposure of DNA to deoxyribonuclease in the interstitial fluid thus necessitating the use of relatively large quantities of DNA, and, in some cases, injection into regenerating muscle in order to enhance immunity. We have recently proposed (1,4) that DNA immunization via liposomes (phospholipid vesicles) could circumvent the need of muscle involvement and instead facilitate (5) uptake of DNA by APC infiltrating the site of injection or in the lymphatics, at the same time protecting DNA from nuclease attack (6). Moreover, transfection of APC with liposomal DNA could be promoted by the judicial choice of vesicle surface charge, size and lipid composition, or by the co-entrapment, together with DNA, of plasmids expressing appropriate cytokines (e.g., interleukin 2), or immunostimulatory sequences.

Setting up a bioreactor for recombinant protein production in yeast

Scale-up from shake flasks to bioreactors allows for the more reproducible, high-yielding production of recombinant proteins in yeast. The ability to control growth conditions through real-time monitoring facilitates further optimization of the process. The setup of a 3-L stirred-tank bioreactor for such an application is described. © 2012 Springer Science+business Media, LLC.

Monitoring of transcriptional dynamics of HIF and NFκB activities

Genetic experiments over the last few decades have identified many regulatory proteins critical for DNA transcription. The dynamics of their transcriptional activities shape the differential expression of the genes they control. Here we describe a simple method, based on the secreted luciferase, to measure the activities of two transcription factors NF?B and HIF. This technique can effectively monitor dynamics of transcriptional events in a population of cells and be up-scaled for high-throughput screening and promoter analysis, making it ideal for data-demanding applications such as mathematical modelling.

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.