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.

Measurement of apoptotic cell clearance in vitro

The phagocytic clearance of apoptotic cells is a highly efficient and nonphlogistic process in vivo. Research in this area has been limited, at least in part, by technical difficulties associated with the techniques used in the detailed study of apoptotic cell clearance mechanisms. This chapter provides details of methods that may be used to study apoptotic cell clearance in vitro. Such methods have been used successfully to identify phagocyte-associated or apoptotic cell-associated molecular players in the recognition process.

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.

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.

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.

Large-scale production of secreted proteins in Pichia pastoris

The production of recombinant therapeutic proteins is an active area of research in drug development. These bio-therapeutic drugs target nearly 150 disease states and promise to bring better treatments to patients. However, if new bio-therapeutics are to be made more accessible and affordable, improvements in production performance and optimization of processes are necessary. A major challenge lies in controlling the effect of process conditions on production of intact functional proteins. To achieve this, improved tools are needed for bio-processing. For example, implementation of process modeling and high-throughput technologies can be used to achieve quality by design, leading to improvements in productivity. Commercially, the most sought after targets are secreted proteins due to the ease of handling in downstream procedures. This chapter outlines different approaches for production and optimization of secreted proteins in the host Pichia pastoris. © 2012 Springer Science+business Media, LLC.

Optimising Pichia pastoris induction

A common method for inducing the production of recombinant proteins in Pichia pastoris is through the use of methanol. However, the by-products of methanol metabolism are toxic to yeast cells and therefore its addition to recombinant cultures must be controlled and monitored throughout the process in order to maximise recombinant protein yields. Described here are online and off-line methods to monitor and control methanol addition to bench-top-scale bioreactors. © 2012 Springer Science+business Media, LLC.

Detergent-free membrane protein purification

Membrane proteins are localised within a lipid bilayer; in order to purify them for functional and structural studies the first step must involve solubilising or extracting the protein from these lipids. To date this has been achieved using detergents which disrupt the bilayer and bind to the protein in the transmembrane region. However finding conditions for optimal extraction, without destabilising protein structure is time consuming and expensive. Here we present a recently-developed method using a styrene maleic acid (SMA) co-polymer instead of detergents. The SMA co-polymer extracts membrane proteins in a small disc of lipid bilayer which can be used for affinity chromatography purification, thus enabling the purification of membrane proteins while maintaining their native lipid bilayer environment.

Membrane protein production in the Yeast, S. cerevisiae

The first crystal structures of recombinant mammalian membrane proteins were solved in 2005 using protein that had been produced in yeast cells. One of these, the rabbit Ca2+-ATPase SERCA1a, was synthesized in Saccharomyces cerevisiae. All host systems have their specific advantages and disadvantages, but yeast has remained a consistently popular choice in the eukaryotic membrane protein field because it is quick, easy and cheap to culture, whilst being able to post-translationally process eukaryotic membrane proteins. Very recent structures of recombinant membrane proteins produced in S. cerevisiae include those of the Arabidopsis thaliana NRT1.1 nitrate transporter and the fungal plant pathogen lipid scramblase, TMEM16. This chapter provides an overview of the methodological approaches underpinning these successes.

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.

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 which can influence this. However, there are few methods which all us to 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. Within our studies we were the first to use ESEM to study liposomes and niosomes and we have been able to dynamically follow the hydration of lipid films and changes in liposome suspensions as water condenses on to, or evaporates from, the sample in real time. This provides insight into the resistance of liposomes to coalescence during dehydration, thereby providing an alternative assay of liposome formulation and stability.