The integration of ProxiMAX randomisation with CIS display for the high-throughput production of novel peptides

Saturation mutagenesis is a powerful tool in modern protein engineering. This can allow the analysis of potential new properties thus allowing key residues within a protein to be targeted and randomised. However, the creation of large libraries using conventional saturation mutagenesis with degenerate codons (NNN or NNK) has inherent redundancy and disparities in residue representation. In this we describe the combination of ProxiMAX randomisation and CIS display for the use of generating novel peptides. Unlike other methods ProxiMAX randomisation does not require any intricate chemistry but simply utilises synthetic DNA and molecular biology techniques. Designed ‘MAX’ oligonucleotides were ligated, amplified and digested in an iterative cycle. Results show that randomised ‘MAX’ codons can be added sequentially to the base sequence creating a series of randomised non-degenerate codons that can subsequently be inserted into a gene. CIS display (Isogencia, UK) is an in vitro DNA based screening method that creates a genotype to phenotype link between a peptide and the nucleic acid that encodes it. The use of straight forward in vitro transcription/translation and other molecular biology techniques permits ease of use along with flexibility making it a potent screening technique. Using ProxiMAX randomisation in combination with CIS display, the aim is to produce randomised anti-nerve growth factor (NGF) and calcitonin gene-related (CGRP) peptides to demonstrate the high-throughput nature of this combination.

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.

Antagonistic effects of two novel GIP analogs, (Hyp3)GIP and (Hyp3)GIPLys16PAL, on the biological actions of GIP and longer-term effects in diabetic ob/ob mice

This study examines the actions of the novel enzyme-resistant, NH 2-terminally modified GIP analog (Hyp3)GIP and its fatty acid-derivatized analog (Hyp3)GIPLys16PAL. Acute effects are compared with the established GIP receptor antagonist (Pro3)GIP. All three peptides exhibited DPP IV resistance, and significantly inhibited GIP stimulated cAMP formation and insulin secretion in GIP receptor-transfected fibroblasts and in clonal pancreatic BRIN-BD11 cells, respectively. Likewise, in obese diabetic ob/ob mice, intraperitoneal administration of GIP analogs significantly inhibited the acute antihyperglycemic and insulin-releasing effects of native GIP. Administration of once daily injections of (Hyp 3)GIP or (Hyp3)GIPLys16PAL for 14 days resulted in significantly lower plasma glucose levels (P < 0.05) after (Hyp 3)GIP on days 12 and 14 and enhanced glucose tolerance (P < 0.05) and insulin sensitivity (P < 0.05 to P < 0.001) in both groups by day 14. Both (Hyp3)GIP and (Hyp3)GIPLys16PAL treatment also reduced pancreatic insulin (P < 0.05 to P < 0.01) without affecting islet number. These data indicate that (Hyp3)GIP and (Hyp 3)GIPLys16PAL function as GIP receptor antagonists with potential for ameliorating obesity-related diabetes. Acylation of (Hyp 3)GIP to extend bioactivity does not appear to be of any additional benefit. Copyright © 2007 the American Physiological Society.

Transporter associated with antigen processing preselection of peptides binding to the MHC:a bioinformatic evaluation

TAP is responsible for the transit of peptides from the cytosol to the lumen of the endoplasmic reticulum. In an immunological context, this event is followed by the binding of peptides to MHC molecules before export to the cell surface and recognition by T cells. Because TAP transport precedes MHC binding, TAP preferences may make a significant contribution to epitope selection. To assess the impact of this preselection, we have developed a scoring function for TAP affinity prediction using the additive method, have used it to analyze and extend the TAP binding motif, and have evaluated how well this model acts as a preselection step in predicting MHC binding peptides. To distinguish between MHC alleles that are exclusively dependent on TAP and those exhibiting only a partial dependence on TAP, two sets of MHC binding peptides were examined: HLA-A*0201 was selected as a representative of partially TAP-dependent HLA alleles, and HLA-A*0301 represented fully TAP-dependent HLA alleles. TAP preselection has a greater impact on TAP-dependent alleles than on TAP-independent alleles. The reduction in the number of nonbinders varied from 10% (TAP-independent) to 33% (TAP-dependent), suggesting that TAP preselection is an important component in the successful in silico prediction of T cell epitopes.

Coupling in silico and in vitro analysis of peptide-MHC binding:a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.

Designing immunogenic peptides

Peptides fulfill many roles in immunology, yet none are more important than their role as immunogenic epitopes driving the adaptive immune response, our ultimate bulwark against infectious disease. Peptide epitopes are mediated primarily by their interaction with major histocompatibility complexes (T-cell epitopes) and antibodies (B-cell epitopes). As pathogen genomes continue to be revealed, both experimental and computational epitope mapping are becoming crucial tools in vaccine discovery1,2. Immunoinformatics offers many tools, techniques and approaches for in silico epitope characterization, which is capable of greatly accelerating epitope design. © 2013 Nature America, Inc. All rights reserved.

The integration of ProxiMAX randomisation with CIS display for the production of novel peptides

Saturation mutagenesis is a powerful tool in modern protein engineering, which permits key residues within a protein to be targeted in order to potentially enhance specific functionalities. However, the creation of large libraries using conventional saturation mutagenesis with degenerate codons (NNN or NNK/S) has inherent redundancy and consequent disparities in codon representation. Therefore, both chemical (trinucleotide phosphoramidites) and biological methods (sequential, enzymatic single codon additions) of non-degenerate saturation mutagenesis have been developed in order to combat these issues and so improve library quality. Large libraries with multiple saturated positions can be limited by the method used to screen them. Although the traditional screening method of choice, cell-dependent methods, such as phage display, are limited by the need for transformation. A number of cell-free screening methods, such as CIS display, which link the screened phenotype with the encoded genotype, have the capability of screening libraries with up to 1014 members. This thesis describes the further development of ProxiMAX technology to reduce library codon bias and its integration with CIS display to screen the resulting library. Synthetic MAX oligonucleotides are ligated to an acceptor base sequence, amplified, and digested, subsequently adding a randomised codon to the acceptor, which forms an iterative cycle using the digested product of the previous cycle as the base sequence for the next. Initial use of ProxiMAX highlighted areas of the process where changes could be implemented in order to improve the codon representation in the final library. The refined process was used to construct a monomeric anti-NGF peptide library, based on two proprietary dimeric peptides (Isogenica) that bind NGF. The resulting library showed greatly improved codon representation that equated to a theoretical diversity of ~69%. The library was subsequently screened using CIS display and the discovered peptides assessed for NGF-TrkA inhibition by ELISA. Despite binding to TrkA, these peptides showed lower levels of inhibition of the NGF-TrkA interaction than the parental dimeric peptides, highlighting the importance of dimerization for inhibition of NGF-TrkA binding.