Random Peptide Sequences Research Articles

Peptide based diagnostics: Are random-sequence peptides more useful than tiling proteome sequences?

Diagnostics using peptide ligands have been available for decades. However, their adoption in diagnostics has been limited, not because of poor sensitivity but in many cases due to diminished specificity. Numerous reports suggest that protein-based rather than peptide-based disease detection is more specific. We examined two different approaches to peptide-based diagnostics using Coccidioides (aka Valley Fever) as the disease model. Although the pathogen was discovered more than a century ago, a highly sensitive diagnostic remains unavailable. We present a case study where two different approaches to diagnosing Valley Fever were used: first, overlapping Valley Fever epitopes representing immunodominant Coccidioides antigens were tiled using a microarray format of presynthesized peptides. Second, a set of random sequence peptides identified using a 10,000 peptide immunosignaturing microarray was compared for sensitivity and specificity. The scientific hypothesis tested was that actual epitope peptides from Coccidioides would provide sufficient sensitivity and specificity as a diagnostic. Results demonstrated that random sequence peptides exhibited higher accuracy when classifying different stages of Valley Fever infection vs. epitope peptides. The epitope peptide array did provide better performance than the existing immunodiffusion array, but when directly compared to the random sequence peptides, reported lower overall accuracy. This study suggests that there are competing aspects of antibody recognition that involve conservation of pathogen sequence and aspects of mimotope recognition and amino acid substitutions. These factors may prove critical when developing the next generation of high-performance immunodiagnostics.

Development of a mimotope vaccine targeting the Staphylococcus aureus quorum sensing pathway.

A major hurdle in vaccine development is the difficulty in identifying relevant target epitopes and then presenting them to the immune system in a context that mimics their native conformation. We have engineered novel virus-like-particle (VLP) technology that is able to display complex libraries of random peptide sequences on a surface-exposed loop in the coat protein without disruption of protein folding or VLP assembly. This technology allows us to use the same VLP particle for both affinity selection and immunization, integrating the power of epitope discovery and epitope mimicry of traditional phage display with the high immunogenicity of VLPs. Previously, we showed that using affinity selection with our VLP platform identifies linear epitopes of monoclonal antibodies and subsequent immunization generates the proper antibody response. To test if our technology could identify immunologic mimotopes, we used affinity selection on a monoclonal antibody (AP4-24H11) that recognizes the Staphylococcus aureus autoinducing peptide 4 (AIP4). AIP4 is a secreted eight amino acid, cyclized peptide produced from the S. aureus accessory gene regulator (agrIV) quorum-sensing operon. The agr system coordinates density dependent changes in gene expression, leading to the upregulation of a host of virulence factors, and passive transfer of AP4-24H11 protects against S. aureus agrIV-dependent pathogenicity. In this report, we identified a set of peptides displayed on VLPs that bound with high specificity to AP4-24H11. Importantly, similar to passive transfer with AP4-24H11, immunization with a subset of these VLPs protected against pathogenicity in a mouse model of S. aureus dermonecrosis. These data are proof of principle that by performing affinity selection on neutralizing antibodies, our VLP technology can identify peptide mimics of non-linear epitopes and that these mimotope based VLP vaccines provide protection against pathogens in relevant animal models.

A malaria vaccine candidate based on an epitope of the Plasmodium falciparum RH5 protein.

BackgroundThe Plasmodium falciparum protein RH5 is an adhesin molecule essential for parasite invasion of erythrocytes. Recent studies show that anti-PfRH5 sera have potent invasion-inhibiting activities, supporting the idea that the PfRH5 antigen could form the basis of a vaccine. Therefore, epitopes recognized by neutralizing anti-PfRH5 antibodies could themselves be effective vaccine immunogens if presented in a sufficiently immunogenic fashion. However, the exact regions within PfRH5 that are targets of this invasion-inhibitory activity have yet to be identified.MethodsA battery of anti-RH5 monoclonal antibodies (mAbs) were produced and screened for their potency by inhibition of invasion assays in vitro. Using an anti-RH5 mAb that completely inhibited invasion as the selecting mAb, affinity-selection using random sequence peptide libraries displayed on virus-like particles of bacteriophage MS2 (MS2 VLPs) was performed. VLPs were sequenced to identify the specific peptide epitopes they encoded and used to raise specific antisera that was in turn tested for inhibition of invasion.ResultsThree anti-RH5 monoclonals (0.1 mg/mL) were able to inhibit invasion in vitro by >95%. Affinity-selection with one of these mAbs yielded a VLP which yielded a peptide whose sequence is identical to a portion of PfRH5 itself. The VLP displaying the peptide binds strongly to the antibody, and in immunized animals elicits an anti-PfRH5 antibody response. The resulting antisera against the specific VLP inhibit parasite invasion of erythrocytes more than 90% in vitro.ConclusionsHere, data is presented from an anti-PfRH5 mAb that completely inhibits erythrocyte invasion by parasites in vitro, one of the few anti-malarial monoclonal antibodies reported to date that completely inhibits invasion with such potency, adding to other studies that highlight the potential of PfRH5 as a vaccine antigen. The specific neutralization sensitive epitope within RH5 has been identified, and antibodies against this epitope also elicit high anti-invasion activity, suggesting this epitope could form the basis of an effective vaccine against malaria.

Ribosome display enhanced by next generation sequencing: A tool to identify antibody-specific peptide ligands

Detection of antibodies in serum has many important applications. Our goal was to develop a facile general experimental approach for identifying antibody-specific peptide ligands that could be used as the reagents for antibody detection. Our emphasis was on an approach that would allow identification of peptide ligands for antibodies in serum without the need to isolate the target antibody or to know the identity of its antigen. We combined ribosome display (RD) with the analysis of peptide libraries by next generation sequencing (NGS) of their coding RNA to facilitate identification of antibody-specific peptide ligands from random sequence peptide library. We first demonstrated, using purified antibodies, that with our approach-specific peptide ligands for antibodies with simple linear epitopes, as well as peptide mimotopes for antibodies recognizing complex epitopes, were readily identified. Inclusion of NGS analysis reduced the number of RD selection rounds that were required to identify specific ligands and facilitated discrimination between specific and spurious nonspecific sequences. We then used a model of human serum spiked with a known target antibody to develop NGS-based analysis that allowed identification of specific ligands for a target antibody in the context of an overwhelming amount of unrelated immunoglobins present in serum.

Application of immunosignatures for diagnosis of valley fever.

Valley fever (VF) is difficult to diagnose, partly because the symptoms of VF are confounded with those of other community-acquired pneumonias. Confirmatory diagnostics detect IgM and IgG antibodies against coccidioidal antigens via immunodiffusion (ID). The false-negative rate can be as high as 50% to 70%, with 5% of symptomatic patients never showing detectable antibody levels. In this study, we tested whether the immunosignature diagnostic can resolve VF false negatives. An immunosignature is the pattern of antibody binding to random-sequence peptides on a peptide microarray. A 10,000-peptide microarray was first used to determine whether valley fever patients can be distinguished from 3 other cohorts with similar infections. After determining the VF-specific peptides, a small 96-peptide diagnostic array was created and tested. The performances of the 10,000-peptide array and the 96-peptide diagnostic array were compared to that of the ID diagnostic standard. The 10,000-peptide microarray classified the VF samples from the other 3 infections with 98% accuracy. It also classified VF false-negative patients with 100% sensitivity in a blinded test set versus 28% sensitivity for ID. The immunosignature microarray has potential for simultaneously distinguishing valley fever patients from those with other fungal or bacterial infections. The same 10,000-peptide array can diagnose VF false-negative patients with 100% sensitivity. The smaller 96-peptide diagnostic array was less specific for diagnosing false negatives. We conclude that the performance of the immunosignature diagnostic exceeds that of the existing standard, and the immunosignature can distinguish related infections and might be used in lieu of existing diagnostics.

Diagnosis and early detection of CNS-SLE in MRL/lpr mice using peptide microarrays.

BackgroundAn accurate method that can diagnose and predict lupus and its neuropsychiatric manifestations is essential since currently there are no reliable methods. Autoantibodies to a varied panel of antigens in the body are characteristic of lupus. In this study we investigated whether serum autoantibody binding patterns on random-sequence peptide microarrays (immunosignaturing) can be used for diagnosing and predicting the onset of lupus and its central nervous system (CNS) manifestations. We also tested the techniques for identifying potentially pathogenic autoantibodies in CNS-Lupus. We used the well-characterized MRL/lpr lupus animal model in two studies as a first step to develop and evaluate future studies in humans.ResultsIn study one we identified possible diagnostic peptides for both lupus and altered behavior in the forced swim test. When comparing the results of study one to that of study two (carried out in a similar manner), we further identified potential peptides that may be diagnostic and predictive of both lupus and altered behavior in the forced swim test. We also characterized five potentially pathogenic brain-reactive autoantibodies, as well as suggested possible brain targets.ConclusionsThese results indicate that immunosignaturing could predict and diagnose lupus and its CNS manifestations. It can also be used to characterize pathogenic autoantibodies, which may help to better understand the underlying mechanisms of CNS-Lupus.

In vitro affinity screening of protein and peptide binders by megavalent bead surface display.

The advent of protein display systems has provided access to tailor-made protein binders by directed evolution. We introduce a new in vitro display system, bead surface display (BeSD), in which a gene is mounted on a bead via strong non-covalent (streptavidin/biotin) interactions and the corresponding protein is displayed via a covalent thioether bond on the DNA. In contrast to previous monovalent or low-copy bead display systems, multiple copies of the DNA and the protein or peptide of interest are displayed in defined quantities (up to 106 of each), so that flow cytometry can be used to obtain a measure of binding affinity. The utility of the BeSD in directed evolution is validated by library selections of randomized peptide sequences for binding to the anti-hemagglutinin (HA) antibody that proceed with enrichments in excess of 103 and lead to the isolation of high-affinity HA-tags within one round of flow cytometric screening. On-bead Kd measurements suggest that the selected tags have affinities in the low nanomolar range. In contrast to other display systems (such as ribosome, mRNA and phage display) that are limited to affinity panning selections, BeSD possesses the ability to screen and rank binders by their affinity in vitro, a feature that hitherto has been exclusive to in vivo multivalent cell display systems (such as yeast display).

Abstract 4919: Myristoylated alanine-rich C kinase substrate (MARCKS) phosphorylation potentiates human lung cancer cell malignancy .

Abstract Myristoylated alanine-rich C kinase substrate (MARCKS), a substrate of protein kinase C (PKC), is a key regulatory molecule controlling mucin secretion by airway epithelial cells in vitro and in vivo. Phosphorylated MARCKS (p-MARCKS) has been reportedly to promote inflammatory cell migration in several lung diseases. However, the functionality of MARCKS and its related phosphorylation in lung cancer malignancy has not been characterized. This study demonstrated MARCKS expression and elevated p-MARCKS in various lung cancer cell lines with high malignancy, such as CL1-0-F3, CL1-5, PC9, A549 and several tyrosine kinase inhibitor (TKI) resistant cell lines (H1650 and H1975), as compared with normal human bronchial epithelial (NHBE) cells and low metastasis cancer cell line. siRNA knockdown MARCKS expression resulted in an inhibition of cell migration, accompanying with a decrease of Slug expression in these malignant lung cancer cell lines. Treatment with myristoylated 24-amino-acid MARCKS N-terminus sequence (MANS) peptide, but not the control myristoylated random N-terminal sequence (RNS) peptide, suppressed MARCKS phosphorylation and impaired cell migration and wound healing ability in these lung cancer cell lines. Importantly, MANS peptide treatment resulted in a suppression of lamellipodia/filopodia formation and an enhancement of cell-cell contacts with increased E-cadherin expression. These results confirmed a crucial role for MARCKS, specifically its phosphorylation, in potentiating lung cancer cell migration and malignancy. This study has also suggested a potential usage of MANS or the MARCKS-related peptides in the treatment of lung cancer metastasis. Citation Format: Ching-Hsien Chen, Phillip Thai, Reen Wu. Myristoylated alanine-rich C kinase substrate (MARCKS) phosphorylation potentiates human lung cancer cell malignancy . [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4919. doi:10.1158/1538-7445.AM2013-4919

A Technology for Developing Synbodies with Antibacterial Activity

The rise in antibiotic resistance has led to an increased research focus on discovery of new antibacterial candidates. While broad-spectrum antibiotics are widely pursued, there is evidence that resistance arises in part from the wide spread use of these antibiotics. Our group has developed a system to produce protein affinity agents, called synbodies, which have high affinity and specificity for their target. In this report, we describe the adaptation of this system to produce new antibacterial candidates towards a target bacterium. The system functions by screening target bacteria against an array of 10,000 random sequence peptides and, using a combination of membrane labeling and intracellular dyes, we identified peptides with target specific binding or killing functions. Binding and lytic peptides were identified in this manner and in vitro tests confirmed the activity of the lead peptides. A peptide with antibacterial activity was linked to a peptide specifically binding Staphylococcus aureus to create a synbody with increased antibacterial activity. Subsequent tests showed that this peptide could block S. aureus induced killing of HEK293 cells in a co-culture experiment. These results demonstrate the feasibility of using the synbody system to discover new antibacterial candidate agents.

Immunosignaturing Microarrays Distinguish Antibody Profiles of Related Pancreatic Diseases

Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. Profiles of the antibody repertoire produced during infection or during long-term chronic disease have proven to be informative for disease classification. An important unanswered question relative to this technology is whether different diseases that target the same organ and result in similar early phenotypes have similar or distinguishable immunosignatures. This question is of clinical relevance when considering patients who present with similar symptoms early during their disease. The pancreas is one such organ; diseases that affects this organ can cause the patient both broad and acute distress, with little to distinguish the disease source. If the cause were made clear without biopsy, and could be accomplished during routine monitoring, earlier intervention could improve health. Pancreatic cancer, chronic or acute pancreatitis, diabetes mellitus, hepatitis B or C infection, and other diseases can deeply affect the function of the pancreas, complicating diagnosis. We tested the immunosignaturing platform for its ability to resolve four different diseases that target the same organ; pancreatic cancer, pre-pancreatic cancer (panIN), type II diabetes and acute pancreatitis. These diseases were separated with >90% specificity from controls and from each other. We also describe a mathematical method that allows identification of 3 distinct components of an immunosignature: disease specific, ‘housekeeping’ and patient specific variation. The first component is useful in diagnosing disease, the second for baseline for the technology and third for monitoring changes in a healthy individual over time.

Feasibility of an early Alzheimer's disease immunosignature diagnostic test

A practical diagnostic test is needed for early Alzheimer's disease (AD) detection. Immunosignaturing, a technology that employs antibody binding to a random-sequence peptide microarray, generates profiles that distinguish transgenic mice engineered with familial AD mutations (APPswe/PSEN1-dE9) from non-transgenic littermates. It can also detect an AD-like signature in humans. Here, we assess the changes in the immunosignature at different time points of the disease in mice and humans. We also evaluate the accuracy of the late-stage signature as a test to discriminate between young mice with familial AD mutations from non-transgenic littermates. Plasma samples from AD patients were assayed 3–12months apart, while APPswe/PSEN1-dE9 and non-transgenic controls supplied plasma at monthly intervals until they reached 15months of age. Microarrays with 10,000 random-sequence peptides were used to compare antibody binding patterns. These patterns gradually changed over the life-span of mice. Strong, characteristic signatures were observed in transgenic mice at early, mid and late stages, but these profiles had minimal overlap. The signature of young transgenic mice had an error rate of 18% at classifying plasma samples from late-stage transgenic mice. Conversely, the late-stage transgenic mice signature discriminated between young transgenic mice and littermates with an error rate of 21%. Less distinctive profiles were recognizable throughout the transgenic mice lifespan, being detectable as early as 2months. The human signature had minimal change on short-term follow-up. Our results call for a reappraisal of the way incipient AD is studied, as biomarkers seen in late-stages of the disease may not be relevant in earlier stages.

Comparative study of classification algorithms for immunosignaturing data

BackgroundHigh-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.ResultsWe characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found ‘Naïve Bayes’ far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy.Conclusions‘Naïve Bayes’ algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.

KDEL Peptide Gold Nanostructures: A Promising Platform for Drug Delivery

The objective was to elucidate the mechanisms of cellular uptake and trafficking of peptide gold nanostructures (Au‐Pep). Peptide A (PepA) contains the carboxy‐terminal sequence Lys‐Asp‐Glu‐Leu (KDEL). Peptide B (PepB) is a random sequence peptide with no known receptor‐binding activity. Au‐Pep were incubated with Sol8 cells for 15 mins in DMEM without serum, followed by a 1 h chase period in DMEM plus serum. Cellular compartments including clathrin‐coated vesicles, Golgi apparatus and endoplasmic reticulum (ER) were labeled for peptide colocalization studies. In a subsequent study, cells were transfected to overexpress KDEL receptor (KDELR), followed by Au‐Pep incubation. Cell imaging was performed using confocal fluorescence microscopy. The results showed that free PepA uptake was significantly greater in KDELR overexpressing cells, whereas there was no difference in PepB uptake, suggesting that free PepA was internalized via KDELR‐mediated endocytosis. However, Au‐Pep uptake was similar in KDELR overexpressing cells and controls, suggesting that KDELR‐mediated endocytosis was not predominant for Au‐Pep internalization. Colocalization data showed that for all peptide treatments, most localized in the ER. This may suggest that these peptides are trafficked via a retrograde coat protein complex I mediated transport pathway. In conclusion, Au‐PepA may provide a nanoplatform for drug delivery. This work was supported by the University of Idaho Blue Ribbon BANTech Initiative.

A minimal model of peptide binding predicts ensemble properties of serum antibodies

BackgroundThe importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings.ResultsMultivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones.ConclusionsOur results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.

Physical Characterization of the “Immunosignaturing Effect”

Identifying new, effective biomarkers for diseases is proving to be a challenging problem. We have proposed that antibodies may offer a solution to this problem. The physical features and abundance of antibodies make them ideal biomarkers. Additionally, antibodies are often elicited early in the ontogeny of different chronic and infectious diseases. We previously reported that antibodies from patients with infectious disease and separately those with Alzheimer's disease display a characteristic and reproducible “immunosignature” on a microarray of 10,000 random sequence peptides. Here we investigate the physical and chemical parameters underlying how immunosignaturing works. We first show that a variety of monoclonal and polyclonal antibodies raised against different classes of antigens produce distinct profiles on this microarray and the relative affinities are determined. A proposal for how antibodies bind the random sequences is tested. Sera from vaccinated mice and people suffering from a fugal infection are individually assayed to determine the complexity of signals that can be distinguished. Based on these results, we propose that this simple, general and inexpensive system could be optimized to generate a new class of antibody biomarkers for a wide variety of diseases.

GuiTope: an application for mapping random-sequence peptides to protein sequences

BackgroundRandom-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task.ResultsGuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance.ConclusionsGuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.

High-Throughput Platform for Rapid Deployment of Antimicrobial Agents

A new approach to conducting bacterial binding assays by using an addressable high density random sequence peptide microarray is described. When bacterial binding is carried out in the presence of a competing excess of corresponding bacterial lipopolysaccharide (LPS), most of the observed bacterial binding is inhibited, suggesting that LPS is the major target of the bacterial binding peptides. Importantly, the amino acid composition of the selected peptides closely resembles the composition of natural antimicrobial peptides. Conjugation of selected peptides to polyvalent nanoparticle scaffold yields constructs that show potent antibacterial agglutination activities. The system is general enough to potentially create antimicrobial agents to virtually any pathogen.

Application of immunosignatures to the assessment of Alzheimer's disease

Accurate assessment of Alzheimer's disease (AD), both presymptomatically and at different disease stages, will become increasingly important with the expanding elderly population. There are a number of indications that the immune system is engaged in AD. Here we explore the ability of an antibody-profiling technology to characterize AD and screen for peptides that may be used for a simple diagnostic test. We developed an array-based system to profile the antibody repertoire of transgenic mice with cerebral amyloidosis (TG) and elderly individuals with or without AD. The array consists of 10,000 random sequence peptides (20-mers) capable of detecting antibody binding patterns, allowing the identification of peptides that mimic epitopes targeted by a donor's serum. TG mice exhibited a distinct immunoprofile compared to nontransgenic littermates. Further, we show that dementia patients, including autopsy-confirmed AD subjects, have distinguishable profiles compared to age-matched nondemented people. Using antibodies to different forms of Aβ peptide and blocking protocols, we demonstrate that most of this signature is not due to the subject's antibodies raised against Aβ. We propose that "immunosignaturing" technology may have potential as a diagnostic tool in AD.

Peptide Epitope Identification by Affinity Selection on Bacteriophage MS2 Virus-Like Particles

Filamentous phages are now the most widely used vehicles for phage display and provide efficient means for epitope identification. However, the peptides they display are not very immunogenic because they normally fail to present foreign epitopes at the very high densities required for efficient B-cell activation. Meanwhile, systems based on virus-like particles (VLPs) permit the engineered high-density display of specific epitopes but are incapable of peptide library display and affinity selection. We developed a new peptide display platform based on VLPs of the RNA bacteriophage MS2. It combines the high immunogenicity of MS2 VLPs with the affinity selection capabilities of other phage display systems. Here, we describe plasmid vectors that facilitate the construction of high-complexity random sequence peptide libraries on MS2 VLPs and that allow control of the stringency of affinity selection through the manipulation of display valency. We used the system to identify epitopes for several previously characterized monoclonal antibody targets and showed that the VLPs thus obtained elicit antibodies in mice whose activities mimic those of the selecting antibodies.

Exploring Antibody Recognition of Sequence Space through Random-Sequence Peptide Microarrays

A universal platform for efficiently mapping antibody epitopes would be of great use for many applications, ranging from antibody therapeutic development to vaccine design. Here we tested the feasibility of using a random peptide microarray to map antibody epitopes. Although peptide microarrays are physically constrained to ∼10(4) peptides per array, compared with 10(8) permitted in library panning approaches such as phage display, they enable a much more high though put and direct measure of binding. Long (20 mer) random sequence peptides were chosen for this study to look at an unbiased sampling of sequence space. This sampling of sequence space is sparse, as an exact epitope sequence is unlikely to appear. Commercial monoclonal antibodies with known linear epitopes or polyclonal antibodies raised against engineered 20-mer peptides were used to evaluate this array as an epitope mapping platform. Remarkably, peptides with the most sequence similarity to known epitopes were only slightly more likely to be recognized by the antibody than other random peptides. We explored the ability of two methods singly and in combination to predict the actual epitope from the random sequence peptides bound. Though the epitopes were not directly evident, subtle motifs were found among the top binding peptides for each antibody. These motifs did have some predictive ability in searching for the known epitopes among a set of decoy sequences. The second approach using a windowing alignment strategy, was able to score known epitopes of monoclonal antibodies well within the test dataset, but did not perform as well on polyclonals. Random peptide microarrays of even limited diversity may serve as a useful tool to prioritize candidates for epitope mapping or antigen identification.