Bioinformatic requirements for protein database searching using predicted epitopes from disease-associated antibodies

Comparison of motif-based and whole-unique-sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera

Detection of protection-associated epitopes via reverse vaccinology is the first step for development of subunit vaccines against microbial pathogens. Mapping subunit vaccine targets requires high throughput methods, which would allow delineation of epitopes recognized by protective antibodies on a large scale. Phage displayed random peptide library coupled to Next Generation Sequencing (PDRPL/NGS) is the universal platform that enables high-yield identification of peptides that mimic epitopes (mimotopes). Despite being unsurpassed as a tool for discovery of polyclonal serum mimotopes, the PDRPL/NGS is far inferior as a quantitative method of immune response. Difficult-to-control fluctuations in amounts of antibody-bound phages after rounds of selection and amplification diminish the quantitative capacity of the PDRPL/NGS. In an attempt to improve the accuracy of the PDRPL/NGS method, we compared the discriminating capacity of two approaches for PDRPL/NGS data analysis. The whole-unique-sequence-based analysis (WUSA) involved generation of 7-mer peptide profiles and comparison of the numbers of sequencing reads for unique peptide sequences between serum samples. The motif-based analysis (MA) included identification of 4-mer consensus motifs unifying unique 7-mer sequences and comparison of motifs between serum samples. The motif comparison was based not on the numbers of sequencing reads, but on the numbers of distinct 7-mers constituting the motifs. Our PDRPL/NGS datasets generated from biopanning of protective and non-protective anti-Borrelia burgdorferi sera of New Zealand rabbits were used to contrast the two approaches. As a result, the principle component analyses (PCA) showed that the discriminating powers of the WUSA and MA were similar. In contrast, the unsupervised hierarchical clustering obtained via the MA classified the preimmune, non-protective, and protective sera better than the WUSA-based clustering. Also, a total number of discriminating motifs was higher than that of discriminating 7-mers. In sum, our results indicate that MA approach improves the accuracy and quantitative capacity of the PDRPL/NGS method.

A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes

Antibodies are essential to functional immunity, yet the epitopes targeted by antibody repertoires remain largely uncharacterized. To aid in characterization, we developed a generalizable strategy to predict antibody-binding epitopes within individual proteins and entire proteomes. Specifically, we selected antibody-binding peptides for 273 distinct sera out of a random library and identified the peptides using next-generation sequencing. To predict antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the sequences of candidate antigens into short overlapping subsequences of length k (k-mers). We used the enrichment over background of these k-mers in the antibody-binding peptide dataset to predict antibody-binding epitopes. As a positive control, we used this approach, termed K-mer Tiling of Protein Epitopes (K-TOPE), to predict epitopes targeted by monoclonal and polyclonal antibodies of well-characterized specificity, accurately recovering their known epitopes. K-TOPE characterized a commonly targeted antigen from Rhinovirus A, predicting four epitopes recognized by antibodies present in 87% of sera (n = 250). An analysis of 2,908 proteins from 400 viral taxa that infect humans predicted seven enterovirus epitopes and five Epstein-Barr virus epitopes recognized by >30% of specimens. Analysis of Staphylococcus and Streptococcus proteomes similarly predicted 22 epitopes recognized by >30% of specimens. Twelve of these common viral and bacterial epitopes agreed with previously mapped epitopes with p-values < 0.05. Additionally, we predicted 30 HSV2-specific epitopes that were 100% specific against HSV1 in novel and previously reported antigens. Experimentally validating these candidate epitopes could help identify diagnostic biomarkers, vaccine components, and therapeutic targets. The K-TOPE approach thus provides a powerful new tool to elucidate the organisms, antigens, and epitopes targeted by human antibody repertoires.

High-titer antibody depletion enhances discovery of diverse serum antibody specificities

The human antibody repertoire is a unique repository of information regarding infection, inflammation, and autoimmunity of the past, present, and future. However, antibodies can span vast ranges of concentrations with varying affinities and the repertoire is often heavily polarized by a few species. These complexities lead to difficulties detecting and characterizing low abundance antibody species that may be relevant to disease. We therefore developed a method to selectively remove antibodies from a sample in proportion to the titer of the species prior to analysis, referred to as high-titer depletion (HTD). Peptides from a large random peptide display library were enriched towards binding high-titer antibody species and utilized as binding reagents to deplete the corresponding species from the specimen. HTD enabled the discovery of antibody binding specificities using random peptide library screening with reduced cross-reactivity and background signal and improved coverage of low abundance species. With HTD, three monoclonal antibody species were detected at concentrations at least an order of magnitude lower than without HTD. Additionally, 92 serum antibody specificities were readily discovered from an individual specimen using HTD compared to only 25 specificities without HTD. Parameters affecting the extent of depletion such as the concentration of depleted serum were also adjusted to reproducibly improve the coverage of antibody specificities. These results demonstrate that HTD could be employed for the discovery of rare specificities related to disease and enable extensive characterization of the antibody repertoire. Moreover, the strategy of depletion in proportion to titer could be extended to other applications with complex biological samples to improve discovery.

Identification of the immunogenic epitopes of the whole venom component of the Hemiscorpius lepturus scorpion using the phage display peptide library

The venom of the Hemiscorpius lepturus scorpion contains mixtures of bioactive compounds that disturb biochemical and physiological functions of the victims. Hemiscorpius lepturus envenomation is recognized as a serious health concern in tropical regions. So far, there is no preventive procedure, and the main focus is on treatment of victims with an antiserum purified from hyper-immunized horses. Although antisera can neutralize the venom, they, in some cases, lead to anaphylactic shock and even death. Selection of peptides mimicking antigenic and immunogenic epitopes of toxins from random peptide libraries is a novel approach for the development of recombinant toxins and poly-epitopic vaccine. To achieve this aim, a phage display peptide library and three rounds of biopanning were performed on immobilized antibodies (IgGs) purified from the sera of hyper-immunized horses. The results show that the highest binding of the phage to immobilized horse antibodies occurred in the third round of biopanning. Over 125 individual clones carrying mimotopes of Hemiscorpius lepturus toxins were selected and subjected for sequencing. The sequencing results identified unique peptides mimicking the antigenic and immunogenic epitopes of Hemiscorpius lepturus toxins. The results of this study provide a basis for further studies and the development of a putative epitopic vaccine and a recombinant toxin.

Identification of disease-specific motifs in the antibody specificity repertoire via next-generation sequencing

Disease-specific antibodies can serve as highly effective biomarkers but have been identified for only a relatively small number of autoimmune diseases. A method was developed to identify disease-specific binding motifs through integration of bacterial display peptide library screening, next-generation sequencing (NGS) and computational analysis. Antibody specificity repertoires were determined by identifying bound peptide library members for each specimen using cell sorting and performing NGS. A computational algorithm, termed Identifying Motifs Using Next- generation sequencing Experiments (IMUNE), was developed and applied to discover disease- and healthy control-specific motifs. IMUNE performs comprehensive pattern searches, identifies patterns statistically enriched in the disease or control groups and clusters the patterns to generate motifs. Using celiac disease sera as a discovery set, IMUNE identified a consensus motif (QPEQPF[PS]E) with high diagnostic sensitivity and specificity in a validation sera set, in addition to novel motifs. Peptide display and sequencing (Display-Seq) coupled with IMUNE analysis may thus be useful to characterize antibody repertoires and identify disease-specific antibody epitopes and biomarkers.

Serum Antibody Repertoire Profiling Using In Silico Antigen Screen

Serum antibodies are valuable source of information on the health state of an organism. The profiles of serum antibody reactivity can be generated by using a high throughput sequencing of peptide-coding DNA from combinatorial random peptide phage display libraries selected for binding to serum antibodies. Here we demonstrate that the targets of immune response, which are recognized by serum antibodies directed against sequential epitopes, can be identified using the serum antibody repertoire profiles generated by high throughput sequencing. We developed an algorithm to filter the results of the protein database BLAST search for selected peptides to distinguish real antigens recognized by serum antibodies from irrelevant proteins retrieved randomly. When we used this algorithm to analyze serum antibodies from mice immunized with human protein, we were able to identify the protein used for immunizations among the top candidate antigens. When we analyzed human serum sample from the metastatic melanoma patient, the recombinant protein, corresponding to the top candidate from the list generated using the algorithm, was recognized by antibodies from metastatic melanoma serum on the western blot, thus confirming that the method can identify autoantigens recognized by serum antibodies. We demonstrated also that our unbiased method of looking at the repertoire of serum antibodies reveals quantitative information on the epitope composition of the targets of immune response. A method for deciphering information contained in the serum antibody repertoire profiles may help to identify autoantibodies that can be used for diagnosing and monitoring autoimmune diseases or malignancies.

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

BACKGROUND

Random-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.

RESULTS

GuiTope 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.

CONCLUSIONS

GuiTope 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.

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.

Accurate identification of paraprotein antigen targets by epitope reconstruction

We describe the first successful clinical application of a new discovery technology, epitope-mediated antigen prediction (E-MAP), to the investigation of multiple myeloma. Until now, there has been no reliable, systematic method to identify the cognate antigens of paraproteins. E-MAP is a variation of previous efforts to reconstruct the epitopes of paraproteins, with the significant difference that it provides enough epitope sequence data so as to enable successful protein database searches. We first reconstruct the paraprotein's epitope by analyzing the peptides that strongly bind. Then, we compile the data and interrogate the nonredundant protein database, searching for a close match. As a clinical proof-of-concept, we apply this technology to uncovering the protein targets of para-proteins in multiple myeloma (MM). E-MAP analysis of 2 MM paraproteins identified human cytomegalovirus (HCMV) as a target in both. E-MAP sequence analysis determined that one para-protein binds to the AD-2S1 epitope of HCMV glycoprotein B. The other binds to the amino terminus of the HCMV UL-48 gene product. We confirmed these predictions using immunoassays and immunoblot analyses. E-MAP represents a new investigative tool for analyzing the role of chronic antigenic stimulation in B-lymphoproliferative disorders.