Anin silico method using an epitope motif database for predicting the location of antigenic determinants on proteins in a structural context

Development and Application of Computational Methods in Phage Display Technology

BACKGROUND

Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display.

METHODS

We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses.

RESULTS

We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data.

CONCLUSION

The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.

PEPOP 2.0: new approaches to mimic non-continuous epitopes

BACKGROUND

Bioinformatics methods are helpful to identify new molecules for diagnostic or therapeutic applications. For example, the use of peptides capable of mimicking binding sites has several benefits in replacing a protein which is difficult to produce, or toxic. Using peptides is less expensive. Peptides are easier to manipulate, and can be used as drugs. Continuous epitopes predicted by bioinformatics tools are commonly used and these sequential epitopes are used as is in further experiments. Numerous discontinuous epitope predictors have been developed but only two bioinformatics tools have been proposed so far to predict peptide sequences: Superficial and PEPOP 2.0. PEPOP 2.0 can generate series of peptide sequences that can replace continuous or discontinuous epitopes in their interaction with their cognate antibody.

RESULTS

We have developed an improved version of PEPOP (PEPOP 2.0) dedicated to answer to experimentalists' need for a tool able to handle proteins and to turn them into peptides. The PEPOP 2.0 web site has been reorganized by peptide prediction category and is therefore better formulated to experimental designs. Since the first version of PEPOP, 32 new methods of peptide design were developed. In total, PEPOP 2.0 proposes 35 methods in which 34 deal specifically with discontinuous epitopes, the most represented epitope type in nature.

CONCLUSION

Through the presentation of its user-friendly, well-structured new web site conceived in close proximity to experimentalists, we report original methods that show how PEPOP 2.0 can assist biologists in dealing with discontinuous epitopes.

Challenges related to the immunogenicity of parenteral recombinant proteins: Underlying mechanisms and new approaches to overcome it

Immune response elicited by therapeutic proteins is an important safety and efficacy issue for regulatory agencies, drug manufacturers, clinicians, and patients. Administration of therapeutic proteins can potentially induce the production of anti-drug antibodies or cell-mediated immune responses. At first, it was speculated that the immunogenicity is related to the non-human origin of these proteins. Later on, it was confirmed that the human proteins may also show immunogenicity. In this review article, we will focus on a number of factors, which play crucial roles in the human protein immunogenicity. These factors are related to the patient's status (or intrinsic properties) and molecular characteristics of the therapeutic protein's (or extrinsic properties). Furthermore, we will discuss available in silico, in vitro, and in vivo methods for the prediction of sequences, which may generate an immune response following parenteral administration of these proteins. In summary, nowadays, it is possible for drug manufacturers to evaluate the risk of immunogenicity of therapeutic proteins and implement a management plan to overcome the problems prior to proceeding to human clinical trials.

B-cell epitope engineering: A matter of recognizing protein features and motives

Using in vivo and in vitro studies B-cell epitopes have been identified on a number of proteins. These epitopes were used to develop predictive methods. After comparison of existing and emerging technologies, this review concludes that antigenicity is not described by physicochemical and structural characteristics of a protein alone. Molecular characteristics of the antigenic amino acids are required. How the structural context affects the selection of these amino acids by the antibody is unknown.:

Recent developments with B-cell epitope identification for predictive studies

This review discusses currently available methods for predicting B-cell epitopes on proteins. The use of animals for assessing protein immunogenicity is addressed primarily to highlight the differences in B- and T-cell epitope recognition between species. These differences have to be considered when interpreting potential B-cell epitopes identified by the methods addressed here. "In vitro alternatives" focuses on the strengths and limitations of peptide-based technologies. Three types of computer-based methods for identifying potential B-cell epitopes are discussed: (i) methods applying physico-chemical and structural propensity scales for predicting linear epitopes from the primary structure of a protein, (ii) comparative methods basing prediction upon amino acid sequence and structural similarities between antigenically known and unknown proteins, and (iii) a method combining structural features with a B-cell epitope motif database for predicting linear and conformational antigenic determinants. With respect to human safety, the usefulness of antibody-based tests is limited to comparative studies between an antigenically known protein and variants thereof. Similarly, computer-based methods using data mining can address similarities in B-cell epitope profiles between related proteins, if a proper cut off can be defined for the minimal amino acid sequence similarity required for obtaining an acceptable accuracy. Among the physico-chemical and structural scales, scales identifying in a protein hairpin and non-specific turns seem useful for predicting epitopes with a continuous primary binding site. When conformational epitopes have to be identified as well, a novel computer-based tool seems to be the most promising alternative to X-ray crystallography. However, both methods remain to be extensively evaluated and validated. Thus, promising tools for B-cell epitope identification have been developed. But, no validated method for B-cell epitope identification on antigenically unknown proteins is available yet.

IgE epitopes of intact and digested Ara h 1: a comparative study in humans and rats

BACKGROUND

Allergen epitope characterization provides valuable information useful for the understanding of proteins as food allergens. It is believed that IgE epitopes in general are conformational, nevertheless, for food allergens known to sensitize through the gastrointestinal tract linear epitopes have been suggested to be of great importance.

OBJECTIVE

The aim of this study was to identify IgE specific epitopes of intact and digested Ara h 1, and to compare epitope patterns between humans and rats.

METHODS

Sera from five peanut allergic patients and five Brown Norway rats were used to identify intact and digested Ara h 1-specific IgE epitopes by competitive immunoscreening of a phage-displayed random hepta-mer peptide library using polyclonal IgE from the individual sera. The resulting peptide sequences were mapped on the surface of a three-dimensional structure of the Ara h 1 molecule to mimic epitopes using a computer-based algorithm.

RESULTS

Patients as well as rats were shown to have individual IgE epitope patterns. All epitope mimics were conformational and found to cluster into three different areas of the Ara h 1 molecule. Five epitope motifs were identified by patient IgE, which by far accounted for most of the eluted peptide sequences. Epitope patterns were rather similar for both intact and digested Ara h 1 as well as for humans and rats.

CONCLUSIONS

Individual patient specific epitope patterns have been identified for the major allergen Ara h 1. IgE binding epitopes have been suggested as biomarkers for persistency and severity of food allergy, wherefore recognition of particular epitope patterns or motifs could be a valuable tool for prevention, diagnosis, and treatment of food allergy.

Residues Arg568 and Phe592 contribute to an antigenic surface for anti-ADAMTS13 antibodies in the spacer domain

BACKGROUND

The majority of patients diagnosed with thrombotic thrombocytopenic purpura have autoantibodies directed towards the spacer domain of ADAMTS13.

DESIGN AND METHODS

In this study we explored the epitope specificity and immunoglobulin class and immunoglobulin G subclass distribution of anti-ADAMTS13 antibodies. The epitope specificity of anti-spacer domain antibodies was examined using plasma from 48 patients with acute acquired thrombotic thrombocytopenic purpura by means of immunoprecipitation of ADAMTS13 variants containing single or multiple alanine substitutions. Using similar methods, we also determined the presence of anti-TSP2-8 and CUB1-2 domain antibodies in this cohort of patients.

RESULTS

Antibody profiling revealed that anti-ADAMTS13 immunoglobulin G1 and immunoglobulin G4 predominate in plasma of patients with acquired thrombotic thrombocytopenic purpura. Analysis of anti-spacer domain antibodies revealed that Arg568 and Phe592, in addition to residues Arg660, Tyr661, and Tyr665, also contribute to an antigenic surface in the spacer domain. The majority of patients (90%) lost reactivity towards the spacer domain following introduction of multiple alanine substitutions at Arg568, Phe592, Arg660, Tyr661 and Tyr665. Anti-TSP2-8 and anti-CUB1-2 domain-directed antibodies were present in, respectively, 17% and 35% of the patients' samples analyzed.

CONCLUSIONS

Immunoglobulin G directed towards a single antigenic surface comprising residues Arg568, Phe592, Arg660, Tyr661 and Tyr665 predominates in the plasma of patients with acquired thrombotic thrombocytopenic purpura.

Bioinformatics resources and tools for phage display

Databases and computational tools for mimotopes have been an important part of phage display study. Five special databases and eighteen algorithms, programs and web servers and their applications are reviewed in this paper. Although these bioinformatics resources have been widely used to exclude target-unrelated peptides, characterize small molecules-protein interactions and map protein-protein interactions, a lot of problems are still waiting to be solved. With the improvement of these tools, they are expected to serve the phage display community better.

Applying bioinformatics for antibody epitope prediction using affinity-selected mimotopes - relevance for vaccine design

To properly characterize protective polyclonal antibody responses, it is necessary to examine epitope specificity. Most antibody epitopes are conformational in nature and, thus, cannot be identified using synthetic linear peptides. Cyclic peptides can function as mimetics of conformational epitopes (termed mimotopes), thereby providing targets, which can be selected by immunoaffinity purification. However, the management of large collections of random cyclic peptides is cumbersome. Filamentous bacteriophage provides a useful scaffold for the expression of random peptides (termed phage display) facilitating both the production and manipulation of complex peptide libraries. Immunoaffinity selection of phage displaying random cyclic peptides is an effective strategy for isolating mimotopes with specificity for a given antiserum. Further epitope prediction based on mimotope sequence is not trivial since mimotopes generally display only small homologies with the target protein. Large numbers of unique mimotopes are required to provide sufficient sequence coverage to elucidate the target epitope. We have developed a method based on pattern recognition theory to deal with the complexity of large collections of conformational mimotopes. The analysis consists of two phases: 1) The learning phase where a large collection of epitope-specific mimotopes is analyzed to identify epitope specific “signs” and 2) The identification phase where immunoaffinity-selected mimotopes are interrogated for the presence of the epitope specific “signs” and assigned to specific epitopes. We are currently using computational methods to define epitope “signs” without the need for prior knowledge of specific mimotopes. This technology provides an important tool for characterizing the breadth of antibody specificities within polyclonal antisera.

Innovative bioinformatic approaches for developing peptide-based vaccines against hypervariable viruses

The application of the fields of pharmacogenomics and pharmacogenetics to vaccine design has been recently labeled 'vaccinomics'. This newly named area of vaccine research, heavily intertwined with bioinformatics, seems to be leading the charge in developing novel vaccines for currently unmet medical needs against hypervariable viruses such as human immunodeficiency virus (HIV), hepatitis C and emerging avian and swine influenza. Some of the more recent bioinformatic approaches in the area of vaccine research include the use of epitope determination and prediction algorithms for exploring the use of peptide epitopes as vaccine immunogens. This paper briefly discusses and explores some current uses of bioinformatics in vaccine design toward the pursuit of peptide vaccines for hypervariable viruses. The various informatics and vaccine design strategies attempted by other groups toward hypervariable viruses will also be briefly examined, along with the strategy used by our group in the design and synthesis of peptide immunogens for candidate HIV and influenza vaccines.

Benchmarking B-cell epitope prediction for the design of peptide-based vaccines: problems and prospects

To better support the design of peptide-based vaccines, refinement of methods to predict B-cell epitopes necessitates meaningful benchmarking against empirical data on the cross-reactivity of polyclonal antipeptide antibodies with proteins, such that the positive data reflect functionally relevant cross-reactivity (which is consistent with antibody-mediated change in protein function) and the negative data reflect genuine absence of cross-reactivity (rather than apparent absence of cross-reactivity due to artifactual masking of B-cell epitopes in immunoassays). These data are heterogeneous in view of multiple factors that complicate B-cell epitope prediction, notably physicochemical factors that define key structural differences between immunizing peptides and their cognate proteins (e.g., unmatched electrical charges along the peptide-protein sequence alignments). If the data are partitioned with respect to these factors, iterative parallel benchmarking against the resulting subsets of data provides a basis for systematically identifying and addressing the limitations of methods for B-cell epitope prediction as applied to vaccine design.

Relevant B cell epitopes in allergic disease

The 3-dimensional structure of an allergen defines the accessible parts on the surface of the molecule or epitopes that interact with antibodies. Mapping the antigenic determinants for IgE antibody binding has been pursued through strategies based on the use of overlapping synthetic peptides, recombinant allergenic fragments or unfolded allergens. These approaches led to the identification of mostly linear epitopes and are useful for food allergens that undergo digestion or food processing. For inhaled allergens, conformational epitopes appear to be the primary targets of IgE responses. Knowledge of the molecular structure of allergens alone and in complex with antibodies that interfere with IgE antibody binding is important to understand the immune recognition of B cell-antigenic determinants on allergens and the design of recombinant allergens for immunotherapy. Starting with the molecular cloning and expression of allergens, and with the advent of X-ray crystallography and nuclear magnetic resonance techniques, we have been able to visualize conformational epitopes on allergens.

Bioinformatics for study of autoimmunity

Recent years have witnessed an explosive growth in available biological data pertaining to autoimmunity research. This includes a tremendous quantity of sequence data (biological structures, genetic and physical maps, pathways, etc.) generated by genome and proteome projects plus extensive clinical and epidemiological data. Autoimmunity research stands to greatly benefit from this data so long as appropriate strategies are available to enable full access to and utilization of this data. The quantity and complexity of this biological data necessitates use of advanced bioinformatics strategies for its efficient retrieval, analysis and interpretation. Major progress has been made in development of specialized tools for storage, analysis and modeling of immunological data, and this has led to development of a whole new field know as immunoinformatics. With advances in novel high-throughput immunology technologies immunoinformatics is transforming understanding of how the immune system functions. This paper reviews advances in the field of immunoinformatics pertinent to autoimmunity research including databases, tools in genomics and proteomics, tools for study of B- and T-cell epitopes, integrative approaches, and web servers.

Characterizing complex polysera produced by antigen-specific immunization through the use of affinity-selected mimotopes

BACKGROUND

Antigen-based (as opposed to whole organism) vaccines are actively being pursued for numerous indications. Even though different formulations may produce similar levels of total antigen-specific antibody, the composition of the antibody response can be quite distinct resulting in different levels of therapeutic activity.

METHODOLOGY/PRINCIPAL FINDINGS

Using plasmid-based immunization against the proto-oncogene HER-2 as a model, we have demonstrated that affinity-selected epitope mimetics (mimotopes) can provide a defined signature of a polyclonal antibody response. Further, using novel computer algorithms that we have developed, these mimotopes can be used to predict epitope targets.

CONCLUSIONS/SIGNIFICANCE

By combining our novel strategy with existing methods of epitope prediction based on physical properties of an individual protein, we believe that this method offers a robust method for characterizing the breadth of epitope-specificity within a specific polyserum. This strategy is useful as a tool for monitoring immunity following vaccination and can also be used to define relevant epitopes for the creation of novel vaccines.

Deciphering epitope specificities within polyserum using affinity selection of random peptides and a novel algorithm based on pattern recognition theory

While numerous strategies have been developed to map epitope specificities for monoclonal antibodies, few have been designed for elucidating epitope specificity within complex polysera. We have developed a novel algorithm based on pattern recognition theory that can be used to characterize the breadth of epitope specificities within a polyserum based on affinity selection of random peptides. To attribute these random peptides to a specific epitope, the sequences of the affinity-selected peptides were matched against a database of random peptides selected using well-described monoclonal antibodies. To test this novel algorithm, we employed polyserum from patients infected with West Nile virus and isolated 109 unique sequences which were recognized selectively by serum from West Nile virus-infected patients but not uninfected patients. Through application of our algorithm, it was possible to match 20% of the polyserum-selected peptides to the database of peptides isolated by affinity selection using monoclonal antibodies against the virus envelope protein. Statistical analysis demonstrated that the peptides selected with the polyserum could not be attributed to the peptide database by chance. This novel algorithm provides the basis for further development of methods to characterize the breadth of epitope recognition within a complex pool of antibodies.

PEPOP: computational design of immunogenic peptides

Background

Most methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides.

Results

PEPOP uses the 3D coordinates of a protein both to predict clusters of surface accessible segments that might correspond to epitopes and to design peptides to be used to raise antibodies that target the cognate antigen at specific sites. To verify the ability of PEPOP to identify epitopes, 13 crystallographically defined epitopes were compared with PEPOP clusters: specificity ranged from 0.75 to 1.00, sensitivity from 0.33 to 1.00, and the positive predictive value from 0.19 to 0.89. Comparison of these results with those obtained with two other prediction algorithms showed comparable specificity and slightly better sensitivity and PPV. To prove the capacity of PEPOP to predict immunogenic peptides that induce protein cross-reactive antibodies, several peptides were designed from the 3D structure of model antigens (IA-2, TPO, and IL8) and chemically synthesized. The reactivity of the resulting anti-peptides antibodies with the cognate antigens was measured. In 80% of the cases (four out of five peptides), the flanking protein sequence process (sequence-based) of PEPOP successfully proposed peptides that elicited antibodies cross-reacting with the parent proteins. Polyclonal antibodies raised against peptides designed from amino acids which are spatially close in the protein, but separated in the sequence, could also be obtained, although they were much less reactive. The capacity of PEPOP to design immunogenic peptides that induce antibodies suitable for a sandwich capture assay was also demonstrated.

Conclusion

PEPOP has the potential to guide experimentalists that want to localize an epitope or design immunogenic peptides for raising antibodies which target proteins at specific sites. More successful predictions of immunogenic peptides were obtained when a peptide was continuous as compared with peptides corresponding to discontinuous epitopes. PEPOP is available for use at .

Modeling the adaptive immune system: predictions and simulations

MOTIVATION

Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered.

SUMMARY

Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state-of-the-art prediction tools for proteasomal cleavage and TAP binding. By comparison, class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions.

Identification of discontinuous antigenic determinants on proteins based on shape complementarities

Diverse procedures for identifying antigenic determinants on proteins have been developed, including experimental as well as computational approaches. However, most of these techniques focus on continuous epitopes, whereas fast and reliable identification and verification of discontinuous epitopes remains barely amenable. In this paper, we describe a computational workflow for the detection of discontinuous epitopes on proteins. The workflow uses a given protein 3D structure as input, and combines a per residue solvent accessibility constraint with epitope to paratope shape complementarity measures and binding energies for assigning antigenic determinants in the conformational context. We have developed the procedure on a given set of 26 antigen-antibody complexes with a known structure, and have further expanded the available paratope shapes by generating a virtual paratope library in order to improve the screening for candidate residues constituting discontinuous epitopes. Applying the workflow on the 26 given antigens with known discontinuous epitopes resulted in the correct identification of the spatial proximity of 12 antigen-antibody interaction sites. Combining solvent accessibility, shape complementarity and binding energies towards the identification of discontinuous epitopes clearly outperforms approaches solely considering accessibility and residue distance constraints.

Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools

A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.

Prediction of residues in discontinuous B-cell epitopes using protein 3D structures

Discovery of discontinuous B-cell epitopes is a major challenge in vaccine design. Previous epitope prediction methods have mostly been based on protein sequences and are not very effective. Here, we present DiscoTope, a novel method for discontinuous epitope prediction that uses protein three-dimensional structural data. The method is based on amino acid statistics, spatial information, and surface accessibility in a compiled data set of discontinuous epitopes determined by X-ray crystallography of antibody/antigen protein complexes. DiscoTope is the first method to focus explicitly on discontinuous epitopes. We show that the new structure-based method has a better performance for predicting residues of discontinuous epitopes than methods based solely on sequence information, and that it can successfully predict epitope residues that have been identified by different techniques. DiscoTope detects 15.5% of residues located in discontinuous epitopes with a specificity of 95%. At this level of specificity, the conventional Parker hydrophilicity scale for predicting linear B-cell epitopes identifies only 11.0% of residues located in discontinuous epitopes. Predictions by the DiscoTope method can guide experimental epitope mapping in both rational vaccine design and development of diagnostic tools, and may lead to more efficient epitope identification.

Immunomics: discovering new targets for vaccines and therapeutics

T-cell-epitope mapping has emerged as one of the most powerful new drug discovery tools for a range of biomedical applications. Initially, T-cell-epitope discovery was applied to the development of vaccines for infectious diseases and cancer. T-cell-epitope-mapping applications have now expanded to include reengineering of protein therapeutics (a process now called deimmunization), as well as the fields of autoimmunity, endocrinology, allergy, transplantation and diagnostics. Research employing T-cell-epitope mapping falls within the realm of immunomics, a new field that addresses the interface between host and (pathogen) proteome, bridging informatics, genomics, proteomics, immunology and clinical medicine. This review highlights aspects of recent immunomics research that are related to the discovery of the T-cell immunome.