On the meaning of affinity limits in B-cell epitope prediction for antipeptide antibody-mediated immunity

B-Cell Epitope Prediction for Antipeptide Paratopes with the HAPTIC2/HEPTAD User Toolkit (HUT)

B-cell epitope prediction is key to developing peptide-based vaccines and immunodiagnostics along with antibodies for prophylactic, therapeutic and/or diagnostic use. This entails estimating paratope binding affinity for variable-length peptidic sequences subject to constraints on both paratope accessibility and antigen conformational flexibility, as described herein for the HAPTIC2/HEPTAD User Toolkit (HUT). HUT comprises the Heuristic Affinity Prediction Tool for Immune Complexes 2 (HAPTIC2), the HAPTIC2-like Epitope Prediction Tool for Antigen with Disulfide (HEPTAD) and the HAPTIC2/HEPTAD Input Preprocessor (HIP). HIP enables tagging of residues (e.g., in hydrophobic blobs, ordered regions and glycosylation motifs) for exclusion from downstream analyses by HAPTIC2 and HEPTAD. HAPTIC2 estimates paratope binding affinity for disulfide-free disordered peptidic antigens (by analogy between flexible-ligand docking and protein folding), from terms attributed to compaction (in view of sequence length, charge and temperature-dependent polyproline-II helical propensity), collapse (disfavored by residue bulkiness) and contact (with glycine and proline regarded as polar residues that hydrogen bond with paratopes). HEPTAD analyzes antigen sequences that each contain two cysteine residues for which the impact of disulfide pairing is estimated as a correction to the free-energy penalty of compaction. All of HUT is freely accessible online ( https://freeshell.de/~badong/hut.htm ).

Prediction of Variable-Length B-Cell Epitopes for Antipeptide Paratopes Using the Program HAPTIC

BACKGROUND

B-cell epitope prediction for antipeptide antibody responses enables peptide-based vaccine design and related translational applications. This entails estimating epitopeparatope binding free-energy changes from antigen sequence; but attempts to do so assuming uniform epitope length (e.g., of hexapeptide sequences, each spanning a typical paratope diameter when fully extended) have neglected empirically established variation in epitope length.

OBJECTIVE

This work aimed to develop a sequence-based physicochemical approach to variablelength B-cell epitope prediction for antipeptide paratopes recognizing flexibly disordered targets.

METHODS

Said approach was developed by analogy between epitope-paratope binding and protein folding modeled as polymer collapse, treating paratope structure implicitly. Epitope-paratope binding was thus conceptually resolved into processes of epitope compaction, collapse and contact, with epitope collapse presenting the main entropic barrier limiting epitope length among nonpolyproline sequences. The resulting algorithm was implemented as a computer program, namely the Heuristic Affinity Prediction Tool for Immune Complexes (HAPTIC), which is freely accessible via an online interface (http://badong.freeshell.org/haptic.htm). This was used in conjunction with published data on representative known peptide immunogens.

RESULTS

HAPTIC predicted immunodominant epitope sequences with lengths limited by penalties for both compaction and collapse, consistent with known paratope-bound structures of flexibly disordered epitopes. In most cases, the predicted association constant was greater than its experimentally determined counterpart but below the predicted upper bound for affinity maturation in vivo.

CONCLUSION

HAPTIC provides a physicochemically plausible means for estimating the affinity of antipeptide paratopes for sterically accessible and flexibly disordered peptidic antigen sequences by explicitly considering candidate B-cell epitopes of variable length.

Expressing Redundancy among Linear-Epitope Sequence Data Based on Residue-Level Physicochemical Similarity in the Context of Antigenic Cross-Reaction

Epitope-based design of vaccines, immunotherapeutics, and immunodiagnostics is complicated by structural changes that radically alter immunological outcomes. This is obscured by expressing redundancy among linear-epitope data as fractional sequence-alignment identity, which fails to account for potentially drastic loss of binding affinity due to single-residue substitutions even where these might be considered conservative in the context of classical sequence analysis. From the perspective of immune function based on molecular recognition of epitopes, functional redundancy of epitope data (FRED) thus may be defined in a biologically more meaningful way based on residue-level physicochemical similarity in the context of antigenic cross-reaction, with functional similarity between epitopes expressed as the Shannon information entropy for differential epitope binding. Such similarity may be estimated in terms of structural differences between an immunogen epitope and an antigen epitope with reference to an idealized binding site of high complementarity to the immunogen epitope, by analogy between protein folding and ligand-receptor binding; but this underestimates potential for cross-reactivity, suggesting that epitope-binding site complementarity is typically suboptimal as regards immunologic specificity. The apparently suboptimal complementarity may reflect a tradeoff to attain optimal immune function that favors generation of immune-system components each having potential for cross-reactivity with a variety of epitopes.

An integrative structure-based framework for predicting biological effects mediated by antipeptide antibodies

A general framework is presented for predicting quantitative biological effects mediated by antipeptide antibodies, primarily on the basis of antigen structure (possibly featuring intrinsic disorder) analyzed to estimate epitope-paratope binding affinities, which in turn is considered within the context of dose-response relationships as regards antibody concentration. This is illustrated mainly using an approach based on protein structural energetics, whereby expected amounts of solvent-accessible surface area buried upon epitope-paratope binding are related to the corresponding binding affinity, which is estimated from putative B-cell epitope structure with implicit treatment of paratope structure, for antipeptide antibodies either reacting with peptides or cross-reacting with cognate protein antigens. Key methods described are implemented in SAPPHIRE/SUITE (Structural-energetic Analysis Program for Predicting Humoral Immune Response Epitopes/SAPPHIRE User Interface Tool Ensemble; publicly accessible via http://freeshell.de/~badong/suite.htm). Representative results thus obtained are compared with published experimental data on binding affinities and quantitative biological effects, with special attention to loss of paratope sidechain conformational entropy (neglected in previous analyses) and in light of key in-vivo constraints on antigen-antibody binding affinity and antibody-mediated effects. Implications for further refinement of B-cell epitope prediction methods are discussed as regards envisioned biomedical applications including the development of prophylactic and therapeutic antibodies, peptide-based vaccines and immunodiagnostics.

"Velcro" engineering of high affinity CD47 ectodomain as signal regulatory protein α (SIRPα) antagonists that enhance antibody-dependent cellular phagocytosis

CD47 is a cell surface protein that transmits an anti-phagocytic signal, known as the "don't-eat-me" signal, to macrophages upon engaging its receptor signal regulatory protein α (SIRPα). Molecules that antagonize the CD47-SIRPα interaction by binding to CD47, such as anti-CD47 antibodies and the engineered SIRPα variant CV1, have been shown to facilitate macrophage-mediated anti-tumor responses. However, these strategies targeting CD47 are handicapped by large antigen sinks in vivo and indiscriminate cell binding due to ubiquitous expression of CD47. These factors reduce bioavailability and increase the risk of toxicity. Here, we present an alternative strategy to antagonize the CD47-SIRPα pathway by engineering high affinity CD47 variants that target SIRPα, which has restricted tissue expression. CD47 proved to be refractive to conventional affinity maturation techniques targeting its binding interface with SIRPα. Therefore, we developed a novel engineering approach, whereby we augmented the existing contact interface via N-terminal peptide extension, coined "Velcro" engineering. The high affinity variant (Velcro-CD47) bound to the two most prominent human SIRPα alleles with greatly increased affinity relative to wild-type CD47 and potently antagonized CD47 binding to SIRPα on human macrophages. Velcro-CD47 synergizes with tumor-specific monoclonal antibodies to enhance macrophage phagocytosis of tumor cells in vitro, with similar potency as CV1. Finally, Velcro-CD47 interacts specifically with a subset of myeloid-derived cells in human blood, whereas CV1 binds all myeloid, lymphoid, and erythroid populations interrogated. This is consistent with the restricted expression of SIRPα compared with CD47. Herein, we have demonstrated that "Velcro" engineering is a powerful protein-engineering tool with potential applications to other systems and that Velcro-CD47 could be an alternative adjuvant to CD47-targeting agents for cancer immunotherapy.

Structure of the extracellular domain of matrix protein 2 of influenza A virus in complex with a protective monoclonal antibody

The extracellular domain of influenza A virus matrix protein 2 (M2e) is conserved and is being evaluated as a quasiuniversal influenza A vaccine candidate. We describe the crystal structure at 1.6 Å resolution of M2e in complex with the Fab fragment of an M2e-specific monoclonal antibody that protects against influenza A virus challenge. This antibody binds M2 expressed on the surfaces of cells infected with influenza A virus. Five out of six complementary determining regions interact with M2e, and three highly conserved M2e residues are critical for this interaction. In this complex, M2e adopts a compact U-shaped conformation stabilized in the center by the highly conserved tryptophan residue in M2e. This is the first description of the three-dimensional structure of M2e.

IMPORTANCE

M2e of influenza A is under investigation as a universal influenza A vaccine, but its three-dimensional structure is unknown. We describe the structure of M2e stabilized with an M2e-specific monoclonal antibody that recognizes natural M2. We found that the conserved tryptophan is positioned in the center of the U-shaped structure of M2e and stabilizes its conformation. The structure also explains why previously reported in vivo escape viruses, selected with a similar monoclonal antibody, carried proline residue substitutions at position 10 in M2.

Identification of protective linear B-cell epitopes on the subolesin/akirin orthologues of Ornithodoros spp. soft ticks

Subolesin/akirin is a protective antigen that is highly conserved across hematophagous vector species and is therefore potentially useful for the development of a universal vaccine for vector control, including soft ticks. Recent results have shown that in Ornithodoros erraticus and O. moubata soft ticks, RNAi-mediated subolesin gene knockdown inhibits tick oviposition and fertility by more than 90%; however, vaccination with recombinant subolesins resulted in remarkably low protective efficacies (5-24.5% reduction in oviposition). Here we report that vaccination with subolesin recombinants induces non-protective antibodies mainly directed against immunodominant linear B-cell epitopes located on highly structured regions of the subolesin protein, probably unrelated to its biological activity, while leaving the unstructured/disordered regions unrecognized. Accordingly, for a new vaccine trial we designed four synthetic peptides (OE1, OE2, OM1 and OM2) from the unrecognized/disordered regions of the Ornithodoros subolesin sequences and coupled them to keyhole limpet haemocyanin (KLH). These KLH-peptide conjugates induced the synthesis of antibodies that recognized linear B-cell epitopes located on the unstructured loops of the subolesin protein and provided up to 70.1% and 83.1% vaccine efficacies in O. erraticus and O. moubata, respectively. These results show that the protective effect of subolesin-based vaccines is highly dependent on the particular epitope recognized by antibodies on the subolesin sequence and strongly suggest that the biological activity of subolesin is exerted through its unstructured regions. The results reported here contribute to our understanding of the mechanism of protection of subolesin-based vaccines and reveal novel protective peptides that could be included among the array of candidate antigens useful for developing anti-vector vaccines based on subolesin/akirin.

Computational peptide vaccinology

Immunoinformatics focuses on modeling immune responses for better understanding of the immune system and in many cases for proposing agents able to modify the immune system. The most classical of these agents are vaccines derived from living organisms such as smallpox or polio. More modern vaccines comprise recombinant proteins, protein domains, and in some cases peptides. Generating a vaccine from peptides however requires technologies and concepts very different from classical vaccinology. Immunoinformatics therefore provides the computational tools to propose peptides suitable for formulation into vaccines. This chapter introduces the essential biological concepts affecting design and efficacy of peptide vaccines and discusses current methods and workflows applied to design successful peptide vaccines using computers.

Benchmarking B-cell epitope prediction with quantitative dose-response data on antipeptide antibodies: towards novel pharmaceutical product development

B-cell epitope prediction can enable novel pharmaceutical product development. However, a mechanistically framed consensus has yet to emerge on benchmarking such prediction, thus presenting an opportunity to establish standards of practice that circumvent epistemic inconsistencies of casting the epitope prediction task as a binary-classification problem. As an alternative to conventional dichotomous qualitative benchmark data, quantitative dose-response data on antibody-mediated biological effects are more meaningful from an information-theoretic perspective in the sense that such effects may be expressed as probabilities (e.g., of functional inhibition by antibody) for which the Shannon information entropy (SIE) can be evaluated as a measure of informativeness. Accordingly, half-maximal biological effects (e.g., at median inhibitory concentrations of antibody) correspond to maximally informative data while undetectable and maximal biological effects correspond to minimally informative data. This applies to benchmarking B-cell epitope prediction for the design of peptide-based immunogens that elicit antipeptide antibodies with functionally relevant cross-reactivity. Presently, the Immune Epitope Database (IEDB) contains relatively few quantitative dose-response data on such cross-reactivity. Only a small fraction of these IEDB data is maximally informative, and many more of them are minimally informative (i.e., with zero SIE). Nevertheless, the numerous qualitative data in IEDB suggest how to overcome the paucity of informative benchmark data.

Hybrid methods for B-cell epitope prediction

Many computational approaches to B-cell epitope prediction have been published, including combinations of previously proposed methods, which complicates the tasks of further developing such computational approaches and of selecting those most appropriate for practical applications (e.g., the design of novel immunodiagnostics and vaccines). These tasks are considered together herein to clarify their close but often overlooked interrelationship, thereby providing a guide to their performance in mutual support of one another, with emphasis on key physicochemical and biological considerations that are relevant from an applications perspective. This aims to assist investigators in performing either or both tasks, with the overall goals of successfully applying computational tools towards practical ends and of generating informative new data towards iterative improvement of the tools, particularly as regards the design of peptide-based immunogens for eliciting the production of antipeptide antibodies that modulate biological activity of protein targets via functionally relevant cross-reactivity in relation to the phenomena of protein folding and protein disorder.

Antidotes, antibody-mediated immunity and the future of pharmaceutical product development

If new scientific knowledge is to be more efficiently generated and applied toward the advancement of health, human safety must be more effectively addressed in the conduct of research. Given the present difficulties of accurately predicting biological outcomes of novel interventions in vivo, the imperative of human safety suggests the development of novel pharmaceutical products in tandem with their prospective antidotes in anticipation of possible adverse events, to render the risks of initial clinical trials more acceptable from a regulatory standpoint. Antibody-mediated immunity provides a generally applicable mechanistic basis for developing antidotes to both biologicals and small-molecule drugs (such that antibodies may serve as antidotes to pharmaceutical agents as a class including other antibodies) and also for the control and prevention of both infectious and noninfectious diseases via passive or active immunization. Accordingly, the development of prophylactic or therapeutic passive-immunization strategies using antipeptide antibodies is a plausible prelude to the development of corresponding active-immunization strategies using peptide-based vaccines. In line with this scheme, global proliferation of antibody- and vaccine-production technologies, especially those that obviate dependence on the cold chain for storage and transport of finished products, could provide geographically distributed breakout capability against emerging and future health challenges.