The molecular relationship between antigenic domains and epitopes on hCG

Antigenic domains are defined to contain a limited number of neighboring epitopes recognized by antibodies (Abs) but their molecular relationship remains rather elusive. We thoroughly analyzed the antigenic surface of the important pregnancy and tumor marker human chorionic gonadotropin (hCG), a cystine knot (ck) growth factor, and set antigenic domains and epitopes in molecular relationships to each other. Antigenic domains on hCG, its free hCGα and hCGβ subunits are dependent on appropriate inherent molecular features such as molecular accessibility and protrusion indices that determine bulging structures accessible to Abs. The banana-shaped intact hCG comprises ∼7500 Å2 of antigenic surface with minimally five antigenic domains that encompass a continuum of overlapping non-linear composite epitopes, not taking into account the C-terminal peptide extension of hCGβ (hCGβCTP). Epitopes within an antigenic domain are defined by specific Abs, that bury nearly 1000 Å2 of surface accessible area on the antigen and recognize a few up to 15 amino acid (aa) residues, whereby between 2 and 5 of these provide the essential binding energy. Variability in Ab binding modes to the contact aa residues are responsible for the variation in affinity and intra- and inter-species specificity, e.g. cross-reactions with luteinizing hormone (LH). Each genetically distinct fragment antigen binding (Fab) defines its own epitope. Consequently, recognition of the same epitope by different Abs is only possible in cases of genetically identical sequences of its binding sites. Due to combinatorial V(D)J gene segment variability of heavy and light chains, Abs defining numerous epitopes within an antigenic domain can be generated by different individuals and species. Far more than hundred Abs against the immuno-dominant antigenic domains of either subunit at both ends of the hCG-molecule, the tips of peptide loops one and three (Ł1 + 3) protruding from the central ck, encompassing hCGβŁ1 + 3 (aa 20–25 + 64 + 68–81) and hCGαŁ1 (aa 13–22; Pro16, Phe17, Phe18) plus hCGαŁ3 (Met71, Phe74), respectively, have been identified in the two “ISOBM Tissue Differentiation-7 Workshops on hCG and Related Molecules” and in other studies. These Abs recognize distinct but overlapping epitopes with slightly different specificity profiles and affinities. Heterodimeric-specific epitopes involve neighboring αŁ1 plus βŁ2 (hCGβ44/45 and 47/48). Diagnostically important Abs recognize the middle of the molecule, the ck (aa Arg10, Arg60 and possibly Gln89) and the linear hCGβCTP “tail” (aa 135–145; Asp139, Pro144, Gln145), respectively. Identification of antigenic domains and of specific epitopes is essential for harmonization of Abs in methods that are used for reliable and robust hCG measurements for the management of pregnancy, pregnancy-related disease and tumors

Comparisons of Allergenic and Metazoan Parasite Proteins:Allergy the Price of Immunity

Allergic reactions can be considered as maladaptive IgE immune responses towards environmental antigens. Intriguingly, these mechanisms are observed to be very similar to those implicated in the acquisition of an important degree of immunity against metazoan parasites (helminths and arthropods) in mammalian hosts. Based on the hypothesis that IgE-mediated immune responses evolved in mammals to provide extra protection against metazoan parasites rather than to cause allergy, we predict that the environmental allergens will share key properties with the metazoan parasite antigens that are specifically targeted by IgE in infected human populations. We seek to test this prediction by examining if significant similarity exists between molecular features of allergens and helminth proteins that induce an IgE response in the human host. By employing various computational approaches, 2712 unique protein molecules that are known IgE antigens were searched against a dataset of proteins from helminths and parasitic arthropods, resulting in a comprehensive list of 2445 parasite proteins that show significant similarity through sequence and structure with allergenic proteins. Nearly half of these parasite proteins from 31 species fall within the 10 most abundant allergenic protein domain families (EF-hand, Tropomyosin, CAP, Profilin, Lipocalin, Trypsin-like serine protease, Cupin, BetV1, Expansin and Prolamin). We identified epitopic-like regions in 206 parasite proteins and present the first example of a plant protein (BetV1) that is the commonest allergen in pollen in a worm, and confirming it as the target of IgE in schistosomiasis infected humans. The identification of significant similarity, inclusive of the epitopic regions, between allergens and helminth proteins against which IgE is an observed marker of protective immunity explains the 'off-target' effects of the IgE-mediated immune system in allergy. All these findings can impact the discovery and design of molecules used in immunotherapy of allergic conditions

Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy

Pan-specific prediction of receptor–ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical. Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor–ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding motifs, to others with no or very limited experimental characterization. The success of this approach has however proven to depend strongly on the similarity of the query molecule to the molecules with characterized specificity using in the machine-learning process. Here, we outline an alternative strategy with the aim of altering this and construct data sets optimal for training of pan-specific receptor–ligand predictions focusing on receptor similarity rather than ligand similarity. We show that this receptor clustering method consistently in benchmarks covering affinity predictions, MHC ligand and MHC epitope identification perform better than the conventional ligand clustering method on the alleles with remote similarity to the training set.Fil: Mattsson, Andreas Holm. Technical University of Denmark; Dinamarca. Evaxion Biotech; DinamarcaFil: Kringelum, J.V.. Evaxion Biotech; DinamarcaFil: Garde, C.. Universidad de Copenhagen; DinamarcaFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentin