Stepwise prediction of conformational discontinuous B‐cell epitopes using the Mapitope algorithm

Bublil, Erez M.; Freund, Natalia T.; Mayrose, Itay; Penn, Osnat; Roitburd-Berman, Anna; Rubinstein, Nimrod D.; Pupko, Tal; Gershoni, Jonathan M.

doi:10.1002/prot.21387

Cited by 82 publications

(83 citation statements)

References 49 publications

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“…It has been suggested that amino acids that are proximal on the epitope act cooperatively, thus enhancing certain traits important for the binding interaction (Bublil et al, 2007;Enshell-Seijffers et al, 2003;Neuvirth et al, 2004). To test this hypothesis, the composition of amino acids was analyzed again, to check for overrepresentation of amino acid pairs (see Supplementary material for a detailed explanation of the statistical test).…”

Section: Amino Acid Cooperativity In Epitopesmentioning

confidence: 99%

Computational characterization of B-cell epitopes

Rubinstein

Mayrose

Halperin

et al. 2008

Molecular Immunology

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Characterizing B-cell epitopes is a fundamental step for understanding the immunological basis of bio-recognition. To date, epitope analyses have either been based on limited structural data, or sequence data alone. In this study, our null hypothesis was that the surface of the antigen is homogeneously antigenic. To test this hypothesis, a large dataset of antibody-antigen complex structures, together with crystal structures of the native antigens, has been compiled. Computational methods were developed and applied to detect and extract physico-chemical, structural, and geometrical properties that may distinguish an epitope from the remaining antigen surface. Rigorous statistical inference was able to clearly reject the null hypothesis showing that epitopes are distinguished from the remaining antigen surface in properties such as amino acid preference, secondary structure composition, geometrical shape, and evolutionary conservation. Specifically, epitopes were found to be significantly enriched with tyrosine and tryptophan, and to show a general preference for charged and polar amino acids. Additionally, epitopes were found to show clear preference for residing on planar parts of the antigen that protrude from the surface, yet with a rugged surface shape at the atom level. The effects of complex formation on the structural properties of the antigen were also computationally characterized and it is shown that epitopes undergo compression upon antibody binding. This correlates with the finding that epitopes are enriched with unorganized secondary structure elements that render them flexible. Thus, this study extends the understanding of the underlying processes required for antibody binding, and reveals new aspects of the antibody-antigen interaction.

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Section: Amino Acid Cooperativity In Epitopesmentioning

confidence: 99%

Computational characterization of B-cell epitopes

Rubinstein

Mayrose

Halperin

et al. 2008

Molecular Immunology

Self Cite

219

233

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“…The Pepitope server (http://pepitope.tau.ac.il/) (Mayrose et al, 2007a) was used to map the selected peptides onto the 3D model of HA from A/swan/Poland/305-135V08/2006 (H5N1). Using the consensus methodology, both PepSurf (Mayrose et al, 2007b) and Mapitope algorithms (Bublil et al, 2007) were executed and their results combined into a single prediction including only residues predicted to be a part of the epitope in both algorithms. Mapping results were compared with the surface of the H5 HA structural model using UCSF Chimera (v1.11).…”

Section: Methodsmentioning

confidence: 99%

Characterization of mAb6-9-1 monoclonal antibody against hemagglutinin of avian influenza virus H5N1 and its engineered derivative, single-chain variable fragment antibody

Sawicka¹,

Siedlecki²,

Kalenik³

et al. 2016

Acta Biochim Pol

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Hemagglutinin (HA), as a major surface antigen of influenza virus, is widely used as a target for production of neutralizing antibodies. Monoclonal antibody, mAb6-9-1, directed against HA of highly pathogenic avian influenza virus A/swan/Poland/305-135V08/2006(H5N1) was purified from mouse hybridoma cells culture and characterized. The antigenic specificity of mAb6-9-1 was verified by testing its cross-reactivity with several variants of HA. The mimotopes recognized by mAb6-9-1 were selected from two types of phage display peptide libraries. The comparative structural model of the HA variant used for antibody generation was developed to further facilitate epitope mapping. Based on the sequences of the affinity-selected polypeptides and the structural model of HA the epitope was located to the region near the receptor binding site (RBS). Such localization of the epitope recognized by mAb6-9-1 is in concordance with its moderate hemagglutination inhibiting activity and its antigenic specificity. Additionally, total RNA isolated from the hybridoma cell line secreting mAb6-9-1 was used for obtaining two variants of cDNA encoding recombinant single-chain variable fragment (scFv) antibody. To ensure high production level and solubility in bacterial expression system, the scFv fragments were produced as chimeric proteins in fusion with thioredoxin or displayed on a phage surface after cloning into the phagemid vector. Specificity and affinity of the recombinant soluble and phage-bound scFv were assayed by suitable variants of ELISA test. The observed differences in specificity were discussed.

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“…These include MIMOP [56], which predicts linear and conformational epitopes based on MimAlign and MimCons methods, MIMOX [57], Pepitope [58] which uses Pepsurf [59] and Mapitope [60] methods, Pep-3D-Search [61], and MimoPro [62] . These methods can be classified into two categories: sequencesequence alignment category and sequence-structure alignment category [63].…”

Section: Conformational B-cell Epitope Prediction Softwarementioning

confidence: 99%

An Overview of Bioinformatics Tools and Resources in Allergy

Lin

2017

Methods in Molecular Biology

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The rapidly increasing number of characterized allergens has created huge demands for advanced information storage, retrieval, and analysis. Bioinformatics and machine learning approaches provide useful tools for the study of allergens and epitopes prediction, which greatly complement traditional laboratory techniques. The specific applications mainly include identification of B- and T-cell epitopes, and assessment of allergenicity and cross-reactivity. In order to facilitate the work of clinical and basic researchers who are not familiar with bioinformatics, we review in this chapter the most important databases, bioinformatic tools, and methods with relevance to the study of allergens.

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Stepwise prediction of conformational discontinuous B‐cell epitopes using the Mapitope algorithm

Cited by 82 publications

References 49 publications

Computational characterization of B-cell epitopes

Computational characterization of B-cell epitopes

Characterization of mAb6-9-1 monoclonal antibody against hemagglutinin of avian influenza virus H5N1 and its engineered derivative, single-chain variable fragment antibody

An Overview of Bioinformatics Tools and Resources in Allergy

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