Literature DB >> 23754530

The indistinguishability of epitopes from protein surface is explained by the distinct binding preferences of each of the six antigen-binding loops.

Vered Kunik1, Yanay Ofran.   

Abstract

General protein-protein interfaces are known to be enriched, compared with other surface patches, with amino acids that can form stabilizing interactions. However, several studies reported that there are hardly any differences between the amino acid composition of B-cell epitopes and that of antigen surface residues. If the amino acid composition of epitopes is indistinguishable from other surface patches, how do antibodies (Abs) identify epitopes? Here, we analyze the antigen binding regions (ABRs, roughly corresponding to the complementarity determining regions) and the epitopes in a non-redundant set of all known Ab-antigen complexes. We find that the ABRs differ significantly from each other in their amino acid composition and length. Analysis of the energetic contribution of each ABR to antigen binding reveals that, while H3 often plays a key role in antigen binding, in many antibodies other ABRs are more important. Moreover, each ABR has a distinct propensity to bind different amino acids on the antigen. The combined binding preferences of the ABRs yield a total preference to amino acids with a composition that is virtually identical to that of surface residues. These results suggest that antibodies evolved to recognize protein surfaces. They may help in improving Ab engineering and B-cell epitope prediction.

Keywords:  antibody–antigen interactions; antigen binding regions; complementarity determining regions; epitopes

Mesh:

Substances:

Year:  2013        PMID: 23754530     DOI: 10.1093/protein/gzt027

Source DB:  PubMed          Journal:  Protein Eng Des Sel        ISSN: 1741-0126            Impact factor:   1.650


  34 in total

1.  The diversity of H3 loops determines the antigen-binding tendencies of antibody CDR loops.

Authors:  Yuko Tsuchiya; Kenji Mizuguchi
Journal:  Protein Sci       Date:  2016-01-20       Impact factor: 6.725

2.  Understanding differences between synthetic and natural antibodies can help improve antibody engineering.

Authors:  Anat Burkovitz; Yanay Ofran
Journal:  MAbs       Date:  2015-12-14       Impact factor: 5.857

Review 3.  Antibody specific epitope prediction-emergence of a new paradigm.

Authors:  Inbal Sela-Culang; Yanay Ofran; Bjoern Peters
Journal:  Curr Opin Virol       Date:  2015-03-31       Impact factor: 7.090

Review 4.  What does it mean to develop an HIV vaccine by rational design?

Authors:  Marc H V van Regenmortel
Journal:  Arch Virol       Date:  2020-11-29       Impact factor: 2.574

5.  Antibody engineering and therapeutics, The Annual Meeting of the Antibody Society: December 8-12, 2013, Huntington Beach, CA.

Authors:  Juan Carlos Almagro; Gary L Gilliland; Felix Breden; Jamie K Scott; Devin Sok; Matthias Pauthner; Janice M Reichert; Gustavo Helguera; Raiees Andrabi; Robert Mabry; Mathieu Bléry; James E Voss; Juha Laurén; Lubna Abuqayyas; Stefan Barghorn; Eshel Ben-Jacob; James E Crowe; James S Huston; Stephen Albert Johnston; Eric Krauland; Fridtjof Lund-Johansen; Wayne A Marasco; Paul W H I Parren; Kai Y Xu
Journal:  MAbs       Date:  2014-03-03       Impact factor: 5.857

6.  Origins of specificity and affinity in antibody-protein interactions.

Authors:  Hung-Pin Peng; Kuo Hao Lee; Jhih-Wei Jian; An-Suei Yang
Journal:  Proc Natl Acad Sci U S A       Date:  2014-06-17       Impact factor: 11.205

7.  Learning context-aware structural representations to predict antigen and antibody binding interfaces.

Authors:  Srivamshi Pittala; Chris Bailey-Kellogg
Journal:  Bioinformatics       Date:  2020-07-01       Impact factor: 6.937

8.  Modeling and docking of antibody structures with Rosetta.

Authors:  Brian D Weitzner; Jeliazko R Jeliazkov; Sergey Lyskov; Nicholas Marze; Daisuke Kuroda; Rahel Frick; Jared Adolf-Bryfogle; Naireeta Biswas; Roland L Dunbrack; Jeffrey J Gray
Journal:  Nat Protoc       Date:  2017-01-26       Impact factor: 13.491

9.  Blind prediction performance of RosettaAntibody 3.0: grafting, relaxation, kinematic loop modeling, and full CDR optimization.

Authors:  Brian D Weitzner; Daisuke Kuroda; Nicholas Marze; Jianqing Xu; Jeffrey J Gray
Journal:  Proteins       Date:  2014-03-31

10.  Computational B-cell epitope identification and production of neutralizing murine antibodies against Atroxlysin-I.

Authors:  Edgar Ernesto Gonzalez Kozlova; Loïc Cerf; Francisco Santos Schneider; Benjamin Thomas Viart; Christophe NGuyen; Bethina Trevisol Steiner; Sabrina de Almeida Lima; Franck Molina; Clara Guerra Duarte; Liza Felicori; Carlos Chávez-Olórtegui; Ricardo Andrez Machado-de-Ávila
Journal:  Sci Rep       Date:  2018-10-08       Impact factor: 4.379

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