Literature DB >> 20304974

Use of amino acid composition to predict epitope residues of individual antibodies.

Shinji Soga1, Daisuke Kuroda, Hiroki Shirai, Masato Kobori, Noriaki Hirayama.   

Abstract

We identified specific amino acid propensities at the interfaces of antigen-antibody interactions in non-redundant qualified antigen-antibody complex structures from Protein Data Bank. Propensities were expressed by the frequency of each of the 20 x 20 standard amino acid pairs that appeared at the interfaces of the complexes and were named the antibody-specific epitope propensity (ASEP) index. Using this index, we developed a novel method of predicting epitope residues for individual antibodies by narrowing down candidate epitope residues which was predicted by the conventional method. The 74 benchmarked antigens were used in ASEP prediction. The efficiency of this method was assessed using the leave-one-out approach. On elimination of residues with ASEP indices in the lowest 10% of all measured, true positives were enriched for 49 antigens. On subsequent elimination of residues with ASEP indices in the lowest 50%, true positives were enriched for 40 of the 74 antigens assessed. The ASEP index is the first benchmark proposed to predict epitope residues for an individual antibody. Used in combination with mutation experiments, this index has the potential to markedly increase the success ratio of epitope analysis.

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Year:  2010        PMID: 20304974     DOI: 10.1093/protein/gzq014

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


  16 in total

Review 1.  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 2.  How repertoire data are changing antibody science.

Authors:  Claire Marks; Charlotte M Deane
Journal:  J Biol Chem       Date:  2020-05-14       Impact factor: 5.157

3.  Fuzzy clustering of physicochemical and biochemical properties of amino acids.

Authors:  Indrajit Saha; Ujjwal Maulik; Sanghamitra Bandyopadhyay; Dariusz Plewczynski
Journal:  Amino Acids       Date:  2011-10-13       Impact factor: 3.520

4.  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

Review 5.  Computer-aided antibody design.

Authors:  Daisuke Kuroda; Hiroki Shirai; Matthew P Jacobson; Haruki Nakamura
Journal:  Protein Eng Des Sel       Date:  2012-06-02       Impact factor: 1.650

Review 6.  The structural basis of antibody-antigen recognition.

Authors:  Inbal Sela-Culang; Vered Kunik; Yanay Ofran
Journal:  Front Immunol       Date:  2013-10-08       Impact factor: 7.561

7.  Improving B-cell epitope prediction and its application to global antibody-antigen docking.

Authors:  Konrad Krawczyk; Xiaofeng Liu; Terry Baker; Jiye Shi; Charlotte M Deane
Journal:  Bioinformatics       Date:  2014-04-21       Impact factor: 6.937

Review 8.  Progress and challenges in predicting protein interfaces.

Authors:  Reyhaneh Esmaielbeiki; Konrad Krawczyk; Bernhard Knapp; Jean-Christophe Nebel; Charlotte M Deane
Journal:  Brief Bioinform       Date:  2015-05-13       Impact factor: 11.622

Review 9.  Bioinformatics resources and tools for conformational B-cell epitope prediction.

Authors:  Pingping Sun; Haixu Ju; Zhenbang Liu; Qiao Ning; Jian Zhang; Xiaowei Zhao; Yanxin Huang; Zhiqiang Ma; Yuxin Li
Journal:  Comput Math Methods Med       Date:  2013-07-21       Impact factor: 2.238

10.  Predicting HIV-1 broadly neutralizing antibody epitope networks using neutralization titers and a novel computational method.

Authors:  Mark C Evans; Pham Phung; Agnes C Paquet; Anvi Parikh; Christos J Petropoulos; Terri Wrin; Mojgan Haddad
Journal:  BMC Bioinformatics       Date:  2014-03-19       Impact factor: 3.169

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