Literature DB >> 25901362

Proteochemometric modeling of the antigen-antibody interaction: new fingerprints for antigen, antibody and epitope-paratope interaction.

Tianyi Qiu1, Han Xiao2, Qingchen Zhang1, Jingxuan Qiu1, Yiyan Yang1, Dingfeng Wu1, Zhiwei Cao3, Ruixin Zhu4.   

Abstract

Despite the high specificity between antigen and antibody binding, similar epitopes can be recognized or cross-neutralized by paratopes of antibody with different binding affinities. How to accurately characterize this slight variation which may or may not change the antigen-antibody binding affinity is a key issue in this area. In this report, by combining cylinder model with shell structure model, a new fingerprint was introduced to describe both the structural and physical-chemical features of the antigen and antibody protein. Furthermore, beside the description of individual protein, the specific epitope-paratope interaction fingerprint (EPIF) was developed to reflect the bond and the environment of the antigen-antibody interface. Finally, Proteochemometric Modeling of the antigen-antibody interaction was established and evaluated on 429 antigen-antibody complexes. By using only protein descriptors, our model achieved the best performance (R2 = 0.91, Qtest(2) = 0.68) among peers. Further, together with EPIF as a new cross-term, our model (R2 = 0.92, Qtest(2) = 0.74) can significantly outperform peers with multiplication of ligand and protein descriptors as a cross-term (R2 ≤ 0.81, Qtest(2) ≤ 0.44). Results illustrated that: 1) our newly designed protein fingerprints and EPIF can better describe the antigen-antibody interaction; 2) EPIF is a better and specific cross-term in Proteochemometric Modeling for antigen-antibody interaction. The fingerprints designed in this study will provide assistance to the description of antigen-antibody binding, and in future, it may be valuable help for the high-throughput antibody screening. The algorithm is freely available on request.

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Year:  2015        PMID: 25901362      PMCID: PMC4406442          DOI: 10.1371/journal.pone.0122416

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  36 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
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4.  A simple and fuzzy method to align and compare druggable ligand-binding sites.

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Journal:  Proteins       Date:  2008-06

5.  Discovery of similar regions on protein surfaces.

Authors:  Mary Ellen Bock; Claudio Garutti; Concettina Guerra
Journal:  J Comput Biol       Date:  2007-04       Impact factor: 1.479

6.  Solvent rearrangement in an antigen-antibody interface introduced by site-directed mutagenesis of the antibody combining site.

Authors:  X Ysern; B A Fields; T N Bhat; F A Goldbaum; W Dall'Acqua; F P Schwarz; R J Poljak; R A Mariuzza
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7.  Data mining of sequences and 3D structures of allergenic proteins.

Authors:  Ovidiu Ivanciuc; Catherine H Schein; Werner Braun
Journal:  Bioinformatics       Date:  2002-10       Impact factor: 6.937

8.  ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment.

Authors:  Janez Konc; Dusanka Janezic
Journal:  Bioinformatics       Date:  2010-03-19       Impact factor: 6.937

9.  SEPPA: a computational server for spatial epitope prediction of protein antigens.

Authors:  Jing Sun; Di Wu; Tianlei Xu; Xiaojing Wang; Xiaolian Xu; Lin Tao; Y X Li; Z W Cao
Journal:  Nucleic Acids Res       Date:  2009-05-22       Impact factor: 16.971

10.  Proteochemometric modeling of the bioactivity spectra of HIV-1 protease inhibitors by introducing protein-ligand interaction fingerprint.

Authors:  Qi Huang; Haixiao Jin; Qi Liu; Qiong Wu; Hong Kang; Zhiwei Cao; Ruixin Zhu
Journal:  PLoS One       Date:  2012-07-27       Impact factor: 3.240

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Authors:  Daria Augustyniak; Rafał Seredyński; Siobhán McClean; Justyna Roszkowiak; Bartosz Roszniowski; Darren L Smith; Zuzanna Drulis-Kawa; Paweł Mackiewicz
Journal:  Sci Rep       Date:  2018-03-21       Impact factor: 4.379

Review 3.  Biotherapeutics: Challenges and Opportunities for Predictive Toxicology of Monoclonal Antibodies.

Authors:  Dale E Johnson
Journal:  Int J Mol Sci       Date:  2018-11-21       Impact factor: 5.923

4.  Research on the Mechanism of Action of a Citrinin and Anti-Citrinin Antibody Based on Mimotope X27.

Authors:  Yanping Li; Yucheng Hu; Zhui Tu; Zhenqiang Ning; Qinghua He; Jinheng Fu
Journal:  Toxins (Basel)       Date:  2020-10-13       Impact factor: 4.546

  4 in total

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