Proteochemometric Modeling of the Antigen-Antibody Interaction : New Fingerprints for Antigen, Antibody and Epitope-Paratope Interaction

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Qiu , T , Xiao , H , Zhang , Q , Qiu , J , Yang , Y , Wu , D , Cao , Z & Zhu , R 2015 , ' Proteochemometric Modeling of the Antigen-Antibody Interaction : New Fingerprints for Antigen, Antibody and Epitope-Paratope Interaction ' , PLoS One , vol. 10 , no. 4 , 0122416 . https://doi.org/10.1371/journal.pone.0122416

Title: Proteochemometric Modeling of the Antigen-Antibody Interaction : New Fingerprints for Antigen, Antibody and Epitope-Paratope Interaction
Author: Qiu, Tianyi; Xiao, Han; Zhang, Qingchen; Qiu, Jingxuan; Yang, Yiyan; Wu, Dingfeng; Cao, Zhiwei; Zhu, Ruixin
Contributor: University of Helsinki, Department of Computer Science
Date: 2015-04-22
Language: eng
Number of pages: 15
Belongs to series: PLoS One
ISSN: 1932-6203
URI: http://hdl.handle.net/10138/160349
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 (R-2 = 0: 91; Q(test)(2) = 0: 68) among peers. Further, together with EPIF as a new cross-term, our model (R-2 = 0: 92; Q(2) test = 0: 74) can significantly outperform peers with multiplication of ligand and protein descriptors as a cross-term (R2
Subject: PROTEIN-BINDING SITES
CRYSTAL-STRUCTURE
SPACE
RECOGNITION
PREDICTION
INHIBITORS
INTERFACE
ALIGNMENT
PATTERNS
SURFACES
113 Computer and information sciences
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