Literature DB >> 24006373

Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking.

Konrad Krawczyk1, Terry Baker, Jiye Shi, Charlotte M Deane.   

Abstract

Antibodies are a class of proteins indispensable for the vertebrate immune system. The general architecture of all antibodies is very similar, but they contain a hypervariable region which allows millions of antibody variants to exist, each of which can bind to different molecules. This binding malleability means that antibodies are an increasingly important category of biopharmaceuticals and biomarkers. We present Antibody i-Patch, a method that annotates the most likely antibody residues to be in contact with the antigen. We show that our predictions correlate with energetic importance and thus we argue that they may be useful in guiding mutations in the artificial affinity maturation process. Using our predictions as constraints for a rigid-body docking algorithm, we are able to obtain high-quality results in minutes. Our annotation method and re-scoring system for docking achieve their predictive power by using antibody-specific statistics. Antibody i-Patch is available from http://www.stats.ox.ac.uk/research/proteins/resources.

Keywords:  CDR; antibody; docking

Mesh:

Substances:

Year:  2013        PMID: 24006373     DOI: 10.1093/protein/gzt043

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


  25 in total

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Authors:  Alexander Bujotzek; Florian Lipsmeier; Seth F Harris; Jörg Benz; Andreas Kuglstatter; Guy Georges
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2.  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

3.  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

Review 4.  How repertoire data are changing antibody science.

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Journal:  J Biol Chem       Date:  2020-05-14       Impact factor: 5.157

5.  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

Review 6.  Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.

Authors:  Rahmad Akbar; Habib Bashour; Puneet Rawat; Philippe A Robert; Eva Smorodina; Tudor-Stefan Cotet; Karine Flem-Karlsen; Robert Frank; Brij Bhushan Mehta; Mai Ha Vu; Talip Zengin; Jose Gutierrez-Marcos; Fridtjof Lund-Johansen; Jan Terje Andersen; Victor Greiff
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

7.  IsAb: a computational protocol for antibody design.

Authors:  Tianjian Liang; Hui Chen; Jiayi Yuan; Chen Jiang; Yixuan Hao; Yuanqiang Wang; Zhiwei Feng; Xiang-Qun Xie
Journal:  Brief Bioinform       Date:  2021-09-02       Impact factor: 11.622

8.  An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants.

Authors:  Johnathan D Guest; Thom Vreven; Jing Zhou; Iain Moal; Jeliazko R Jeliazkov; Jeffrey J Gray; Zhiping Weng; Brian G Pierce
Journal:  Structure       Date:  2021-02-03       Impact factor: 5.871

9.  Scoring docking conformations using predicted protein interfaces.

Authors:  Reyhaneh Esmaielbeiki; Jean-Christophe Nebel
Journal:  BMC Bioinformatics       Date:  2014-06-06       Impact factor: 3.169

10.  SAbDab: the structural antibody database.

Authors:  James Dunbar; Konrad Krawczyk; Jinwoo Leem; Terry Baker; Angelika Fuchs; Guy Georges; Jiye Shi; Charlotte M Deane
Journal:  Nucleic Acids Res       Date:  2013-11-08       Impact factor: 16.971

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