Computational approaches to therapeutic antibody design: established methods and emerging trends

被引:115
|
作者
Norman, Richard A. [1 ,2 ]
Ambrosetti, Francesco [3 ]
Bonvin, Alexandre M. J. J. [4 ]
Colwell, Lucy J. [5 ]
Kelm, Sebastian [6 ]
Kumar, Sandeep [7 ]
Krawczyk, Konrad [8 ]
机构
[1] Pistoia Alliance Inc, Wakefield, MA USA
[2] Norman Consulting, Oslo, Norway
[3] Univ Utrecht, Utrecht, Netherlands
[4] Univ Utrecht, Computat Struct Biol, Utrecht, Netherlands
[5] Univ Cambridge, Chem, Cambridge, England
[6] UCB Celltech, Slough, Berks, England
[7] Boehringer Ingelheim GmbH & Co KG, Ingelheim, Germany
[8] NaturalAntibody, D-22393 Hamburg, Germany
基金
欧盟地平线“2020”;
关键词
homology modelling; therapeutic antibodies; docking; antibody-antigen complexes; databases; PROTEIN-PROTEIN DOCKING; COMPLEMENTARITY-DETERMINING REGIONS; AGGREGATION-PRONE REGIONS; B-CELL EPITOPES; SIDE-CHAIN CONFORMATIONS; STRUCTURE PREDICTION; WEB SERVER; MONOCLONAL-ANTIBODIES; VARIABLE DOMAINS; RATIONAL DESIGN;
D O I
10.1093/bib/bbz095
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.
引用
收藏
页码:1549 / 1567
页数:19
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