Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.669728
Title: Computational antibody design
Author: Krawczyk, Konrad
ISNI:       0000 0004 5369 4173
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2013
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Abstract:
Antibodies are a class of proteins vital in mediating immune responses in vertebrates. Their binding site is highly malleable, allowing them to bind virtually any antigen. The versatility of antibody binding sites has received much attention from the pharmaceutical industry, marking them out as the most important category of biopharmaceuticals. The development of antibodies which bind to a specific antigen has thus far been achieved by costly and time-consuming experimental screening campaigns. However, in recent years computational approaches to antibody design have started to emerge, which offer an alternative. Computational antibody design techniques focus on determination of the binding site on the antibody, antibody-modelling, antibody-antigen docking and prediction of the binding site on the antigen. Here, we explore aspects of computational antibody design with the aim of gaining a better understanding of antibody-antigen interactions and improving existing artificial antibody design tools. We start by demonstrating our structural antibody database which has become a primary resource for antibody structural information. This is followed by a detailed analysis of the antibodyantigen interactions. The information gathered from this analysis allowed us to create an antibody contact site prediction tool, Antibody i-Patch. This tool was then employed to develop a local antibody-antigen docking pipeline, which used knowledge of the binding site of the antigen. We then tackled the global antibody-antigen docking problem by developing EpiPred, antigen binding site predictor which was employed in our global antibody-antigen docking pipeline.
Supervisor: Deane, Charlotte M.; Shi, Jiye; Baker, Terry Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.669728  DOI: Not available
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