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Title: Understanding antibody binding sites
Author: Nowak, Jaroslaw
ISNI:       0000 0004 7232 1129
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2017
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Antibodies are soluble proteins produced by the adaptive immune system to bind and counteract invading pathogens. The binding properties of a typical human antibody are determined by the structure of its variable domain, composed of two chains – heavy and light and by the conformation of six loops located on the surface of the variable domain, known as Complementarity Determining Regions (CDRs). In the first chapter, we describe our analysis of the conformational space occupied by five out of six antibody CDRs (L1, L2, L3, H1 and H2) and the development of a novel, length-independent method for grouping these CDRs into structural clusters (canonical forms). We show that using our method we can increase coverage and precision of assigning CDR sequences into clusters. In the next chapter, we describe a method for ranking structural decoys of the CDR-H3 loop. We show that by computationally perturbing CDR-H3 decoys we can improve the performance of existing ranking methods. In the same chapter, we discuss the development of a method for high-throughput assignment of heavy-light chain orientation. The power of the method was demonstrated by assigning orientation to billions of potential Fv sequences. The third Chapter describes the analysis of a large dataset of CDR sequences with the aim of identifying sequence patterns responsible for the loops' structure. Using a neural network methodology, we found several groups of CDR sequences which might be indicative of previously-unseen conformations. In the final results Chapter, we describe how we used the structural knowledge developed throughout the rest of the thesis to create a novel pipeline for computational antibody design. We show that the binders developed using our methodology had similar features to available antibody therapeutics and low predicted propensity to cause an immunogenic response. These results demonstrate the potential for using computational methods for designing high affinity therapeutics with human properties.
Supervisor: Deane, Charlotte M. Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Statistics ; Machine learning ; Biology ; Sequences ; Protein Structure ; Antibodies ; Complementarity Determining Regions ; Protein Binding