Observed Antibody Space: A Resource for Data Mining Next-Generation Sequencing of Antibody Repertoires

Kovaltsuk, Aleksandr; Leem, Jinwoo; Kelm, Sebastian; Snowden, James; Deane, Charlotte M.; Krawczyk, Konrad

doi:10.4049/jimmunol.1800708

Cited by 210 publications

(253 citation statements)

References 92 publications

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“…The set of viable antibody sequences, functional antibody sequence space, lies much below the theoretically possible (28) and close to the already observed and annotated antibody sequence space (29).…”

Section: Antibody Clonal Representation In Sequence Spacesupporting

confidence: 67%

Network organization of antibody interactions in sequence and structure space: the RADARS model

Prechl¹

2018

Preprint

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Adaptive immunity in vertebrates represents a complex self-organizing network of protein interactions that develops throughout the lifetime of an individual. While deep sequencing of the immune-receptor repertoire may reveal clonal relationships, functional interpretation of such data is hampered by the inherent limitations of converting sequence to structure to function.In this paper a novel model of antibody interaction space and network, termed radial adjustment of system resolution, RADARS, is proposed. The model is based on the radial growth of interaction affinity of antibodies towards an infinity of directions in structure space, each direction representing particular shapes of antigen epitopes. Levels of interaction affinity appear as free energy shells of the system, where hierarchical B-cell development and differentiation takes place. Equilibrium in this immunological thermodynamic system can be described by a power-law distribution of antibody free energies with an ideal network degree exponent of phi square, representing a scale-free fractal network of antibody interactions. Plasma cells are network hubs, memory B cells are nodes with intermediate degrees and B1 cells represent nodes with minimal degree.Thus, the RADARS model implies that antibody structure space develops against an infinite antigen structure space via interactions that are individually immunologically controlled, but on a systems level are organized by thermodynamic probability distributions. The network of interactions, which control B-cell development and differentiation, represent pathways of antigen removal on systems level. Understanding such quantitative network properties of the system should help the organization of sequence-derived structural data, offering the possibility to relate sequence to function in a complex, self-organizing biological system.Graphical AbstractGraphical abstract

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Section: Antibody Clonal Representation In Sequence Spacesupporting

confidence: 67%

Network organization of antibody interactions in sequence and structure space: the RADARS model

Prechl¹

2018

Preprint

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“…This is especially pertinent in the face of large-scale organized efforts to make naturally sourced antibody NGS data 8 and analytics 9,10 more accessible 11 . Specifically, we recently created the Observed Antibody Space (OAS) database that curates the NGS antibody data from public archives and makes them available for easy processing 12 . OAS currently holds ~1b (~960m heavy chain and ~60m light chain) sequences from 60 independent studies.…”

Section: Introductionmentioning

confidence: 99%

Looking for Therapeutic Antibodies in Next Generation Sequencing Repositories

Krawczyk¹,

Kovaltsuk

Raybould

et al. 2019

Preprint

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Recently it has become possible to query the great diversity of natural antibody repertoires using Next Generation Sequencing (NGS). These methods are capable of producing millions of sequences in a single experiment. Here we compare Clinical Stage Therapeutic antibodies to the ~1b sequences from 60 independent sequencing studies in the Observed Antibody Space Database. Of the 242 post Phase I antibodies, we find 16 with sequence identity matches of 95% or better for both heavy and light chains. There are also 54 perfect matches to therapeutic CDR-H3 regions in the NGS outputs, suggesting a nontrivial amount of convergence between naturally observed sequences and those developed artificially. This has potential implications for both the discovery of antibody therapeutics and the legal protection of commercial antibodies.

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“…We then use data from a longitudinal Ig-seq flu vaccination study by Gupta et al (5) to measure three individuals' structural responses to exposure to a common antigen. Both translated Ig-seq datasets were downloaded from the Observed Antibody Space (OAS) database (9), retaining only the 41 Gidoni volunteers with sufficiently deep reads (see Methods). We used an updated version of our repertoire structural profiling pipeline (29) for improved accuracy in CDR structure and VH-VL interface orientation prediction (see Methods, SI Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, we believe that our approach should find immediate applicability in both in silico and in vitro screening. We have made available the 'Public Baseline' and 'Public Response' Antibody Model Libraries for further investigation, and will continue to build and share the Antibody Model Libraries derived from other unpaired and paired VH+VL datasets in the Observed Antibody Space database (9).…”

Section: Discussionmentioning

confidence: 99%

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Evidence of Antibody Repertoire Functional Convergence through Public Baseline and Shared Response Structures

Raybould¹,

Marks²,

Kovaltsuk³

et al. 2020

Preprint

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The antibody repertoires of different individuals ought to exhibit significant functional commonality, given that most pathogens trigger a successful immune response in most people. Sequence-based approaches have so far offered little evidence for this phenomenon. For example, a recent study estimated the number of shared ('public') antibody clonotypes in circulating baseline repertoires to be around 0.02% across ten unrelated individuals. However, to engage the same epitope, antibodies only require a similar binding site structure and the presence of key paratope interactions, which can occur even when their sequences are dissimilar. Here, we investigate functional convergence in human antibody repertoires by comparing the antibody structures they contain. We first structurally profile baseline antibody diversity (using snapshots from 41 unrelated individuals), predicting all modellable distinct structures within each repertoire. This analysis uncovers a high degree of structural commonality. For instance, around 3% of distinct structures are common to the ten most diverse individual samples ('Public Baseline' structures). Our approach is the first computational method to provide support for the long-assumed levels of baseline repertoire functional commonality. We then apply the same structural profiling approach to repertoire snapshots from three individuals before and after flu vaccination, detecting a convergent structural drift indicative of recognising similar epitopes ('Public Response' structures). Antibody Model Libraries (AMLs) derived from Public Baseline and Public Response structures represent a powerful geometric basis set of low-immunogenicity candidates exploitable for general or target-focused therapeutic antibody screening. antibody repertoire | structural profiling | public structures | antibody model libraries | antibody screening Correspondence: deane@stats.ox.ac.uk

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Observed Antibody Space: A Resource for Data Mining Next-Generation Sequencing of Antibody Repertoires

Cited by 210 publications

References 92 publications

Network organization of antibody interactions in sequence and structure space: the RADARS model

Network organization of antibody interactions in sequence and structure space: the RADARS model

Looking for Therapeutic Antibodies in Next Generation Sequencing Repositories

Evidence of Antibody Repertoire Functional Convergence through Public Baseline and Shared Response Structures

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