A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-Pertussis toxoid antibodies

Richardson, Eve; Galson, Jacob D.; Kellam, Paul; Kelly, Dominic F.; Smith, Sarah E.; Palser, Anne L.; Watson, Sandra; Deane, Charlotte M.

doi:10.1101/2020.06.02.121129

Cited by 4 publications

(6 citation statements)

References 50 publications

(85 reference statements)

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“…This shows how Repertoire Structural Profiling could be used in conjunction with clonotyping to add geometric support to convergent clones being functionally equivalent. Recently published methods that can predict paratope similarity across all six CDRs [ 22 , 38 ] may be able to find considerably more antibodies within each distinct structure cluster with similar enough interaction profiles to be functionally equivalent. To facilitate future investigations into this area, we supply the Fv sequences across all ten individuals assigned to each ‘Public Baseline’ distinct structure.…”

Section: Resultsmentioning

confidence: 99%

“…This is because it assumes that antibodies require a similar genetic background and high CDRH3 sequence identity to achieve complementarity to the same epitope. In reality, similar binding site structures and paratopes can be achieved from different genetic origins [ 21 , 22 ] and with surprisingly low CDRH3 sequence identity [ 23 ] (conversely, false positives can arise where antibodies with high CDRH3 sequence identity and the same genetic origins adopt markedly different binding site topologies [ 23 ]). It is also the case that not every epitope is naturally suited to CDRH3-dominated binding, instead preferring broader engagement by multiple CDRs [ 24 ], putting less selection pressure on CDRH3 sequence identity.…”

Section: Introductionmentioning

confidence: 99%

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Public Baseline and shared response structures support the theory of antibody repertoire functional commonality

et al. 2021

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The naïve antibody/B-cell receptor (BCR) repertoires of different individuals ought to exhibit significant functional commonality, given that most pathogens trigger an effective antibody response to immunodominant epitopes. Sequence-based repertoire analysis has 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 search for evidence of geometric similarity/convergence across human antibody repertoires. We first structurally profile naïve (‘baseline’) antibody diversity using snapshots from 41 unrelated individuals, predicting all modellable distinct structures within each repertoire. This analysis uncovers a high (much greater than random) 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 find levels of BCR commonality commensurate with epitope immunodominance and could therefore be harnessed to find more genetically distant antibodies with same-epitope complementarity. 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). We show that Antibody Model Libraries 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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Public Baseline and shared response structures support the theory of antibody repertoire functional commonality

et al. 2021

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“…A very recent paper uses a microfluidics-based approach to experimentally evaluate antibody specificity in high throughput [ 43 ] (of particular relevance to our results, they find no correlation between and measured affinity). Another recent paper clusters sequences into specificity groups using amino acid hamming distance on a subset of CDR residues deemed likely to contribute to binding [ 44 ]. These paratope residues are chosen using a deep learning approach trained on a large database of existing structural data [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Using B cell receptor lineage structures to predict affinity

Ralph¹,

Matsen²

2020

PLoS Comput Biol

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We are frequently faced with a large collection of antibodies, and want to select those with highest affinity for their cognate antigen. When developing a first-line therapeutic for a novel pathogen, for instance, we might look for such antibodies in patients that have recovered. There exist effective experimental methods of accomplishing this, such as cell sorting and baiting; however they are time consuming and expensive. Next generation sequencing of B cell receptor (BCR) repertoires offers an additional source of sequences that could be tapped if we had a reliable method of selecting those coding for the best antibodies. In this paper we introduce a method that uses evolutionary information from the family of related sequences that share a naive ancestor to predict the affinity of each resulting antibody for its antigen. When combined with information on the identity of the antigen, this method should provide a source of effective new antibodies. We also introduce a method for a related task: given an antibody of interest and its inferred ancestral lineage, which branches in the tree are likely to harbor key affinity-increasing mutations? We evaluate the performance of these methods on a wide variety of simulated samples, as well as two real data samples. These methods are implemented as part of continuing development of the partis BCR inference package, available at https://github.com/psathyrella/partis. Comments Please post comments or questions on this paper as new issues at https://git.io/Jvxkn.

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“…A very recent paper uses a microfluidics-based approach to experimentally evaluate antibody specificity in high throughput [43] (of particular relevance to our results, they find no correlation between n-shm and measured affinity). Another recent paper clusters sequences into specificity groups using amino acid hamming distance on a subset of CDR residues deemed likely to contribute to binding [44].…”

Section: Discussionmentioning

confidence: 99%

Using B cell receptor lineage structures to predict affinity

Ralph

Matsen

2020

Preprint

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We are frequently faced with a large collection of antibodies, and want to select those with highest affinity for their cognate antigen. When developing a first-line therapeutic for a novel pathogen, for instance, we might look for such antibodies in patients that have recovered. There exist effective experimental methods of accomplishing this, such as cell sorting and baiting; however they are time consuming and expensive. Next generation sequencing of B cell receptor (BCR) repertoires offers an additional source of sequences that could be tapped if we had a reliable method of selecting those coding for the best antibodies. In this paper we introduce a method that uses evolutionary information from the family of related sequences that share a naive ancestor to predict the affinity of each resulting antibody for its antigen. When combined with information on the identity of the antigen, this method should provide a source of effective new antibodies. We also introduce a method for a related task: given an antibody of interest and its inferred ancestral lineage, which branches in the tree are likely to harbor key affinity-increasing mutations? These methods are implemented as part of continuing development of the partis BCR inference package, available at https://github.com/psathyrella/partis. Comments.Please post comments or questions on this paper as new issues at https://git.io/ Jvxkn.

show abstract

A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-Pertussis toxoid antibodies

Cited by 4 publications

References 50 publications

Public Baseline and shared response structures support the theory of antibody repertoire functional commonality

Public Baseline and shared response structures support the theory of antibody repertoire functional commonality

Using B cell receptor lineage structures to predict affinity

Using B cell receptor lineage structures to predict affinity

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