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Article ; Online: Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling.

Raybould, Matthew I J / Turnbull, Oliver M / Suter, Annabel / Guloglu, Bora / Deane, Charlotte M

Communications biology

2024 Volume 7, Issue 1, Page(s) 62

Abstract: Antibodies with lambda light chains (λ-antibodies) are generally considered to be less developable than those with kappa light chains (κ-antibodies). Though this hypothesis has not been formally established, it has led to substantial systematic biases in ...

Abstract	Antibodies with lambda light chains (λ-antibodies) are generally considered to be less developable than those with kappa light chains (κ-antibodies). Though this hypothesis has not been formally established, it has led to substantial systematic biases in drug discovery pipelines and thus contributed to kappa dominance amongst clinical-stage therapeutics. However, the identification of increasing numbers of epitopes preferentially engaged by λ-antibodies shows there is a functional cost to neglecting to consider them as potential lead candidates. Here, we update our Therapeutic Antibody Profiler (TAP) tool to use the latest data and machine learning-based structure prediction, and apply it to evaluate developability risk profiles for κ-antibodies and λ-antibodies based on their surface physicochemical properties. We find that while human λ-antibodies on average have a higher risk of developability issues than κ-antibodies, a sizeable proportion are assigned lower-risk profiles by TAP and should represent more tractable candidates for therapeutic development. Through a comparative analysis of the low- and high-risk populations, we highlight opportunities for strategic design that TAP suggests would enrich for more developable λ-antibodies. Overall, we provide context to the differing developability of κ- and λ-antibodies, enabling a rational approach to incorporate more diversity into the initial pool of immunotherapeutic candidates.
MeSH term(s)	Humans ; Antibodies/therapeutic use ; Drug Discovery ; Epitopes ; Machine Learning ; Surface Properties
Chemical Substances	Antibodies ; Epitopes
Language	English
Publishing date	2024-01-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-023-05744-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies.

Raybould, Matthew I J / Nissley, Daniel A / Kumar, Sandeep / Deane, Charlotte M

Frontiers in immunology

2023 Volume 13, Page(s) 1080596

Abstract: T-cell receptor-mimetic antibodies (TCRms) targeting disease-associated peptides presented by Major Histocompatibility Complexes (pMHCs) are set to become a major new drug modality. However, we lack a general understanding of how TCRms engage pMHC ... ...

Abstract	T-cell receptor-mimetic antibodies (TCRms) targeting disease-associated peptides presented by Major Histocompatibility Complexes (pMHCs) are set to become a major new drug modality. However, we lack a general understanding of how TCRms engage pMHC targets, which is crucial for predicting their specificity and safety. Several new structures of TCRm:pMHC complexes have become available in the past year, providing sufficient initial data for a holistic analysis of TCRms as a class of pMHC binding agents. Here, we profile the complete set of TCRm:pMHC complexes against representative TCR:pMHC complexes to quantify the TCR-likeness of their pMHC engagement. We find that intrinsic molecular differences between antibodies and TCRs lead to fundamentally different roles for their heavy/light chains and Complementarity-Determining Region loops during antigen recognition. The idiotypic properties of antibodies may increase the likelihood of TCRms engaging pMHCs with less peptide selectivity than TCRs. However, the pMHC recognition features of some TCRms, including the two TCRms currently in clinical trials, can be remarkably TCR-like. The insights gained from this study will aid in the rational design and optimisation of next-generation TCRms.
MeSH term(s)	Receptors, Antigen, T-Cell ; Peptides ; Complementarity Determining Regions/chemistry ; Histocompatibility Antigens ; Major Histocompatibility Complex ; Antibodies
Chemical Substances	Receptors, Antigen, T-Cell ; Peptides ; Complementarity Determining Regions ; Histocompatibility Antigens ; Antibodies
Language	English
Publishing date	2023-01-09
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2022.1080596
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: SAbDab in the age of biotherapeutics: updates including SAbDab-nano, the nanobody structure tracker.

Schneider, Constantin / Raybould, Matthew I J / Deane, Charlotte M

Nucleic acids research

2022 Volume 50, Issue D1, Page(s) D1368–D1372

Abstract: In 2013, we released the Structural Antibody Database (SAbDab), a publicly available repository of experimentally determined antibody structures. In the interim, the rapid increase in the number of antibody structure depositions to the Protein Data Bank, ...

Abstract	In 2013, we released the Structural Antibody Database (SAbDab), a publicly available repository of experimentally determined antibody structures. In the interim, the rapid increase in the number of antibody structure depositions to the Protein Data Bank, driven primarily by increased interest in antibodies as biotherapeutics, has led us to implement several improvements to the original database infrastructure. These include the development of SAbDab-nano, a sub-database that tracks nanobodies (heavy chain-only antibodies) which have seen a particular growth in attention from both the academic and pharmaceutical research communities over the past few years. Both SAbDab and SAbDab-nano are updated weekly, comprehensively annotated with the latest features described here, and are freely accessible at opig.stats.ox.ac.uk/webapps/newsabdab/.
MeSH term(s)	Antibodies/genetics ; Antibodies/immunology ; Databases, Genetic ; Humans ; Immunoglobulin Heavy Chains/genetics ; Immunoglobulin Heavy Chains/immunology ; Single-Domain Antibodies/genetics ; Single-Domain Antibodies/immunology ; Single-Domain Antibodies/therapeutic use ; Software
Chemical Substances	Antibodies ; Immunoglobulin Heavy Chains ; Single-Domain Antibodies
Language	English
Publishing date	2022-02-10
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkab1050
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling

Matthew I. J. Raybould / Oliver M. Turnbull / Annabel Suter / Bora Guloglu / Charlotte M. Deane

Communications Biology, Vol 7, Iss 1, Pp 1-

2024 Volume 13

Abstract: Abstract Antibodies with lambda light chains (λ-antibodies) are generally considered to be less developable than those with kappa light chains (κ-antibodies). Though this hypothesis has not been formally established, it has led to substantial systematic ... ...

Abstract	Abstract Antibodies with lambda light chains (λ-antibodies) are generally considered to be less developable than those with kappa light chains (κ-antibodies). Though this hypothesis has not been formally established, it has led to substantial systematic biases in drug discovery pipelines and thus contributed to kappa dominance amongst clinical-stage therapeutics. However, the identification of increasing numbers of epitopes preferentially engaged by λ-antibodies shows there is a functional cost to neglecting to consider them as potential lead candidates. Here, we update our Therapeutic Antibody Profiler (TAP) tool to use the latest data and machine learning-based structure prediction, and apply it to evaluate developability risk profiles for κ-antibodies and λ-antibodies based on their surface physicochemical properties. We find that while human λ-antibodies on average have a higher risk of developability issues than κ-antibodies, a sizeable proportion are assigned lower-risk profiles by TAP and should represent more tractable candidates for therapeutic development. Through a comparative analysis of the low- and high-risk populations, we highlight opportunities for strategic design that TAP suggests would enrich for more developable λ-antibodies. Overall, we provide context to the differing developability of κ- and λ-antibodies, enabling a rational approach to incorporate more diversity into the initial pool of immunotherapeutic candidates.
Keywords	Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Therapeutic Antibody Profiler for Computational Developability Assessment.

Raybould, Matthew I J / Deane, Charlotte M

Methods in molecular biology (Clifton, N.J.)

2021 Volume 2313, Page(s) 115–125

Abstract: The need to consider an antibody's "developability" (immunogenicity, solubility, specificity, stability, manufacturability, and storability) is now well understood in therapeutic antibody design. Predicting these properties rapidly and inexpensively is ... ...

Abstract	The need to consider an antibody's "developability" (immunogenicity, solubility, specificity, stability, manufacturability, and storability) is now well understood in therapeutic antibody design. Predicting these properties rapidly and inexpensively is critical to industrial workflows, to avoid devoting resources to non-productive candidates. Here, we describe a high-throughput computational developability assessment tool, the Therapeutic Antibody Profiler (TAP), which assesses the physicochemical "druglikeness" of an antibody candidate. Input variable domain sequences are converted to three-dimensional structural models, and then five developability-linked molecular surface descriptors are calculated and compared to advanced-stage clinical therapeutics. Values at the extremes of/outside of the distributions seen in therapeutics imply an increased risk of developability issues. Therefore, TAP, starting only from sequence information, provides a route to rapidly identifying drug candidate antibodies that are likely to have poor developability. Our web application ( opig.stats.ox.ac.uk/webapps/tap ) profiles input antibody sequences against a continually updated reference set of clinical therapeutics.
MeSH term(s)	Antibodies/therapeutic use ; Antibodies, Monoclonal ; Software ; Solubility ; Workflow
Chemical Substances	Antibodies ; Antibodies, Monoclonal
Language	English
Publishing date	2021-09-03
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-1450-1_5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Current strategies for detecting functional convergence across B-cell receptor repertoires.

Raybould, Matthew I J / Rees, Anthony R / Deane, Charlotte M

mAbs

2021 Volume 13, Issue 1, Page(s) 1996732

Abstract: Convergence across B-cell receptor (BCR) and antibody repertoires has become instrumental in prioritizing candidates in recent rapid therapeutic antibody discovery campaigns. It has also increased our understanding of the immune system, providing ... ...

Abstract	Convergence across B-cell receptor (BCR) and antibody repertoires has become instrumental in prioritizing candidates in recent rapid therapeutic antibody discovery campaigns. It has also increased our understanding of the immune system, providing evidence for the preferential selection of BCRs to particular (immunodominant) epitopes post vaccination/infection. These important implications for both drug discovery and immunology mean that it is essential to consider the optimal way to combine experimental and computational technology when probing BCR repertoires for convergence signatures. Here, we discuss the theoretical basis for observing BCR repertoire functional convergence and explore factors of study design that can impact functional signal. We also review the computational arsenal available to detect antibodies with similar functional properties, highlighting opportunities enabled by recent clustering algorithms that exploit structural similarities between BCRs. Finally, we suggest future areas of development that should increase the power of BCR repertoire functional clustering.
MeSH term(s)	Antibodies ; Receptors, Antigen, B-Cell/genetics
Chemical Substances	Antibodies ; Receptors, Antigen, B-Cell
Language	English
Publishing date	2021-09-14
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2537838-7
ISSN	1942-0870 ; 1942-0870
ISSN (online)	1942-0870
ISSN	1942-0870
DOI	10.1080/19420862.2021.1996732
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope.

Spoendlin, Fabian C / Abanades, Brennan / Raybould, Matthew I J / Wong, Wing Ki / Georges, Guy / Deane, Charlotte M

Frontiers in molecular biosciences

2023 Volume 10, Page(s) 1237621

Abstract: The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that ... ...

Abstract	The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).
Language	English
Publishing date	2023-09-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2814330-9
ISSN	2296-889X
ISSN	2296-889X
DOI	10.3389/fmolb.2023.1237621
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures.

Abanades, Brennan / Olsen, Tobias H / Raybould, Matthew I J / Aguilar-Sanjuan, Broncio / Wong, Wing Ki / Georges, Guy / Bujotzek, Alexander / Deane, Charlotte M

Nucleic acids research

2023 Volume 52, Issue D1, Page(s) D545–D551

Abstract: Antibodies are key proteins of the adaptive immune system, and there exists a large body of academic literature and patents dedicated to their study and concomitant conversion into therapeutics, diagnostics, or reagents. These documents often contain ... ...

Abstract	Antibodies are key proteins of the adaptive immune system, and there exists a large body of academic literature and patents dedicated to their study and concomitant conversion into therapeutics, diagnostics, or reagents. These documents often contain extensive functional characterisations of the sets of antibodies they describe. However, leveraging these heterogeneous reports, for example to offer insights into the properties of query antibodies of interest, is currently challenging as there is no central repository through which this wide corpus can be mined by sequence or structure. Here, we present PLAbDab (the Patent and Literature Antibody Database), a self-updating repository containing over 150,000 paired antibody sequences and 3D structural models, of which over 65 000 are unique. We describe the methods used to extract, filter, pair, and model the antibodies in PLAbDab, and showcase how PLAbDab can be searched by sequence, structure, or keyword. PLAbDab uses include annotating query antibodies with potential antigen information from similar entries, analysing structural models of existing antibodies to identify modifications that could improve their properties, and facilitating the compilation of bespoke datasets of antibody sequences/structures that bind to a specific antigen. PLAbDab is freely available via Github (https://github.com/oxpig/PLAbDab) and as a searchable webserver (https://opig.stats.ox.ac.uk/webapps/plabdab/).
MeSH term(s)	Antibodies/chemistry ; Antibodies/genetics ; Antigens/metabolism ; Databases, Factual ; Models, Molecular ; Patents as Topic ; Internet
Chemical Substances	Antibodies ; Antigens
Language	English
Publishing date	2023-12-20
Publishing country	England
Document type	Journal Article
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkad1056
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: CoV-AbDab: the coronavirus antibody database.

Raybould, Matthew I J / Kovaltsuk, Aleksandr / Marks, Claire / Deane, Charlotte M

Bioinformatics (Oxford, England)

2020 Volume 37, Issue 5, Page(s) 734–735

Abstract: Motivation: The emergence of a novel strain of betacoronavirus, SARS-CoV-2, has led to a pandemic that has been associated with over 700 000 deaths as of August 5, 2020. Research is ongoing around the world to create vaccines and therapies to minimize ... ...

Abstract	Motivation: The emergence of a novel strain of betacoronavirus, SARS-CoV-2, has led to a pandemic that has been associated with over 700 000 deaths as of August 5, 2020. Research is ongoing around the world to create vaccines and therapies to minimize rates of disease spread and mortality. Crucial to these efforts are molecular characterizations of neutralizing antibodies to SARS-CoV-2. Such antibodies would be valuable for measuring vaccine efficacy, diagnosing exposure and developing effective biotherapeutics. Here, we describe our new database, CoV-AbDab, which already contains data on over 1400 published/patented antibodies and nanobodies known to bind to at least one betacoronavirus. This database is the first consolidation of antibodies known to bind SARS-CoV-2 as well as other betacoronaviruses such as SARS-CoV-1 and MERS-CoV. It contains relevant metadata including evidence of cross-neutralization, antibody/nanobody origin, full variable domain sequence (where available) and germline assignments, epitope region, links to relevant PDB entries, homology models and source literature. Results: On August 5, 2020, CoV-AbDab referenced sequence information on 1402 anti-coronavirus antibodies and nanobodies, spanning 66 papers and 21 patents. Of these, 1131 bind to SARS-CoV-2. Availabilityand implementation: CoV-AbDab is free to access and download without registration at http://opig.stats.ox.ac.uk/webapps/coronavirus. Community submissions are encouraged. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Antibodies, Neutralizing ; Antibodies, Viral ; COVID-19 ; Humans ; Middle East Respiratory Syndrome Coronavirus ; SARS-CoV-2 ; Spike Glycoprotein, Coronavirus
Chemical Substances	Antibodies, Neutralizing ; Antibodies, Viral ; Spike Glycoprotein, Coronavirus
Keywords	covid19
Language	English
Publishing date	2020-08-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa739
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Public Baseline and shared response structures support the theory of antibody repertoire functional commonality.

Raybould, Matthew I J / Marks, Claire / Kovaltsuk, Aleksandr / Lewis, Alan P / Shi, Jiye / Deane, Charlotte M

PLoS computational biology

2021 Volume 17, Issue 3, Page(s) e1008781

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Antibodies ; Antibody Diversity ; B-Lymphocytes/chemistry ; B-Lymphocytes/immunology ; B-Lymphocytes/metabolism ; Computational Biology ; Databases, Genetic ; Humans ; Immunodominant Epitopes
Chemical Substances	Antibodies ; Immunodominant Epitopes
Language	English
Publishing date	2021-03-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1008781
Database	MEDical Literature Analysis and Retrieval System OnLINE

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