AbDiver – A tool to explore the natural antibody landscape to aid therapeutic design

Młokosiewicz, Jakub; Deszyński, Piotr; Wilman, Wiktoria; Jaszczyszyn, Igor; Ganesan, Rajkumar; Kovaltsuk, Aleksandr; Leem, Jinwoo; Galson, Jacob D.; Krawczyk, Konrad

doi:10.1101/2021.11.03.467080

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Specifically, collating therapeutic sequences can lead to insights regarding their therapeutic properties, as was the case with antibodies (19). Nevertheless, unlike with antibodies, of which there are more than 800 molecules with assigned INNs (20), there do not exist similar resources for TCR sequences. This is mostly due to the fact that there is only one approved therapeutic and merely a handful disclosed in development.…”

Section: Resultsmentioning

confidence: 99%

PATCRdb: Database of TCRs from data mining patent documents

Lee

Freitag

Ganesan

et al. 2023

Preprint

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T-cells are crucial actuators of the innate immune system. Because their receptors recognize intracellular disease markers, there is considerable interest in developing them as novel biotherapies. Computational methods to support discovery, design and development of TCR-based therapeutics need robust repositories of curated sequence and structural information on TCRs. The urgency of this need is highlighted by the recent approval of the first TCR biotherapeutic, tebentafusp. In this work, we have collected patent data on TCR sequences to provide early access to TCRs that are in various stages of product and clinical development (pre-FDA approvals) and are already past the initial discovery / proof of concept (scientific publications) stages. We employ literature mining to identify patent documents disclosing TCR sequences. Such documents are further analyzed to provide a birds-eye view of TCR patenting landscape. We compile the information into a database available at http://github.com/konradkrawczyk/patcrdb that we hope should help TCR engineers.

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Section: Resultsmentioning

confidence: 99%

PATCRdb: Database of TCRs from data mining patent documents

Lee

Freitag

Ganesan

et al. 2023

Preprint

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Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

Wilman¹,

Wróbel²,

Bielska³

et al. 2022

Briefings in Bioinformatics

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Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody–antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.

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AbDiver – A tool to explore the natural antibody landscape to aid therapeutic design

Cited by 2 publications

References 15 publications

PATCRdb: Database of TCRs from data mining patent documents

PATCRdb: Database of TCRs from data mining patent documents

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

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