Computational Design of Epitope-Specific Functional Antibodies

Nimrod, Guy; Fischman, Sharon; Austin, Mark; Herman, Asael; Keyes, Feenagh; Leiderman, Olga; Hargreaves, David; Štrajbl, Marek; Breed, J.; Klompus, Shelley; Minton, Kevin; Spooner, Jennifer; Buchanan, Andrew; Vaughan, Tristan J.; Ofran, Yishai

doi:10.1016/j.celrep.2018.10.081

Cited by 66 publications

(73 citation statements)

References 52 publications

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“…Recent reports have, however, provided preliminary evidence for the potential predictability of antibody-antigen interaction: (i) The antibody repertoire field has now established that antibody sequence diversity underlies predictable rules (Elhanati et al, 2015;Greiff et al, 2017bGreiff et al, , 2017a. (ii) The presence of transferable "specificity units" between distinct antibody molecules was recently suggested by showing that tightly binding functional antibodies may be conceived by designing and improving seemingly unrelated paratopes (Nimrod et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Akbar

Jeliazkov

Robert

et al. 2019

Preprint

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Antibody recognition of antigen relies on the specific interaction of amino acids at the paratopeepitope interface. A long-standing question in the fields of immunology and structural biology is whether paratope-epitope interaction is predictable. A fundamental premise for the predictability of paratope-epitope binding is the existence of structural units that are universally shared among antibody-antigen binding complexes. Here, we identified structural interaction motifs, which together compose a vocabulary of paratope-epitope binding that is shared among investigated antibody-antigen complexes. The vocabulary (i) is finite with less than 10 4 motifs, (ii) mediates specific and non-redundant interactions between paratope-epitope pairs, (iii) is immunity-specific (distinct from the motif vocabulary used by non-immune protein-protein interactions), and (iv) enables the machine learning prediction of paratope or epitope. The discovery of a vocabulary of paratope-epitope interaction demonstrates the learnability and predictability of paratope-epitope interaction.

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Section: Introductionmentioning

confidence: 99%

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Akbar

Jeliazkov

Robert

et al. 2019

Preprint

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“…In biotherapeutics, DL algorithms are, for example, being trained on patient data to predict neoantigens in an effort to better inform immunotherapeutic development [35]. Moreover, computational design and prediction of antibody epitopes is also helping design novel antibodies [36], illustrating how biotherapeutic design is also benefiting from Big Data and AI.…”

Section: Drug Designmentioning

confidence: 99%

Transforming cancer drug discovery with Big Data and AI

Workman

Antolín

Al‐Lazikani

2019

Expert Opinion on Drug Discovery

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“…To date, as the ability to describe the antibody through binding affinity to the antigen is supplemented by information on antibody structure and amino acid sequences, several computational tools have been developed for the design of antibodies [17,[24][25][26][27]. For example, Rosetta Antibody is a novel antibody FV (the variable fragment critical for Ag binding) region structure prediction server [24].…”

Section: Introductionmentioning

confidence: 99%

Extracting the Interfacial Electrostatic Features from Experimentally Determined Antigen and/or Antibody-Related Structures inside Protein Data Bank for Machine Learning-Based Antibody Design

Li¹

2020

Preprint

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The importance of antibodies in health care and the biotechnology research and development demands not only knowledge of their experimental structures at high resolution, but also practical implementation of this knowledge for both effective and efficient design and production of antibody for its use in both medical and research applications. While the experimental wet-lab approach is usually costly, laborious and time-consuming, computational (dry-lab) approaches, in spite of their intrinsic limitations in comparison with its experimental (wet-lab) counterpart, provide a cheaper and faster alternative option. For the first time, this article reports a comprehensive set of structural electrostatic features extracted from experimentally determined antigen-antibody-related structures, including especially those structural electrostatic features at the interfaces of all experimentally determined antigen-antibody complex structures as of February 29, 2020, to facilitate effective and efficient machine learning-based computational antibody design using currently available experimental structures inside Protein Data Bank.

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Computational Design of Epitope-Specific Functional Antibodies

Abstract: Highlights d ''Re-epitoping'' of an existing antibody by in silico design d Engineering a functional antibody to IL-17A d Crystal structure of the antibody-antigen complex confirms the targeted epitope

Cited by 66 publications

References 52 publications

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Transforming cancer drug discovery with Big Data and AI

Extracting the Interfacial Electrostatic Features from Experimentally Determined Antigen and/or Antibody-Related Structures inside Protein Data Bank for Machine Learning-Based Antibody Design

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