Immune Literacy: Reading, Writing, and Editing Adaptive Immunity

Csepregi, Lucia; Ehling, Roy; Wagner, Bastian; Reddy, Sai T.

doi:10.1016/j.isci.2020.101519

Cited by 21 publications

(13 citation statements)

References 242 publications

(288 reference statements)

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“…Implications for machine-learning-driven antibody, epitope, and vaccine engineering Monoclonal antibodies are of substantial importance in the treatment of cancer and autoimmunity (Brown et al, 2019;Csepregi et al, 2020;Ecker et al, 2015). Thus, their efficient discovery is of particular interest.…”

Section: Predictability and Learnability Of The Paratope-epitope Interfacementioning

confidence: 99%

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

et al. 2021

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Section: Predictability and Learnability Of The Paratope-epitope Interfacementioning

confidence: 99%

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

et al. 2021

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“…The prediction of the recognized 3D epitopes (conformational epitopes) by an antibody is crucial for long-standing problems in computational antibody design (8)(9)(10)(11)(12)(13) and vaccine engineering (14). 3D-antibody-antigen complexes resolved at the atomic level, for instance by crystallization, represent the gold standard for describing antibody binding but are time and cost-intensive to generate.…”

Section: Introductionmentioning

confidence: 99%

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for real-world antibody specificity prediction

Akbar

Frank

et al. 2021

Preprint

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Machine learning (ML) is a key technology to enable accurate prediction of antibody-antigen binding, a prerequisite for in silico vaccine and antibody design. Two orthogonal problems hinder the current application of ML to antibody-specificity prediction and the benchmarking thereof: (i) The lack of a unified formalized mapping of immunological antibody specificity prediction problems into ML notation and (ii) the unavailability of large-scale training datasets. Here, we developed the Absolut! software suite that allows the parameter-based unconstrained generation of synthetic lattice-based 3D-antibody-antigen binding structures with ground-truth access to conformational paratope, epitope, and affinity. We show that Absolut!-generated datasets recapitulate critical biological sequence and structural features that render antibody-antigen binding prediction challenging. To demonstrate the immediate, high-throughput, and large-scale applicability of Absolut!, we have created an online database of 1 billion antibody-antigen structures, the extension of which is only constrained by moderate computational resources. We translated immunological antibody specificity prediction problems into ML tasks and used our database to investigate paratope-epitope binding prediction accuracy as a function of structural information encoding, dataset size, and ML method, which is unfeasible with existing experimental data. Furthermore, we found that in silico investigated conditions, predicted to increase antibody specificity prediction accuracy, align with and extend conclusions drawn from experimental antibody-antigen structural data. In summary, the Absolut! framework enables the development and benchmarking of ML strategies for biotherapeutics discovery and design.

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“…More recently, immune organoids from human tonsils and other lymphoid tissues have been developed with a potential for the discovery of antigen-specific antibodies, mimicking key germinal center features including somatic hypermutation and affinity maturation. 29 We expect that artificial intelligence (AI)-and machine learning (ML)-based methods [30][31][32] could essentially exploit the best of both worlds of in vivo-and in vitro-generated methods, large-scale naïve and antigen-specific antibody sequence and structure data, [33][34][35] knowledge of immune repertoire and literacy, [36][37][38] help design feature-controlled antibody libraries and developable antibodies, 39,40 which, in turn, would ultimately solve scientific and ethical problems in antibody generation.…”

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confidence: 99%

Animal immunization merges with innovative technologies: A new paradigm shift in antibody discovery

Prabakaran

Rao

Wendt

2021

mAbs

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Animal-derived antibody sources, particularly, transgenic mice that are engineered with human immunoglobulin loci, along with advanced antibody generation technology platforms have facilitated the discoveries of human antibody therapeutics. For example, isolation of antigen-specific B cells, microfluidics, and next-generation sequencing have emerged as powerful tools for identifying and developing monoclonal antibodies (mAbs). These technologies enable not only antibody drug discovery but also lead to the understanding of B cell biology, immune mechanisms and immunogenetics of antibodies. In this perspective article, we discuss the scientific merits of animal immunization combined with advanced methods for antibody generation as compared to animal-free alternatives through in-vitro-generated antibody libraries. The knowledge gained from animal-derived antibodies concerning the recombinational diversity, somatic hypermutation patterns, and physiochemical properties is found more valuable and prerequisite for developing in vitro libraries, as well as artificial intelligence/machine learning methods to discover safe and effective mAbs.

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Immune Literacy: Reading, Writing, and Editing Adaptive Immunity

Cited by 21 publications

References 242 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

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for real-world antibody specificity prediction

Animal immunization merges with innovative technologies: A new paradigm shift in antibody discovery

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