Greiff, Victor

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Greiff
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Victor
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Greiff, Victor

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Autor:in: Frank, Robert × Thema: Paratope ×

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    Publikation
    Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction
    (Nature, 19.12.2022) Robert, Philippe A.; Akbar, Rahmad; Frank, Robert; Pavlović, Milena; Widrich, Michael; Snapkov, Igor; Slabodkin, Andrei; Chernigovskaya, Maria; Scheffer, Lonneke; Smorodina, Eva; Rawat, Puneet; Mehta, Brij Bhushan; Vu, Mai Ha; Mathisen, Ingvild Frøberg; Prósz, Aurél; Abram, Krzysztof; Olar, Alex; Miho, Enkelejda; Haug, Dag Trygve Tryslew; Lund-Johansen, Fridtjof; Hochreiter, Sepp; Haff, Ingrid Hobæk; Klambauer, Günter; Sandve, Geir Kjetil; Greiff, Victor [in: Nature Computational Science]
    Machine learning (ML) is a key technology for accurate prediction of antibody–antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody–antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.
    01A - Beitrag in wissenschaftlicher Zeitschrift
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    Publikation
    In silico proof of principle of machine learning-based antibody design at unconstrained scale
    (Taylor & Francis, 04.04.2022) Akbar, Rahmad; Robert, Philippe A.; Weber, Cédric R.; Widrich, Michael; Frank, Robert; Pavlović, Milena; Scheffer, Lonneke; Chernigovskaya, Maria; Snapkov, Igor; Slabodkin, Andrei; Mehta, Brij Bhushan; Miho, Enkelejda; Lund-Johansen, Fridtjof; Andersen, Jan Terje; Hochreiter, Sepp; Hobæk Haff, Ingrid; Klambauer, Günter; Sandve, Geir Kjetil; Greiff, Victor [in: mAbs]
    Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.
    01A - Beitrag in wissenschaftlicher Zeitschrift