Greiff, Victor

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Greiff, Victor

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Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction

2022-12-19, 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

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.

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In silico proof of principle of machine learning-based antibody design at unconstrained scale

2022-04-04, 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

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.