dc.date.accessioned | 2023-02-14T16:10:28Z | |
dc.date.available | 2023-02-14T16:10:28Z | |
dc.date.created | 2022-05-22T23:03:48Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Akbar, Rahmad Robert, Philippe Paul Auguste Weber, Cédric R. Widrich, Michael Frank, Robert Pavlovic, Milena Scheffer, Lonneke Chernigovskaia, Maria Snapkow, Igor Slabodkin, Andrei Mehta, Brij Bhushan Miho, Enkelejda Lund-Johansen, Fridtjof Andersen, Jan Terje Hochreiter, Sepp Haff, Ingrid Hobæk Klambauer, Günter Sandve, Geir Kjetil Ferkingstad Greiff, Victor . In silico proof of principle of machine learning-based antibody design at unconstrained scale. mAbs. 2022, 14:e2031482(1), 1-18 | |
dc.identifier.uri | http://hdl.handle.net/10852/99926 | |
dc.description.abstract | 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. | |
dc.language | EN | |
dc.rights | Attribution-NonCommercial 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | |
dc.title | In silico proof of principle of machine learning-based antibody design at unconstrained scale | |
dc.title.alternative | ENEngelskEnglishIn silico proof of principle of machine learning-based antibody design at unconstrained scale | |
dc.type | Journal article | |
dc.creator.author | Akbar, Rahmad | |
dc.creator.author | Robert, Philippe Paul Auguste | |
dc.creator.author | Weber, Cédric R. | |
dc.creator.author | Widrich, Michael | |
dc.creator.author | Frank, Robert | |
dc.creator.author | Pavlovic, Milena | |
dc.creator.author | Scheffer, Lonneke | |
dc.creator.author | Chernigovskaia, Maria | |
dc.creator.author | Snapkow, Igor | |
dc.creator.author | Slabodkin, Andrei | |
dc.creator.author | Mehta, Brij Bhushan | |
dc.creator.author | Miho, Enkelejda | |
dc.creator.author | Lund-Johansen, Fridtjof | |
dc.creator.author | Andersen, Jan Terje | |
dc.creator.author | Hochreiter, Sepp | |
dc.creator.author | Haff, Ingrid Hobæk | |
dc.creator.author | Klambauer, Günter | |
dc.creator.author | Sandve, Geir Kjetil Ferkingstad | |
dc.creator.author | Greiff, Victor | |
cristin.unitcode | 185,53,18,12 | |
cristin.unitname | Avdeling for immunologi og transfusjonsmedisin | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 2026272 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=mAbs&rft.volume=14:e2031482&rft.spage=1&rft.date=2022 | |
dc.identifier.jtitle | mAbs | |
dc.identifier.volume | 14 | |
dc.identifier.issue | 1 | |
dc.identifier.doi | https://doi.org/10.1080/19420862.2022.2031482 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 1942-0862 | |
dc.type.version | PublishedVersion | |
cristin.articleid | 2031482 | |
dc.relation.project | SKGJ/SKGJ-MED-017 | |
dc.relation.project | KF/215817 | |
dc.relation.project | NFR/311341 | |
dc.relation.project | NFR/300740 | |
dc.relation.project | EC/H2020/825821 | |