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

Akbar, RahmadRobert, Philippe A.Weber, Cédric R.Widrich, MichaelFrank, RobertPavlović, MilenaScheffer, LonnekeChernigovskaya, MariaSnapkov, IgorSlabodkin, AndreiMehta, Brij BhushanMiho, EnkelejdaLund-Johansen, FridtjofAndersen, Jan TerjeHochreiter, SeppHobæk Haff, IngridKlambauer, GünterSandve, Geir KjetilGreiff, Victor2024-08-162024-08-162022-04-041942-08621942-087010.1080/19420862.2022.2031482https://irf.fhnw.ch/handle/11654/46928https://doi.org/10.26041/fhnw-9953Generative 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.enGenerative machine learningAntibody designParatopeEpitope600 - Technik, Medizin, angewandte WissenschaftenIn silico proof of principle of machine learning-based antibody design at unconstrained scale01A - Beitrag in wissenschaftlicher Zeitschrift