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

Rahmad AkbarPhilippe A RobertCédric R WeberMichael WidrichRobert FrankMilena PavlovićLonneke SchefferMaria ChernigovskayaIgor SnapkovAndrei SlabodkinBrij Bhushan MehtaEnkelejda MihoFridtjof Lund-JohansenJan Terje AndersenSepp HochreiterIngrid Hobæk HaffGuenter KlambauerGeir Kjetil F SandveVictor Greiff
Published in: mAbs (2022)
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.
Keyphrases
  • machine learning
  • high throughput
  • big data
  • electronic health record
  • artificial intelligence
  • virtual reality
  • mass spectrometry
  • high resolution
  • body composition
  • single molecule
  • amino acid
  • data analysis