Auflistung nach Autor:in "Weber, Cédric R."
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- PublikationIn 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
- PublikationLearning the high-dimensional immunogenomic features that predict public and private antibody repertoires(American Association of Immunologists, 15.10.2017) Greiff, Victor; Weber, Cédric R.; Palme, Johannes; Bodenhofer, Ulrich; Miho, Enkelejda; Menzel, Ulrike; Reddy, Sai T. [in: Journal of Immunology]Recent studies have revealed that immune repertoires contain a substantial fraction of public clones, which may be defined as Ab or TCR clonal sequences shared across individuals. It has remained unclear whether public clones possess predictable sequence features that differentiate them from private clones, which are believed to be generated largely stochastically. This knowledge gap represents a lack of insight into the shaping of immune repertoire diversity. Leveraging a machine learning approach capable of capturing the high-dimensional compositional information of each clonal sequence (defined by CDR3), we detected predictive public clone and private clone–specific immunogenomic differences concentrated in CDR3’s N1–D–N2 region, which allowed the prediction of public and private status with 80% accuracy in humans and mice. Our results unexpectedly demonstrate that public, as well as private, clones possess predictable high-dimensional immunogenomic features. Our support vector machine model could be trained effectively on large published datasets (3 million clonal sequences) and was sufficiently robust for public clone prediction across individuals and studies prepared with different library preparation and high-throughput sequencing protocols. In summary, we have uncovered the existence of high-dimensional immunogenomic rules that shape immune repertoire diversity in a predictable fashion. Our approach may pave the way for the construction of a comprehensive atlas of public mouse and human immune repertoires with potential applications in rational vaccine design and immunotherapeutics.01A - Beitrag in wissenschaftlicher Zeitschrift