Miho, Enkelejda

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Miho
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Enkelejda
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Miho, Enkelejda

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2019 - 201922020 - 20247
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Autor:in: Greiff, Victor × Anfangsdatum: 2019 ×

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Gerade angezeigt 1 - 9 von 9
  • Vorschaubild
    Publikation
    Author Correction. The dengue-specific immune response and antibody identification with machine learning
    (Nature, 20.01.2024) Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario; Babrak, Lmar Marie; Fink, Katja; Miho, Enkelejda [in: npj Vaccines]
    Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    The dengue-specific immune response and antibody identification with machine learning
    (Nature, 20.01.2024) Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario; Babrak, Lmar Marie; Fink, Katja; Miho, Enkelejda [in: npj Vaccines]
    Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction
    (Nature, 19.12.2022) 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 [in: Nature Computational Science]
    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.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction
    (Nature, 19.12.2022) Robert, Philippe A.; Akbar, Rahmad; 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, Axel; Miho, Enkelejda; Haug, Dag Trygve Tryslew; Lund-Johansen, Fridtjof; Hochreiter, Sepp; Hobæk Haff, Ingrid; Klambauer, Günter; Sandve, Geir Kjetil; Greiff, Victor [in: Nature Computational Science]
    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.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Computational deconvolution of the dengue immune response complexity with identification of novel broadly neutralizing antibodies
    (21.09.2022) Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario; Babrak, Lmar Marie; Djordjevic, Kristina; Fink, Katja; Traggiai, Elisabetta; Miho, Enkelejda
    Dengue virus poses a serious threat to global health as the causative agent of the dengue fever. Currently, there is no approved therapeutic, and broadly neutralizing antibodies recognizing all four serotypes may be an effective treatment. High-throughput immune repertoire sequencing and bioinformatic analysis enable in-depth understanding of the immune response in dengue infection. Here, we use these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences through investigation of antibody response in dengue. We observed challenging the immune system with dengue elicits the following signatures on the antibody repertoire: (i) an increase of the diversity in the CDR3 regions and the germline genes; (ii) a change in the architecture by eliciting power-law network distributions and enrichment in polar amino acids of the CDR3; (iii) an increase in the expression of transcription factors of the JNK/Fos pathways and ribosomal proteins. Moreover, our work demonstrates the applicability of computational methods and machine learning to high-throughput antibody repertoire sequencing datasets for neutralizing antibody candidate identification. Further investigation with antibody expression and functional assays is planned to validate the obtained results.
    06 - Präsentation
  • Vorschaubild
    Publikation
    In 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
  • Vorschaubild
    Publikation
    A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding
    (Cell Press, 16.03.2021) Miho, Enkelejda; Akbar, Rahmad; Pavlovic, Milena; Snapkov, Igor; Slabodkin, Andrei; Scheffer, Lonneke; Haff, Ingrid Hobaed; Tryslew Haug, Dag Trygve; Lund-Johanson, Fridtjof; Safonova, Yana; Greiff, Victor; Robert, Philippe; Jeliazkov, Jeliazko; Weber, Cedric; Sandve, Geir [in: Cell Reports]
    Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 104 motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Large-scale network analysis reveals the sequence space architecture of antibody repertoires
    (Nature, 01.12.2019) Miho, Enkelejda; Roškar, Rok; Greiff, Victor; Reddy, Sai T. [in: Nature Communications]
    The architecture of mouse and human antibody repertoires is defined by the sequence similarity networks of the clones that compose them. The major principles that define the architecture of antibody repertoires have remained largely unknown. Here, we establish a high-performance computing platform to construct large-scale networks from comprehensive human and murine antibody repertoire sequencing datasets (>100,000 unique sequences). Leveraging a network-based statistical framework, we identify three fundamental principles of antibody repertoire architecture: reproducibility, robustness and redundancy. Antibody repertoire networks are highly reproducible across individuals despite high antibody sequence dissimilarity. The architecture of antibody repertoires is robust to the removal of up to 50–90% of randomly selected clones, but fragile to the removal of public clones shared among individuals. Finally, repertoire architecture is intrinsically redundant. Our analysis provides guidelines for the large-scale network analysis of immune repertoires and may be used in the future to define disease-associated and synthetic repertoires.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Augmenting adaptive immunity. Progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires
    (Royal Society of Chemistry, 2019) Brown, Alex J.; Snapkov, Igor; Akbar, Rahmad; Pavlović, Milena; Miho, Enkelejda; Sandve, Geir K.; Greiff, Victor [in: Molecular Systems Design & Engineering]
    The adaptive immune system is a natural diagnostic sensor and therapeutic. It recognizes threats earlier than clinical symptoms manifest and neutralizes antigens with exquisite specificity. Recognition specificity and broad reactivity are enabled via adaptive B- and T-cell receptors: the immune receptor repertoire. The human immune system, however, is not omnipotent. Our natural defense system sometimes loses the battle to parasites and microbes and even turns against us in the case of cancer and (autoimmune) inflammatory disease. A long-standing dream of immunoengineers has been, therefore, to mechanistically understand how the immune system “sees”, “reacts” and “remembers” (auto)antigens. Only very recently, experimental and computational methods have achieved sufficient quantitative resolution to start querying and engineering adaptive immunity with high precision. Specifically, these innovations have been applied with the greatest fervency and success in immunotherapy, autoimmunity and vaccine design. The work here highlights advances, challenges and future directions of quantitative approaches which seek to advance the fundamental understanding of immunological phenomena, and reverse engineer the immune system to produce auspicious biopharmaceutical drugs and immunodiagnostics. Our review shows how the merger of fundamental immunology, computational immunology and (digital) biotechnology advances both immunological knowledge and immunoengineering methodologies.
    01A - Beitrag in wissenschaftlicher Zeitschrift