Antibody complementarity determining region design using high-capacity machine learning.

Journal: Bioinformatics (Oxford, England)
Published Date: Apr 1, 2020

Abstract

MOTIVATION: The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties.

Authors

  • Ge Liu
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
  • Haoyang Zeng
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
  • Jonas Mueller
    MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.
  • Brandon Carter
    MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.
  • Ziheng Wang
  • Jonas Schilz
    Novartis Institutes for BioMedical Research, Basel, Switzerland.
  • Geraldine Horny
    Novartis Institutes for BioMedical Research, Basel, Switzerland.
  • Michael E Birnbaum
    Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Stefan Ewert
    Novartis Institutes for BioMedical Research, Basel, Switzerland.
  • David K Gifford
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.

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