Protein matchmaking through representation learning.

Pathology State Required CME
Journal: Cell systems
Published Date: Oct 20, 2021

Abstract

Sledzieski, Singh, Cowen, and Berger employ representation learning to predict protein interactions and associations, additionally identifying binding residues between protein pairs. Generalizability is showcased by training on one organism while evaluating on others. The work exemplifies how transfer of AI-learned representations can advance knowledge in molecular biology.

Authors

  • Michael Heinzinger
    Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany. [email protected].
  • Christian Dallago
    Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.
  • Burkhard Rost

Keywords

External Resources

Popular Topics
Recent Journals