Mimetic Neural Networks: A Unified Framework for Protein Design and Folding

Moshe Eliasof, Tue Boesen, Eldad Haber, Chen Keasar, Eran Treister

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Recent advancements in machine learning techniques for protein structure prediction motivate better results in its inverse problem–protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein backbone design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be met and even improved, given recent architectures for protein folding.

Original languageEnglish
Article number715006
JournalFrontiers in Bioinformatics
Volume2
DOIs
StatePublished - 1 Jan 2022

Keywords

  • deep learning
  • graph neural networks
  • protein design
  • protein folding
  • protein sructure prediction

ASJC Scopus subject areas

  • Computational Mathematics
  • Structural Biology
  • Biochemistry
  • Biotechnology
  • Statistics and Probability

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