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

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

doi:10.3389/fbinf.2022.715006

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

Front Bioinform. 2022 May 5:2:715006. doi: 10.3389/fbinf.2022.715006. eCollection 2022.

Authors

Moshe Eliasof¹, Tue Boesen², Eldad Haber², Chen Keasar¹, Eran Treister¹

Affiliations

¹ Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
² Department of EOAS, The University of British Columbia, Vancouver, BC, Canada.

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.

Keywords: deep learning; graph neural networks; protein design; protein folding; protein sructure prediction.