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 language | English |
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Article number | 715006 |
Journal | Frontiers in Bioinformatics |
Volume | 2 |
DOIs | |
State | Published - 1 Jan 2022 |
Keywords
- deep learning
- graph neural networks
- protein design
- protein folding
- protein sructure prediction
ASJC Scopus subject areas
- Biochemistry
- Biotechnology
- Computational Mathematics
- Statistics and Probability
- Structural Biology