Related Experiment Video
Updated: Aug 23, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
Published on: July 8, 2025
Mimetic Neural Networks: A Unified Framework for Protein Design and Folding
Moshe Eliasof1, Tue Boesen2, Eldad Haber2
1Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
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.
Related Concept Videos
Protein Folding
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Organization
The primary structure of a protein is its amino acid sequence....
Protein-protein Interfaces
Conservation of Protein Domains Over Different Proteins
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
Molecular Chaperones and Protein Folding
The...

