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

被引：7

作者：

Eliasof, Moshe ^{[1
]}

Boesen, Tue ^{[2
]}

Haber, Eldad ^{[2
]}

Keasar, Chen ^{[1
]}

Treister, Eran ^{[1
]}

机构：

[1] Ben Gurion Univ Negev, Dept Comp Sci, Beer Sheva, Israel

[2] Univ British Columbia, Dept EOAS, Vancouver, BC, Canada

来源：

FRONTIERS IN BIOINFORMATICS | 2022年 / 2卷

关键词：

graph neural networks; protein design; protein folding; deep learning; protein sructure prediction; STRUCTURE PREDICTION; ACCURATE PREDICTION; SEQUENCE; ENHANCEMENT; POTENTIALS; CONTACTS; MODEL;

D O I：

10.3389/fbinf.2022.715006

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：13

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