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Updated: May 9, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

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语义和几何蛋白质编码,以提高生物活性和热稳定性

Yang Tan1,2,3,4, Bingxin Zhou1,3,5,6, Lirong Zheng5

  • 1Shanghai-Chongqing Institute of Artificial Intelligence, Shanghai Jiao Tong University, Chongqing, China.

eLife
|May 2, 2025
PubMed
概括

这项研究引入了蛋白质工程的新深度学习框架,整合了序列和几何数据,以准确预测变异效应. 该方法通过模拟自然选择来增强蛋白质的功能和稳定性来增强蛋白质的设计.

关键词:
计算生物学是计算生物学.深度学习是一种深度学习.突变效应的预测和预测没有,没有,没有.蛋白质语言模型系统生物学 系统生物学

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