Backbone Structure-Conditioned Sequence Design Using Machine Learning-Based Approaches

Gushchin, I. Yu.

doi:10.1134/s1068162026601497

2026-05-01

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DOI：10.1134/s1068162026601497

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摘要

En 中文

理性修饰和增强蛋白质功能对于理解天然大分子运行的基本原理以及实际应用都极为重要。基于机器学习的方法在结构生物学领域的蓬勃发展，推动了蛋白质结构预测任务的显著进展，进而促进了多功能蛋白质工程方法的发展。其中，旨在预测能够折叠成特定结构的序列的方法被证明尤为有用。本文综述了这些方法的多样性、它们在基础和实际研究中的应用，并讨论了未来需要克服的挑战。

Keyword:

protein engineering

de novo protein design

protein stability

machine learning

期刊

Russian Journal of Bioorganic Chemistry

IF:

论文数:

118

被引数:

机构

moscow institute of physics & technology

学者数:

4.5K

论文数: 3.0K

被引数: 3

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