深度学习为蛋白质设计开启了新时代。

Hamed Khakzad, Ilia Igashov, Arne Schneuing, Casper Goverde, Michael Bronstein, Bruno Correia
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引用次数: 0

摘要

深度学习领域的快速发展对蛋白质设计产生了重大影响。深度学习方法最近在蛋白质结构预测方面取得了突破,导致数百万蛋白质的高质量模型的可用性。随着生成建模和序列分析的新架构,它们在过去几年中通过提高识别新蛋白质序列和结构的准确性和能力,显著地改变了蛋白质设计领域。深度神经网络现在可以学习和提取蛋白质结构的基本特征,预测它们如何与其他生物分子相互作用,并有可能创造新的有效药物来治疗疾病。由于它们在蛋白质设计中的适用性正在迅速增长,我们回顾了深度学习方法的最新发展和技术,并提供了它们在生成新型功能蛋白质方面的性能示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new age in protein design empowered by deep learning.

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.

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