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使用深度学习探索"暗物质"蛋白质折叠

Zander Harteveld¹, Alexandra Van Hall-Beauvais¹, Irina Morozova²

¹École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.

|October 9, 2024

概括

我们开发了Genesis,一个卷积变异自编码器,用于设计新型蛋白质. 这种人工智能模型有效地创造了稳定的,与原生蛋白质类似的蛋白质结构,在蛋白质工程中开辟了新的前沿.

科学领域:

计算生物学是一种计算生物学.
蛋白质工程是一种蛋白质工程.
科学中的人工智能.

背景情况:

新型蛋白质设计旨在创造超越进化采样的新型蛋白质.
一个关键的挑战是为序列生成开发"可设计"的结构模板.
现有的方法往往难以探索各种蛋白质拓.

研究的目的:

介绍创世纪,一个卷积变异自编码器,用于学习蛋白质结构模式.
为了证明创世纪在设计针对目标结构的新型蛋白质序列方面的能力.
为了应对脊柱可设计性挑战,在 de novo 蛋白质设计中.

主要方法:

开发了创世纪,一个卷积变异自编码器,以学习蛋白质结构模式.
将Genesis与trRosetta结合起来,用于针对特定蛋白质折叠的序列设计.
使用高通量蛋白酶抗性试验来评估蛋白质的稳定性.

主要成果:

创世纪成功地重建了5个本地和3个新型蛋白质折叠的本地式距离和角度分布.
人工智能模型在多样化和以前未被探索的蛋白质拓学中展示了概括性.
设计的蛋白质表现出令人鼓舞的稳定性,在折叠中成功率高.

结论:

创世纪使蛋白质折叠空间的快速探索成为可能,大大加速了新型蛋白质的设计.
像创世纪这样的小型神经网络可以有效地学习蛋白质中的复杂结构模式.
这种方法提升了为各种应用设计功能性新型蛋白质的能力.

关键词:

计算型蛋白质设计暗物质的折叠是暗物质的折叠数据驱动的蛋白质设计这是一个de novo设计.深度学习用于蛋白质设计.高通量选的高通量选机器学习用于分子生成.

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