Exploring "dark-matter" protein folds using deep learning.

Cell systems Pub Date : 2024-10-16 Epub Date: 2024-10-08 DOI:10.1016/j.cels.2024.09.006
Zander Harteveld, Alexandra Van Hall-Beauvais, Irina Morozova, Joshua Southern, Casper Goverde, Sandrine Georgeon, Stéphane Rosset, Michëal Defferrard, Andreas Loukas, Pierre Vandergheynst, Michael M Bronstein, Bruno E Correia
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Abstract

De novo protein design explores uncharted sequence and structure space to generate novel proteins not sampled by evolution. A main challenge in de novo design involves crafting "designable" structural templates to guide the sequence searches toward adopting target structures. We present a convolutional variational autoencoder that learns patterns of protein structure, dubbed Genesis. We coupled Genesis with trRosetta to design sequences for a set of protein folds and found that Genesis is capable of reconstructing native-like distance and angle distributions for five native folds and three novel, the so-called "dark-matter" folds as a demonstration of generalizability. We used a high-throughput assay to characterize the stability of the designs through protease resistance, obtaining encouraging success rates for folded proteins. Genesis enables exploration of the protein fold space within minutes, unrestricted by protein topologies. Our approach addresses the backbone designability problem, showing that small neural networks can efficiently learn structural patterns in proteins. A record of this paper's transparent peer review process is included in the supplemental information.

利用深度学习探索 "暗物质 "蛋白质折叠。
从头蛋白质设计探索未知的序列和结构空间,以生成进化过程中未采样的新型蛋白质。从头设计的一个主要挑战是制作 "可设计 "的结构模板,引导序列搜索采用目标结构。我们提出了一种学习蛋白质结构模式的卷积变异自动编码器,称为 Genesis。我们将 Genesis 与 trRosetta 相结合,为一组蛋白质褶皱设计序列,发现 Genesis 能够为五种原生褶皱和三种新型褶皱(即所谓的 "暗物质 "褶皱)重建类似原生的距离和角度分布,从而证明了它的普适性。我们使用了一种高通量检测方法,通过蛋白酶抗性来鉴定设计的稳定性,获得了令人鼓舞的折叠蛋白成功率。Genesis 能够在几分钟内探索蛋白质折叠空间,不受蛋白质拓扑结构的限制。我们的方法解决了骨架可设计性问题,表明小型神经网络可以高效地学习蛋白质的结构模式。本文的同行评审过程透明,记录见补充信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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