Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning.
Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning.
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DOI：
10.1038/s41467-022-32538-z
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发表时间：
2022-08-17
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
--
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神经网络将所有基因型与适应度景观中的等效表型连接起来，并在生物分子的突变鲁棒性和可进化性中发挥重要作用。与早期的理论研究相比，最近的适应度景观实验研究中缺乏大型神经网络的证据。这表明进化可能在全球范围内受到限制。在这里，我们证明了深度学习引导的进化算法可以有效地识别RNA连接酶核酶序列空间内的中性基因型。此外，我们测量了所有216个变体的活动，连接两个活性核酶，不同的16个突变，并分析突变的相互作用（上位性）到第16阶。我们发现了一个广泛的网络连接两种基因型的中性路径，并揭示这些路径可能只使用低阶相互作用的信息进行预测。我们对超过120，000个核酶序列的实验评估提供了重要的经验证据，表明中性网络可以增加适应度景观的可访问性和可预测性。神经网络是通过共享相同表型的单个突变连接的基因型集合，对于可进化性很重要。在这里，作者使用高通量分析和深度学习提供了RNA酶中中性网络的实验证据。
A neutral network connects all genotypes with equivalent phenotypes in a fitness landscape and plays an important role in the mutational robustness and evolvability of biomolecules. In contrast to earlier theoretical works, evidence of large neutral networks has been lacking in recent experimental studies of fitness landscapes. This suggests that evolution could be constrained globally. Here, we demonstrate that a deep learning-guided evolutionary algorithm can efficiently identify neutral genotypes within the sequence space of an RNA ligase ribozyme. Furthermore, we measure the activities of all 216 variants connecting two active ribozymes that differ by 16 mutations and analyze mutational interactions (epistasis) up to the 16th order. We discover an extensive network of neutral paths linking the two genotypes and reveal that these paths might be predicted using only information from lower-order interactions. Our experimental evaluation of over 120,000 ribozyme sequences provides important empirical evidence that neutral networks can increase the accessibility and predictability of the fitness landscape. Neutral networks, which are sets of genotypes connected via single mutations that share the same phenotype, are important for evolvability. Here, the authors provide experimental evidence of a neutral network in an RNA enzyme using a high-throughput assay and deep learning.
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影响因子：
16.6
作者：
Kobori, Shungo;Yokobayashi, Yohei
通讯作者：
Yokobayashi, Yohei
影响因子：
3.7
作者：
Payne JL;Wagner A
通讯作者：
Wagner A
DOI：
10.1073/pnas.1420902112
发表时间：
2015-10-06
影响因子：
11.1
作者：
Hayden, Eric J.;Bendixsen, Devin P.;Wagner, Andreas
通讯作者：
Wagner, Andreas
影响因子：
9.8
作者：
Bendixsen, Devin P.;Collet, James;Hayden, Eric J.
通讯作者：
Hayden, Eric J.
DOI：
10.1126/science.aae0568
发表时间：
2016-05-13
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Li C;Qian W;Maclean CJ;Zhang J
通讯作者：
Zhang J