Predicting RNA Mutation Effects through Machine Learning of High-Throughput Ribozyme Experiments (Student Abstract)
Predicting RNA Mutation Effects through Machine Learning of High-Throughput Ribozyme Experiments (Student Abstract)
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DOI：
10.1609/aaai.v36i11.21629
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Joey Kitzhaber;Ashlyn Trapp;James D. Beck;Edoardo Serra;Francesca Spezzano;Eric J. Hayden;J. Roberts
中科院分区：
文献类型：
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作者：
Joey Kitzhaber;Ashlyn Trapp;James D. Beck;Edoardo Serra;Francesca Spezzano;Eric J. Hayden;J. Roberts
研究“功能获得”突变的能力对于识别和减轻与病毒感染相关的公共卫生和国家安全风险具有重要意义。许多令人关注的呼吸道病毒具有RNA基因组（例如，SARS和流感）。这些RNA基因组折叠成复杂的结构，执行病毒的几个关键功能。然而，我们预测RNA结构突变的功能后果的能力继续限制我们预测由改变的或新的RNA结构引起的功能突变的获得的能力。这一领域的生物学研究也受到直接用病毒进行实验工作的相当大的风险的限制。在这里，我们使用小的功能性RNA分子（核酶）作为RNA结构和功能的模型系统。我们使用组合DNA合成来产生所有可能的个体和成对突变，并使用高通量测序来评估每个单突变和双突变序列的功能后果。我们使用这些数据来训练机器学习模型（长短期记忆）。该模型还用于预测在哺乳动物基因组中发现的具有三个突变的序列的功能，这些突变不在我们的训练集中。我们在所有实验中发现了很强的预测相关性。
The ability to study "gain of function" mutations has important implications for identifying and mitigating risks to public health and national security associated with viral infections. Numerous respiratory viruses of concern have RNA genomes (e.g., SARS and flu). These RNA genomes fold into complex structures that perform several critical functions for viruses. However, our ability to predict the functional consequence of mutations in RNA structures continues to limit our ability to predict gain of function mutations caused by altered or novel RNA structures. Biological research in this area is also limited by the considerable risk of direct experimental work with viruses. Here we used small functional RNA molecules (ribozymes) as a model system of RNA structure and function. We used combinatorial DNA synthesis to generate all of the possible individual and pairs of mutations and used high-throughput sequencing to evaluate the functional consequence of each single- and double-mutant sequence. We used this data to train a machine learning model (Long Short-Term Memory). This model was also used to predict the function of sequences found in the genomes of mammals with three mutations, which were not in our training set. We found a strong prediction correlation in all of our experiments.