论文标题
通过应用深度学习方法，预测RNA序列的羟基介导的核降解和分子稳定性
Predicting Hydroxyl Mediated Nucleophilic Degradation and Molecular Stability of RNA Sequences through the Application of Deep Learning Methods
论文作者
论文摘要
合成和有效的实施mRNA链已被证明具有广泛的效用，尤其是最近在共同疫苗的开发中。但是，由于核糖糖中存在2'-羟基，mRNA的内在化学稳定性构成了挑战。骨干结构中的-OH基团通过去质子化羟基对磷酸二酯键的相邻磷和随之而来的自由溶解进行了基础催化的亲核攻击。正如预期的，对于在线水解裂解反应时，mRNA链的化学稳定性高度取决于外部环境因素，例如pH，温度，氧化剂等。使用计算模型预测这种化学不稳定性将减少通过识别最有希望的候选者合成和测试的序列的数量，从而有助于MRNA相关疗法的发展。本文提出并评估了三个深度学习模型（长期短期记忆，封闭式复发单元和图形卷积网络），以预测mRNA序列降解的反应性和风险。在这项研究中，使用了6034个mRNA序列的斯坦福开放疫苗数据集。训练集由这些序列的3029个（长度为107个核苷酸碱基）组成，而测试数据集则包括3005个序列（长度为130个核苷酸碱基），以结构化（最低熵基对概率矩阵）和非结构化（节点和边缘）形式组成。精确产生了mRNA链的稳定性，图形卷积网络是反应性的最佳预测指标（$ rmse = 0.249 $），而封闭式复发单位网络是预测降解风险（$ rmse = 0.266 $）的最佳方法。 GRU结合了所有目标变量，表现最好，精度为76％。结果表明，这些模型可以用于理解和预测不久的将来mRNA的化学稳定性。
Synthesis and efficient implementation mRNA strands has been shown to have wide utility, especially recently in the development of COVID vaccines. However, the intrinsic chemical stability of mRNA poses a challenge due to the presence of 2'-hydroxyl groups in ribose sugars. The -OH group in the backbone structure enables a base-catalyzed nucleophilic attack by the deprotonated hydroxyl on the adjacent phosphorous and consequent self-hydrolysis of the phosphodiester bond. As expected for in-line hydrolytic cleavage reactions, the chemical stability of mRNA strands is highly dependent on external environmental factors, e.g. pH, temperature, oxidizers, etc. Predicting this chemical instability using a computational model will reduce the number of sequences synthesized and tested through identifying the most promising candidates, aiding the development of mRNA related therapies. This paper proposes and evaluates three deep learning models (Long Short Term Memory, Gated Recurrent Unit, and Graph Convolutional Networks) as methods to predict the reactivity and risk of degradation of mRNA sequences. The Stanford Open Vaccine dataset of 6034 mRNA sequences was used in this study. The training set consisted of 3029 of these sequences (length of 107 nucleotide bases) while the testing dataset consisted of 3005 sequences (length of 130 nucleotide bases), in structured (Lowest Entropy Base Pair Probability Matrix) and unstructured (Nodes and Edges) forms. The stability of mRNA strands was accurately generated, with the Graph Convolutional Network being the best predictor of reactivity ($RMSE = 0.249$) while the Gated Recurrent Unit Network was the best at predicting risks of degradation ($RMSE = 0.266$). Combining all target variables, the GRU performed the best with 76% accuracy. Results suggest these models can be applied to understand and predict the chemical stability of mRNA in the near future.
