应用深度学习方法预测RNA序列的羟基介导的亲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
摘要：
合成和有效实施mRNA链已被证明具有广泛的用途，特别是最近在COVID疫苗的开发中。然而，由于核糖中存在2 '-羟基，mRNA的内在化学稳定性提出了挑战。骨架结构中的-OH基团能够通过去质子化的羟基对相邻的磷进行碱催化的亲核攻击，并随后使磷酸二酯键自水解。正如在线水解裂解反应所预期的那样，mRNA链的化学稳定性高度依赖于外部环境因素，例如pH值、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合成序列的数量，并通过鉴定最有希望的候选物进行测试，从而有助于mRNA相关疗法的开发。本文提出并评估了三种深度学习模型（长短期记忆、门控递归单元和图卷积网络），作为预测mRNA序列降解反应性和风险的方法。本研究中使用了6034个mRNA序列的斯坦福开放疫苗数据集。训练集由3029个这些序列（长度为107个核苷酸碱基）组成，而测试数据集由3005个序列（长度为130个核苷酸碱基）组成，以结构化（最低熵碱基对概率矩阵）和非结构化（节点和边缘）形式。准确地生成mRNA链的稳定性，其中图卷积网络是反应性的最佳预测器（$RMSE = 0.249$），而门控递归单元网络在预测降解风险（$RMSE = 0.266$）方面是最佳的。结合所有目标变量，GRU表现最好，准确率为76%。结果表明，这些模型可以应用于理解和预测mRNA的化学稳定性在不久的将来。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
