论文标题
持续的拉普拉斯（Laplacian
Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants
论文作者
论文摘要
由于其高传递性，Omicron Ba.1驱逐了Delta变体在2021年底成为主要的变体，并被2022年3月2日更可传播的Omicron BA.2取代。基于拓扑的深度学习模型过去在预测新兴变体方面取得了巨大的成功。然而，拓扑对病毒蛋白质蛋白质结合的同型形状变化不敏感，这对于病毒进化和传播至关重要。这项挑战是通过持续的拉普拉斯人解决的，该挑战能够捕获数据的拓扑结构和形状。持续的基于拉普拉斯的深度学习模型是系统地评估变异感染力的。我们对Alpha，Beta，Gamma，Delta，Lambda，Mu和Omicron BA.1，BA.1.1，BA.2，BA.2，BA.2.11，BA.2.12.1，BA.2.1，BA.3，BA.4，BA.4，BA.4和BA。比BA.2传染。特别是，BA.4和BA.5比BA.2高约36 \％，预计通过自然选择将成为新的主导变体。此外，提出的模型的表现优于三个主要基准数据集的最新方法，用于突变诱导的蛋白质 - 蛋白质结合能变化。
Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to become a dominating variant in late 2021 and was replaced by more transmissible Omicron BA.2 in March 2022. An important question is which new variants will dominate in the future. Topology-based deep learning models have had tremendous success in forecasting emerging variants in the past. However, topology is insensitive to homotopic shape variations in virus-human protein-protein binding, which are crucial to viral evolution and transmission. This challenge is tackled with persistent Laplacian, which is able to capture both the topology and shape of data. Persistent Laplacian-based deep learning models are developed to systematically evaluate variant infectivity. Our comparative analysis of Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron BA.1, BA.1.1, BA.2, BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 unveils that Omicron BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 are more contagious than BA.2. In particular, BA.4 and BA.5 are about 36\% more infectious than BA.2 and are projected to become new dominating variants by natural selection. Moreover, the proposed models outperform the state-of-the-art methods on three major benchmark datasets for mutation-induced protein-protein binding free energy changes.
