Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA.
Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
Comput Biol Med. 2022 Dec;151(Pt A):106262. doi: 10.1016/j.compbiomed.2022.106262. Epub 2022 Nov 2.
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 evolution in virus-human protein-protein binding, which is crucial to viral evolution and transmission. This challenge is tackled with persistent Laplacian, which is able to capture both the topological change and homotopic shape evolution 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 dominant 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. Our key projection about BA4 and BA.5's dominance made on May 1, 2022 (see arXiv:2205.00532) became a reality in late June 2022.
由于其高传染性,Omicron BA.1 在 2021 年末取代了 Delta 变体成为主要变体,并于 2022 年 3 月被传播性更强的 Omicron BA.2 所取代。一个重要的问题是未来哪些新变体将占主导地位。基于拓扑的深度学习模型在过去对预测新出现的变体取得了巨大成功。然而,拓扑对于病毒-人类蛋白质-蛋白质结合中的同伦形状演化是不敏感的,这对于病毒的进化和传播至关重要。该挑战通过持久拉普拉斯(persistent Laplacian)来解决,它能够捕捉数据的拓扑变化和同伦形状演化。开发基于持久拉普拉斯的深度学习模型来系统地评估变体的感染力。我们对 Alpha、Beta、Gamma、Delta、Lambda、Mu 和 Omicron BA.1、BA.1.1、BA.2、BA.2.11、BA.2.12.1、BA.3、BA.4 和 BA.5 的比较分析表明,Omicron BA.2.11、BA.2.12.1、BA.3、BA.4 和 BA.5 比 BA.2 更具传染性。特别是,BA.4 和 BA.5 比 BA.2 传染性高出约 36%,预计将通过自然选择成为新的主要变体。此外,所提出的模型在三个主要的突变诱导蛋白质-蛋白质结合自由能变化基准数据集上优于最先进的方法。我们在 2022 年 5 月 1 日(见 arXiv:2205.00532)对 BA4 和 BA.5 主导地位的关键预测在 2022 年 6 月底成为现实。