Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning.

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance exposure on the viral infection remains unclear. It is well-known that, during viral infection, organism receptors play a significant role in mediating the entry of viruses to enter host cells. A major receptor of SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2). This study proposes a deep learning model based on the graph convolutional network (GCN) that enables, for the first time, the prediction of exogenous substances that affect the transcriptional expression of the ACE2 gene. It outperforms other machine learning models, achieving an area under receiver operating characteristic curve (AUROC) of 0.712 and 0.703 on the validation and internal test set, respectively. In addition, quantitative polymerase chain reaction (qPCR) experiments provided additional supporting evidence for indoor air pollutants identified by the GCN model. More broadly, the proposed methodology can be applied to predict the effect of environmental chemicals on the gene transcription of other virus receptors as well. In contrast to typical deep learning models that are of black box nature, we further highlight the interpretability of the proposed GCN model and how it facilitates deeper understanding of gene change at the structural level.

Front Cover: Computational Insights into the Allosteric Effect and Dynamic Structural Features of the SARS‐COV‐2 Spike Protein (Chem. Eur. J. 6/2022)

Although the prefusion conformation of the spike protein of SARS‐COV‐2 can be stabilized alone, a spontaneously and energy‐friendly centripetal movement of the receptor binding domain occurs when spike protein binds to ACE2. During the binding process, several residues, especially Phe329 and Phe515, play a significant role in the allosteric effect. As a result, two potential cleavage sites S1/S2 and S2′ are exposed on the surface. More information can be found in the Research Article by A. Zhang, J. Fu et al. (DOI: 10.1002/chem.202104215).

Computational Insights into the Allosteric Effect and Dynamic Structural Features of the SARS-COV-2 Spike Protein.

Invited for the cover of this issue are Aiqian Zhang, Jianjie Fu, Guibin Jiang, and co-workers at the Chinese Academy of Sciences. The image depicts the molecular recognition of human angiotensin-converting enzyme 2 by the SARS-COV-2 spike protein. Read the full text of the article at 10.1002/chem.202104215.