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Recently, it was confirmed that ACE2 is the receptor of 2019-nCoV, the pathogen causing the recent outbreak of severe pneumonia in China. It is confused that ACE2 is widely expressed across a variety of organs and is expressed moderately but not highly in lung, which, however, is the major infected organ. It remains unclear why it is the lung but not other tissues among which ACE2 highly expressed is mainly infected. We hypothesized that there could be some other genes playing key roles in the entry of 2019-nCoV into human cells. Here we found that AGTR2 (angiotensin II receptor type 2), a G-protein coupled receptor, has interaction with ACE2 and is highly expressed in lung with a high tissue specificity. More importantly, simulation of 3D structure based protein-protein interaction reveals that AGTR2 shows a higher binding affinity with the Spike protein of 2019-nCov than ACE2 (energy score: -15.7 vs. -6.9 [kcal/mol]). Given these observations, we suggest that AGTR2 could be a putative novel gene for the the entry of 2019-nCoV into human cells but need further confirmation by biological experiments. Finally, a number of compounds, biologics and traditional Chinese medicine that could decrease the expression level of AGTR2 were predicted.

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Background Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. Results In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. Conclusions We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at https://www.cuilab.cn/smorfunction.