Hong Wei

Abstract RNA 3D structure prediction is a long-standing challenge. Inspired by the recent breakthrough in protein structure prediction, we developed trRosettaRNA, an automated deep learning-based approach to RNA 3D structure prediction. The trRosettaRNA pipeline comprises two major steps: 1D and 2D geometries prediction by a transformer network; and 3D structure folding by energy minimization. Benchmark tests suggest that trRosettaRNA outperforms traditional automated methods. In the blind tests of the 15 th Critical Assessment of Structure Prediction (CASP15) and the RNA-Puzzles experiments, the automated trRosettaRNA predictions for the natural RNAs are competitive with the top human predictions. trRosettaRNA also outperforms other deep learning-based methods in CASP15 when measured by the Z-score of the Root-Mean-Square Deviation. Nevertheless, it remains challenging to predict accurate structures for synthetic RNAs with an automated approach. We hope this work could be a good start toward solving the hard problem of RNA structure prediction with deep learning.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein is essential for viral replication, making it a promising target for antiviral drug and vaccine development. SARS-CoV-2 infected patients exhibit an uncoordinated immune response; however, the underlying mechanistic details of this imbalance remain obscure. Here, starting from a functional proteomics workflow, we cataloged the protein-protein interactions of SARS-CoV-2 proteins, including an evolutionarily conserved specific interaction of N with the stress granule resident proteins G3BP1 and G3BP2. N localizes to stress granules and sequesters G3BPs away from their typical interaction partners, thus attenuating stress granule formation. We found that N binds directly to host mRNAs in cells, with a preference for 3' UTRs, and modulates target mRNA stability. We show that the N protein rewires the G3BP1 mRNA-binding profile and suppresses the physiological stress response of host cells, which may explain the imbalanced immune response observed in SARS-CoV-2 infected patients.

The outbreak of the infection of 2019 novel coronavirus disease (COVID--19) has become a challenging public health threat worldwide. Limited data are available for pregnant women with COVID-19 pneumonia. We report a case of a convalescing pregnant woman diagnosed with COVID-19 infection 37 days before delivery in the third trimester. A live birth without severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was performed successfully via the vagina. The findings from our case indicate that there is no intrauterine transmission in this woman who developed COVID-19 pneumonia in late pregnancy.

Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMSearch (Protein Language Model), a homologous protein search method with only sequences as input. PLMSearch uses deep representations from a pre-trained protein language model and trains the similarity prediction model with a large number of real structure similarity. This enables PLMSearch to capture the remote homology information concealed behind the sequences. Extensive experimental results show that PLMSearch can search millions of query-target protein pairs in seconds like MMseqs2 while increasing the sensitivity by more than threefold, and is comparable to state-of-the-art structure search methods. In particular, unlike traditional sequence search methods, PLMSearch can recall most remote homology pairs with dissimilar sequences but similar structures. PLMSearch is freely available at https://dmiip.sjtu.edu.cn/PLMSearch .