北京大學定量生物學中心

學術報告

題 目: RNA structural systems biology powered by big data and machine intelligence

報告人: 張強峰 副教授

清華大學beat365

時 間: 11月8日（周一）13:00-14:00

地 點: 呂志和樓B101報告廳

主持人: 韓敬東 教授

摘 要:

Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP–RNA interactions employ RNA sequence and/or predicted RNA structures, and therefore do not capture their condition-dependent nature. Here, after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types, we developed PrismNet, a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular conditions.

We further applied PrismNet for the prediction of host proteins that bind and regulate the viral RNA (vRNA) of SARS-CoV-2, which has infected more than 180 million people with more than 4 million deaths, causing tremendous damage to the global human society. As a single-stranded RNA virus, SARS-CoV-2 vRNA is a key component in regulating host infection, which relies heavily on interactions with proteins in host cells. With our recently resolved SARS-CoV-2 RNA genome structure in infected human cells, PrismNet predicted binding of many host proteins on SARS-CoV-2 vRNA. We found that FDA-approved drugs inhibiting the SARS-CoV-2 vRNA binding proteins dramatically reduced SARS-CoV-2 infection in cells. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.

REFERENCES

1. Lei Sun*, Kui Xu*, Wenze Huang*, Yucheng Yang*, Lei Tang, Tuanlin Xiong, Qiangfeng Cliff Zhang#. (2021) Predicting dynamic cellular protein-RNA interactions using deep learning and in vivo RNA structure, Cell Research 31(5):495-516.

2. Lei Sun*, Pan Li*, Xiaohui Ju*, Jian Rao*, Wenze Huang*, Lili Ren, Shaojun Zhang, Tuanlin Xiong, Kui Xu, Xiaolin Zhou, Mingli Gong, Eric Miska, Qiang Ding#, Jianwei Wang#, Qiangfeng Cliff Zhang#. (2021) In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs, Cell 184(7):1865-1883. e20

報告人簡介：

張強鋒博士，清華大學beat365副教授。2006年在中國科大獲得計算機博士學位，主要從事計算複雜性和算法研究。于2012年在哥倫比亞大學獲得生物物理的第二個博士學位，研究領域為計算結構生物學。随後在斯坦福大學從事基因組學博士後研究。2015年加入清華大學。實驗室緻力于結構生物學、基因組學、人工智能和大數據交叉領域研究。在RNA結構研究方面，開發了細胞内RNA結構高通量解析新技術，并應用于解析新冠病毒等RNA病毒基因組結構圖譜，發現并驗證了病毒RNA保守結構對其傳播的作用。實驗室還緻力于開發人工智能新算法，應用于基因組結構、轉錄組結構及冷凍電鏡結構解析等結構生物學研究。以通訊作者身份發表Cell等雜志學術文章多篇。