北京大学定量生物学中心

学术报告

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

报告人: 张强峰 副教授

清华大学6165cc金沙总站检测中心

时 间: 11月8日（周一）13:00-14:00

地 点: 吕志和楼B101报告厅

主持人: 韩敬东 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

报告人简介：

张强锋博士，清华大学6165cc金沙总站检测中心副教授。2006年在中国科大获得计算机博士学位，主要从事计算复杂性和算法研究。于2012年在哥伦比亚大学获得生物物理的第二个博士学位，研究领域为计算结构生物学。随后在斯坦福大学从事基因组学博士后研究。2015年加入清华大学。实验室致力于结构生物学、基因组学、人工智能和大数据交叉领域研究。在RNA结构研究方面，开发了细胞内RNA结构高通量解析新技术，并应用于解析新冠病毒等RNA病毒基因组结构图谱，发现并验证了病毒RNA保守结构对其传播的作用。实验室还致力于开发人工智能新算法，应用于基因组结构、转录组结构及冷冻电镜结构解析等结构生物学研究。以通讯作者身份发表Cell等杂志学术文章多篇。