利用人工智能预测RNA的结构与功能。

Predicting RNA structures and functions by artificial intelligence.

作者信息

Zhang Jun, Lang Mei, Zhou Yaoqi, Zhang Yang

机构信息

National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong, 518106, China.

出版信息

Trends Genet. 2023 Oct 26. doi: 10.1016/j.tig.2023.10.001.

DOI:10.1016/j.tig.2023.10.001

PMID:39492264

Abstract

RNA functions by interacting with its intended targets structurally. However, due to the dynamic nature of RNA molecules, RNA structures are difficult to determine experimentally or predict computationally. Artificial intelligence (AI) has revolutionized many biomedical fields and has been progressively utilized to deduce RNA structures, target binding, and associated functionality. Integrating structural and target binding information could also help improve the robustness of AI-based RNA function prediction and RNA design. Given the rapid development of deep learning (DL) algorithms, AI will provide an unprecedented opportunity to elucidate the sequence-structure-function relation of RNAs.

摘要

RNA通过与预期靶点进行结构上的相互作用来发挥功能。然而，由于RNA分子的动态特性，RNA结构很难通过实验确定或通过计算预测。人工智能（AI）已经给许多生物医学领域带来了变革，并逐渐被用于推断RNA结构、靶点结合及相关功能。整合结构和靶点结合信息也有助于提高基于人工智能的RNA功能预测和RNA设计的稳健性。鉴于深度学习（DL）算法的快速发展，人工智能将为阐明RNA的序列-结构-功能关系提供前所未有的机会。