Discovery Projects - Grant ID: DP210101875

基于深度学习和进化约束的RNA结构预测。该项目解决了核糖核酸（RNA）长期存在的结构折叠问题，其解决方案对于阐明非编码RNA在生物体中的作用至关重要。该方法将通过开发新的搜索算法和用于深度学习的智能神经网络来检测隐藏的同源序列并增强进化共变信号。该项目希望为基于结构的RNA进化和功能机制的探测产生新的工具。该研究结果将为RNA结构的高精度计算建模提供显著的好处，目前的结构生物学技术很难解决这些问题，而且成本高昂，但对生物技术和临床应用至关重要。

RNA structure prediction by deep learning and evolution-derived restraints. This project addresses the long-standing structure-folding problem of Ribonucleic acids (RNA) whose solution is essential for elucidating the roles of noncoding RNAs in living organisms. The proposed approach will detect hidden homologous sequences and enhance evolutionary covariation signals by developing new algorithms for search and smarter neural networks for deep learning. The project expects to generate new tools for structure-based probing of RNA evolutional and functional mechanisms. The outcomes should provide significant benefits by high-accuracy computational modelling of RNA structures that are difficult and costly to solve by current structural biology techniques but important for enabling biotech and clinical applications.