RNA structure prediction by deep learning and evolution-derived restraints

该项目致力于解决核糖核酸（RNA）的长期结构折叠问题，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。所提出的方法将通过开发新的搜索算法和更智能的深度学习神经网络来检测隐藏的同源序列并增强进化协变信号。 该项目预计将产生新的工具，用于基于结构的RNA进化和功能机制的探测。这些结果应该通过RNA结构的高精度计算建模提供显着的好处，这些RNA结构通过当前的结构生物学技术难以解决且成本高昂，但对于实现生物技术和临床应用非常重要。

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.