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arXiv 2107.12243q-bio.BMcs.LGcs.NE
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使用深度学习预测蛋白质-RNA相互作用:结构很重要

Protein-RNA interaction prediction with deep learning: Structure matters

The Chinese University of Hong Kong (CUHK)(香港中文大学);King Abdullah University of Science and Technology (KAUST)(阿卜杜拉国王科技大学);The CUHK Shenzhen Research Institute(香港中文大学深圳研究院)

Junkang Wei, Siyuan Chen, Licheng Zong, Xin Gao, Yu Li

摘要:

蛋白质-RNA相互作用对多种细胞活动至关重要。实验和计算技术已经被开发来研究相互作用。由于以往数据库的局限性,特别是缺乏蛋白质结构数据,现有的计算方法大多严重依赖于序列数据,只有一小部分方法利用了结构信息。最近,AlphaFold彻底改变了整个蛋白质和生物学领域。可以预见,蛋白质-RNA相互作用的预测也将在未来几年内得到显着提升。在这项工作中,我们给出了一个彻底的审查这一领域,调查的结合位点和结合偏好预测问题,并涵盖了常用的数据集,功能和模型。我们还指出了这一领域的潜在挑战和机遇。本综述总结了RBP-RNA相互作用领域过去的发展,并展望了其在后AlphaFold时代的未来发展。

英文摘要:

Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Due to the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features, and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RBP-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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