使用深度学习技术ARTINA从原始NMR光谱快速进行蛋白质分配和结构分析
Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA
摘要：
核磁共振（NMR）光谱是结构生物学的主要技术之一，在蛋白质数据库中保存了超过11，800种蛋白质结构。NMR可以阐明溶液、活细胞和固体中的中小型蛋白质的结构和动力学，但受到繁琐的数据分析过程的限制。通常需要经过培训的专家进行数周或数月的手工工作，才能将NMR测量结果转化为蛋白质结构。这个过程的自动化是一个开放的问题，在30多年前就在该领域形成了。在这里，我们提出了一个解决方案，可以在完成测量后的几个小时内完全自动化地分析蛋白质NMR数据。仅使用NMR光谱和蛋白质序列作为输入，我们基于机器学习的方法ARTINA可以在没有任何人为干预的情况下严格提供信号位置、共振分配和结构。在包含1329个多维NMR谱的100种蛋白质基准上进行测试，ARTINA证明了其解决PDB参考结构的能力，其中位RMSD为1.44，并识别91.36%正确的NMR共振分配。ARTINA可由非专业人员使用，减少了通过NMR进行蛋白质分配或结构测定的工作，主要是样品的制备和光谱测量。
英文摘要：
Nuclear Magnetic Resonance (NMR) spectroscopy is one of the major techniques in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, living cells, and solids, but has been limited by the tedious data analysis process. It typically requires weeks or months of manual work of a trained expert to turn NMR measurements into a protein structure. Automation of this process is an open problem, formulated in the field over 30 years ago. Here, we present a solution to this challenge that enables the completely automated analysis of protein NMR data within hours after completing the measurements. Using only NMR spectra and the protein sequence as input, our machine learning-based method, ARTINA, delivers signal positions, resonance assignments, and structures strictly without any human intervention. Tested on a 100-protein benchmark comprising 1329 multidimensional NMR spectra, ARTINA demonstrated its ability to solve structures with 1.44 Å median RMSD to the PDB reference and to identify 91.36% correct NMR resonance assignments. ARTINA can be used by non-experts, reducing the effort for a protein assignment or structure determination by NMR essentially to the preparation of the sample and the spectra measurements.
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。
