findMySequence: a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM.
findMySequence: a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM.
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DOI：
10.1107/s2052252521011088
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发表时间：
2022-01-01
期刊：
影响因子：
3.9
通讯作者：
Rigden DJ
中科院分区：
文献类型：
--
作者：
Chojnowski G;Simpkin AJ;Leonardo DA;Seifert-Davila W;Vivas-Ruiz DE;Keegan RM;Rigden DJ
findMySequence 是一种机器学习方法，用于识别未知蛋白质以及冷冻电镜和 X 射线晶体学中的序列分配验证。尽管实验蛋白质结构测定通常针对已知蛋白质，但经常会遇到未知序列的链。它们可以从天然来源中纯化，表现为已明确表征的蛋白质的意外片段或表现为污染物。无论问题的根源是什么，未知的蛋白质总是需要表征。这里提出了一个自动化管道，用于从冷冻电镜重建和晶体学数据中识别蛋白质序列。介绍了该方法应用于表征从蛇毒中纯化的未知蛋白质的晶体结构。研究还表明，该方法可以成功应用于冷冻电镜蛋白质结构中蛋白质序列的鉴定和序列分配的验证。
findMySequence is presented – a machine-learning method for the identification of unknown proteins and sequence-assignment validation in cryo-EM and X-ray crystallography. Although experimental protein-structure determination usually targets known proteins, chains of unknown sequence are often encountered. They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or appear as a contaminant. Regardless of the source of the problem, the unknown protein always requires characterization. Here, an automated pipeline is presented for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. The method’s application to characterize the crystal structure of an unknown protein purified from a snake venom is presented. It is also shown that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures.