findMySequence : a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM
Year: 2021
Type: article
Abstract: 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.
Source: IUCrJ
Authors: Grzegorz Chojnowski, Adam J. Simpkin, Diego A. Leonardo, Wolfram Seifert-Davila, Dan E. Vivas-Ruiz, Ronan M. Keegan, Daniel J. Rigden
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,200
Awards: CCP4 Grant Renewal 2014-2019: Question-driven crystallographic data collection and advanced structure solution, G3678527048, G3714012316, G6457563902, CCP4 Advanced integrated approaches to macromolecular structure determination, CCP4 Advanced integrated approaches to macromolecular structure determination
OpenAlex ID: W4200419843