findMySequence: a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM

IUCrJ. 2021 Dec 1;9(Pt 1):86-97. doi: 10.1107/S2052252521011088. eCollection 2022 Jan 1.

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.

Keywords: SIMBAD; bioinformatics; cryo-EM; findMySequence; neural networks; protein sequences; protein structures; structure determination.

Grants and funding

This work was funded by Biotechnology and Biological Sciences Research Council grant BB/S007105/1.