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© Mart Krupovic, Junfeng Liu
Scanning electron micrograph of Saccharolobus islandicus cells (light blue) infected with the lemon-shaped virus STSV2 (yellow). Artistic rendering by Ala Krupovic.
Publication : Biophysical Journal

DeepTracer-ID: de novo protein identification from cryo-EM maps

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Biophysical Journal - 28 Jun 2022

Chang L, Wang F, Connolly K, Meng H, Su Z, Cvirkaite-Krupovic V, Krupovic M, Egelman EH, Si D

Link to Pubmed [PMID] – 35769006

Link to DOI – 10.1016/j.bpj.2022.06.025

Biophys J 2022 Aug; 121(15): 2840-2848

The recent revolution in cryo-electron microscopy (cryo-EM) has made it possible to determine macromolecular structures directly from cell extracts. However, identifying the correct protein from the cryo-EM map is still challenging and often needs additional sequence information from other techniques, such as tandem mass spectrometry and/or bioinformatics. Here, we present DeepTracer-ID, a server-based approach to identify the candidate protein in a user-provided organism de novo from a cryo-EM map, without the need for additional information. Our method first uses DeepTracer to generate a protein backbone model that best represents the cryo-EM map, and this model is then searched against the library of AlphaFold2 predictions for all proteins in the given organism. This method is highly accurate and robust for high-resolution cryo-EM maps: in all 13 experimental maps tested blindly, DeepTracer-ID identified the correct proteins as the top candidates. Eight of the maps were of known structures, while the other five unpublished maps were validated by prior protein annotation and careful inspection of the model refined into the map. The program also showed promising results for both homomeric and heteromeric protein complexes. This platform is possible because of the recent breakthroughs in large-scale three-dimensional protein structure prediction.

https://pubmed.ncbi.nlm.nih.gov/35769006