Residue-level error detection in cryoelectron microscopy models

Reggiano, Gabriella; Farrell, Daniel; DiMaio, Frank; Marlovits, Thomas; Lugmayr, Wolfgang

doi:10.1016/j.str.2023.05.002

Journal Article

PUBDB-2023-07781

Residue-level error detection in cryoelectron microscopy models

Reggiano, G. ; Lugmayr, W.CSSB*DESY* ; Farrell, D. ; Marlovits, T.CSSB*DESY* ; DiMaio, F. (Corresponding author)

2023
Cell Press Cambridge, Mass.

Structure 31(7), 860-869.e4 (2023) [10.1016/j.str.2023.05.002]

This record in other databases:

Please use a persistent id in citations: doi:10.1016/j.str.2023.05.002

Abstract: Building accurate protein models into moderate resolution (3–5 Å) cryoelectron microscopy (cryo-EM) maps is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model that identifies local backbone errors in protein structures built into cryo-EM maps by combining local fit-to-density with deep-learning-derived structural information. MEDIC is validated on a set of 28 structures that were subsequently solved to higher resolutions, where we identify the differences between low- and high-resolution structures with 68% precision and 60% recall. We additionally use this model to fix over 100 errors in 12 deposited structures and to identify errors in 4 refined AlphaFold predictions with 80% precision and 60% recall. As modelers more frequently use deep learning predictions as a starting point for refinement and rebuilding, MEDIC’s ability to handle errors in structures derived from hand-building and machine learning methods makes it a powerful tool for structural biologists.

Classification: