Residue-level error detection in cryoelectron microscopy models
Standard
Residue-level error detection in cryoelectron microscopy models. / Reggiano, Gabriella; Lugmayr, Wolfgang; Farrell, Daniel; Marlovits, Thomas C; DiMaio, Frank.
in: STRUCTURE, Jahrgang 31, Nr. 7, 06.07.2023, S. 860-869.e4.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Residue-level error detection in cryoelectron microscopy models
AU - Reggiano, Gabriella
AU - Lugmayr, Wolfgang
AU - Farrell, Daniel
AU - Marlovits, Thomas C
AU - DiMaio, Frank
N1 - Copyright © 2023. Published by Elsevier Ltd.
PY - 2023/7/6
Y1 - 2023/7/6
N2 - 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.
AB - 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.
U2 - 10.1016/j.str.2023.05.002
DO - 10.1016/j.str.2023.05.002
M3 - SCORING: Journal article
C2 - 37253357
VL - 31
SP - 860-869.e4
JO - STRUCTURE
JF - STRUCTURE
SN - 0969-2126
IS - 7
ER -