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| 001 | 600207 | ||
| 005 | 20250715173207.0 | ||
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| 100 | 1 | _ | |a Reggiano, Gabriella |0 0000-0003-2311-2155 |b 0 |
| 245 | _ | _ | |a Residue-level error detection in cryoelectron microscopy models |
| 260 | _ | _ | |a Cambridge, Mass. |c 2023 |b Cell Press |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Lugmayr, Wolfgang |0 P:(DE-H253)PIP1021411 |b 1 |
| 700 | 1 | _ | |a Farrell, Daniel |b 2 |
| 700 | 1 | _ | |a Marlovits, Thomas |0 P:(DE-H253)PIP1021412 |b 3 |
| 700 | 1 | _ | |a DiMaio, Frank |0 0000-0002-7524-8938 |b 4 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.str.2023.05.002 |g Vol. 31, no. 7, p. 860 - 869.e4 |0 PERI:(DE-600)2031189-8 |n 7 |p 860-869.e4 |t Structure |v 31 |y 2023 |x 0969-2126 |
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