Automatic annotation of Cryo-EM maps with the convolutional neural network Haruspex

Mostosi, Philipp; Schindelin, Hermann; Kollmannsberger, Philip; Thorn, Andrea

doi:10.1101/644476

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…There are errors that are very rare or have no great impact (not even in the downstream usage of a structure). It is only reasonable to combat errors if they have significant impact or occur often, such as a metal mis-assignment in a catalytic centre, a failure to assign the correct chirality to a glycosidic bond or the introduction of two domains on a different scale when docking into a cryo-EM reconstruction map (Croll, Diederichs et al, 2021;Mostosi et al, 2020). After the error has been identified, its source determined and its cost established, and it has been decided that it needs to be addressed, measures must be implemented to eliminate the risk of recurrence.…”

Section: Dealing With Errorsmentioning

confidence: 99%

Errors in structural biology are not the exception

Gao¹,

Thorn²,

Thorn³

2023

Acta Crystallogr D Struct Biol

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During the COVID-19 pandemic, the structural biology community swung into action quickly and efficiently, and many urgent questions were solved by macromolecular structure determination. The Coronavirus Structural Task Force evaluated all structures from SARS-CoV-1 and SARS-CoV-2, but errors in measurement, data processing and modelling are present beyond these structures and throughout the structures deposited in the Protein Data Bank. Identifying them is only the first step; in order to minimize the impact that errors have in structural biology, error culture needs to change. It should be emphasized that the atomic model which is published is an interpretation of the measurement. Furthermore, risks should be minimized by addressing issues early and by investigating the source of a given problem, so that it may be avoided in the future. If we as a community can do this, it will greatly benefit experimental structural biologists as well as downstream users who are using structural models to deduce new biological and medical answers in the future.

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Section: Dealing With Errorsmentioning

confidence: 99%

Errors in structural biology are not the exception

Gao¹,

Thorn²,

Thorn³

2023

Acta Crystallogr D Struct Biol

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“…We utilized one of the deep-learning methods, 3D-CNN [10][11][12] , which is widely used to detect or classify threedimensional objects constructed from several resources, such as a video data [12][13][14] and magnetic resonance imaging [15][16][17] . Moreover, 3D-CNN has been shown to exhibit remarkable performance on the three-dimensional cryo-EM maps to recognize several structural patterns, such as secondary structures, amino acids, and the local map resolutions [18][19][20][21] .…”

Section: Mainmentioning

confidence: 99%

Extraction of Protein Dynamics Information Hidden in Cryogenic Electron Microscopy Maps Using Deep Learning

Matsumoto

Ishida

Araki

et al. 2020

Preprint

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Technical breakthroughs in cryogenic electron microscopy (cryo-EM)-based single-particle analysis have enabled the structures of numerous proteins to be solved at atomic or near-atomic resolutions, including extremely large macromolecules whose structures could not be solved by conventional techniques. Determining the dynamics properties of these macromolecules, based on their solved structures, can further improve our understanding of their functional mechanisms. However, such analysis is often hampered by the large molecular size and complex structural assembly, making both experimental and computational approaches to determine dynamics properties challenging. Here, we report a deep learning-based approach, DEFMap, to extract the dynamics information “hidden” in cryo-EM density maps. By relying only on cryo-EM maps, DEFMap successfully provided dynamics information equivalent to that determined from molecular dynamics (MD) simulations and experimental approaches at the atomic and residue levels. Additionally, DEFMap could detect dynamics changes associated with molecular recognition and the accompanying allosteric conformational stabilizations, which trigger various biological events such as signal transduction and enzyme catalysis. This approach will provide new insights into the functional mechanisms of biological molecules, accelerating modern molecular biology researches. Furthermore, this advanced strategy combining experimental data, deep learning approaches, and MD simulations would open a new multidisciplinary science area

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“…The output has four channels that annotate the voxel data α-helical, β-strand, nucleotide or unassigned which is used for the segmentation process. Haruspex’s work which reconstructs each cryo-EM density map based on a protein’s secondary structure and DNA/RNA voxel regions 18 . To utilize the output data, the α-helical, β-strand protein and unassigned probabilities are combined to represent the amino acid density of the map.…”

Section: Introductionmentioning

confidence: 99%

Fast and Automated Protein-DNA/RNA Macromolecular Complex Modeling from Cryo-EM Maps

Nakamura

Meng

Zhao

et al. 2022

Preprint

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Cryo-electron microscopy (cryo-EM) allows a macromolecular structure such as protein-DNA/RNA complexes to be reconstructed in a three-dimensional coulomb potential map. The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fit residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https://deeptracer.uw.edu/.

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Automatic annotation of Cryo-EM maps with the convolutional neural network Haruspex

Abstract: 9

Cited by 5 publications

References 37 publications

Errors in structural biology are not the exception

Errors in structural biology are not the exception

Extraction of Protein Dynamics Information Hidden in Cryogenic Electron Microscopy Maps Using Deep Learning

Fast and Automated Protein-DNA/RNA Macromolecular Complex Modeling from Cryo-EM Maps

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