Automated model building and protein identification in cryo-EM maps

Deep Residual Learning for Image Recognition

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Attention Is All You Need

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Highly accurate protein structure prediction with AlphaFold

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Feature Pyramid Networks for Object Detection

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Features and development of Coot

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Mfold web server for nucleic acid folding and hybridization prediction

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ColabFold - Making protein folding accessible to all

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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

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Accelerated Profile HMM Searches

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Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix

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Evolutionary-scale prediction of atomic level protein structure with a language model

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ViennaRNA Package 2.0

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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

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Real-space refinement in PHENIX for cryo-EM and crystallography

Afonine, P., Poon, B., Read, R. J., Sobolev, O., Terwilliger, T., Urzhumtsev, A., & Adams, P. (2018) · bioRxiv · doi:10.1107/s2059798318006551 · Cited by 3,057 · see citing works

Vienna RNA secondary structure server

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HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

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Hidden Markov Models in Computational Biology

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ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps

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Quantifying the local resolution of cryo-EM density maps

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Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions

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RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

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DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

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New tools for automated cryo-EM single-particle analysis in RELION-4.0

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UNAFold

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Real-time cryo–EM data pre-processing with Warp

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Single-particle cryo-EM at atomic resolution

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Atomic-resolution protein structure determination by cryo-EM

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Detection of gene pathways with predictive power for breast cancer prognosis

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ColabFold - Making protein folding accessible to allPreprint

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Measurement of atom resolvability in cryo-EM maps with Q-scores

Pintilie, G. D., Zhang, K., Su, Z., Li, S., Schmid, M. F., & Chiu, W. (2020) · Nature Methods · doi:10.1038/s41592-020-0731-1 · Cited by 404 · see citing works

Real-space refinement in PHENIX for cryo-EM and crystallography

Afonine, P. V., Poon, B. K., Read, R. J., Sobolev, O. V., Terwilliger, T. C., Urzhumtsev, A., & Adams, P. D. (2018) · eScholarship (California Digital Library) · Cited by 374 · see citing works

Cryo-EM single-particle structure refinement and map calculation using Servalcat

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Symbolic Discovery of Optimization Algorithms

Chen, X., Liang, C., Huang, D., Real, E., Wang, K., Liu, Y., Pham, H., Dong, X., Luong, T., Hsieh, C.-J., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.06675 · Cited by 303 · see citing works

EMDataBank unified data resource for 3DEM

Lawson, C. L., Patwardhan, A., Baker, M. L., Hryc, C. F., Sanz‐García, E., Hudson, B. P., Lagerstedt, I. C., Ludtke, S. J., Pintilie, G. D., Sala, R., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1126 · Cited by 288 · see citing works

Improvement of cryo-EM maps by simultaneous local and non-local deep learning

He, J., Li, T., & Huang, S. (2023) · Nature Communications · doi:10.1038/s41467-023-39031-1 · Cited by 217 · see citing works

Focus: The interface between data collection and data processing in cryo-EM.

Biyani, N., Righetto, R., McLeod, R., Caujolle-Bert, D., Castaño-Díez, D., Goldie, K., & Stahlberg, H. (2017) · Journal of Structural Biology · doi:10.1016/j.jsb.2017.03.007 · Cited by 203 · see citing works

DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes

Pfab, J., Phan, N. M., & Si, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2017525118 · Cited by 196 · see citing works

Age-dependent formation of TMEM106B amyloid filaments in human brains

Schweighauser, M., Arseni, D., Bacioglu, M., Huang, M., Lövestam, S., Shi, Y., Yang, Y., Zhang, W., Kotecha, A., Garringer, H. J., et al. (2022) · Nature · doi:10.1038/s41586-022-04650-z · Cited by 194 · see citing works

Amyloid fibrils in FTLD-TDP are composed of TMEM106B and not TDP-43

Jiang, Y. X., Cao, Q., Sawaya, M. R., Abskharon, R. N. N., Ge, P., Deture, M. A., Dickson, D. W., Fu, J. Y., Loo, R. R. O., Loo, J. A., et al. (2022) · Nature · doi:10.1038/s41586-022-04670-9 · Cited by 161 · see citing works

Homotypic fibrillization of TMEM106B across diverse neurodegenerative diseases

Chang, A., Xiang, X., Wang, J., Lee, C., Arakhamia, T., Simjanoska, M., Wang, C., Carlomagno, Y., Zhang, G., Dhingra, S., et al. (2022) · Cell · doi:10.1016/j.cell.2022.02.026 · Cited by 150 · see citing works

In situ structure of the red algal phycobilisome–PSII–PSI–LHC megacomplex

You, X., Zhang, X., Cheng, J., Xiao, Y., Ma, J., Sun, S., Zhang, X., Wang, H., & Sui, S. (2023) · Nature · doi:10.1038/s41586-023-05831-0 · Cited by 147 · see citing works

De novo main-chain modeling for EM maps using MAINMAST

Terashi, G., & Kihara, D. (2018) · Nature Communications · doi:10.1038/s41467-018-04053-7 · Cited by 137 · see citing works

Structures of radial spokes and associated complexes important for ciliary motility

Gui, M., Ma, M., Sze-Tu, E., Wang, X., Koh, F., Zhong, E. D., Berger, B., Davis, J. H., Dutcher, S. K., Zhang, R., et al. (2020) · Nature Structural & Molecular Biology · doi:10.1038/s41594-020-00530-0 · Cited by 135 · see citing works

The Chlamydomonas Genome Project, version 6: Reference assemblies for mating-type plus and minus strains reveal extensive structural mutation in the laboratory

Craig, R. J., Gallaher, S. D., Shu, S., Salomé, P. A., Jenkins, J. W., Blaby, C., Purvine, S. O., O’Donnell, S., Barry, K., Grimwood, J., et al. (2022) · The Plant Cell · doi:10.1093/plcell/koac347 · Cited by 110 · see citing works

Ciliary central apparatus structure reveals mechanisms of microtubule patterning

Gui, M., Wang, X., Dutcher, S. K., Brown, A., & Zhang, R. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00770-2 · Cited by 97 · see citing works

Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly

He, J., Lin, P., Chen, J., Cao, H., & Huang, S. (2022) · Nature Communications · doi:10.1038/s41467-022-31748-9 · Cited by 93 · see citing works

Structures of the holo CRISPR RNA-guided transposon integration complex

Park, J.-U., Tsai, A. W.-L., Rizo, A. N., Truong, V. H., Wellner, T. X., Schargel, R. D., & Kellogg, E. H. (2023) · Nature · doi:10.1038/s41586-022-05573-5 · Cited by 80 · see citing works

Progressive assembly of multi-domain protein structures from cryo-EM density maps

Zhou, X., Li, Y., Zhang, C., Zheng, W., Zhang, G., & Zhang, Y. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00232-1 · Cited by 75 · see citing works

CR-I-TASSER: assemble protein structures from cryo-EM density maps using deep convolutional neural networks

Zhang, X., Zhang, B., Freddolino, L., & Zhang, Y. (2022) · Nature Methods · doi:10.1038/s41592-021-01389-9 · Cited by 73 · see citing works

TranSPHIRE: automated and feedback-optimized on-the-fly processing for cryo-EM

Stabrin, M., Schönfeld, F., Wagner, T., Pospich, S., Gatsogiannis, C., & Raunser, S. (2020) · Nature Communications · doi:10.1038/s41467-020-19513-2 · Cited by 72 · see citing works

Structural basis for context-specific inhibition of translation by oxazolidinone antibiotics

Tsai, K., Stojković, V., Lee, D. J., Young, I. D., Szal, T., Klepacki, D., Vázquez‐Laslop, N., Mankin, A. S., Fraser, J. S., & Fujimori, D. G. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00723-9 · Cited by 70 · see citing works

Putting <i>AlphaFold</i> models to work with <i>phenix.process_predicted_model</i> and <i>ISOLDE</i>

Oeffner, R. D., Croll, T. I., Millán, C., Poon, B. K., Schlicksup, C. J., Read, R. J., & Terwilliger, T. C. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798322010026 · Cited by 63 · see citing works

findMySequence : a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM

Chojnowski, G., Simpkin, A. J., Leonardo, D. A., Seifert-Dávila, W., Vivas-Ruiz, D. E., Keegan, R. M., & Rigden, D. J. (2021) · IUCrJ · doi:10.1107/s2052252521011088 · Cited by 62 · see citing works

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

Chang, L., Wang, F., Connolly, K., Meng, H., Su, Z., Cvirkaite-Krupovic, V., Krupovic, M., Egelman, E. H., & Si, D. (2022) · Biophysical Journal · doi:10.1016/j.bpj.2022.06.025 · Cited by 50 · see citing works

CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning

Wang, X., Terashi, G., & Kihara, D. (2023) · Nature Methods · doi:10.1038/s41592-023-02032-5 · Cited by 48 · see citing works

Measurement of Atom Resolvability in CryoEM Maps with Q-scores

Pintilie, G. D., Zhang, K., Su, Z., Li, S., Schmid, M. F., & Chiu, W. (2020) · Microscopy and Microanalysis · doi:10.1017/s1431927620021170 · Cited by 42 · see citing works

Cryomicroscopy in situ : what is the smallest molecule that can be directly identified without labels in a cell?

Russo, C. J., Dickerson, J. L., & Naydenova, K. (2022) · Faraday Discussions · doi:10.1039/d2fd00076h · Cited by 35 · see citing works

Using Scipion for stream image processing at Cryo-EM facilities

Gómez-Blanco, J., de la Rosa-Trevin, J. M., Marabini, R., del Caño, L., Jiménez, A., Martínez, M., Melero, R., Majtner, T., Maluenda, D., Mota, J., et al. (2018) · Journal of Structural Biology · doi:10.1016/j.jsb.2018.10.001 · Cited by 33 · see citing works

Making the invisible enemy visible

Croll, T. I., Diederichs, K., Fischer, F., Fyfe, C. D., Gao, Y., Horrell, S., Joseph, A. P., Kandler, L., Kippes, O., Kirsten, F., et al. (2021) · Nature Structural & Molecular Biology · doi:10.1038/s41594-021-00593-7 · Cited by 32 · see citing works

Current approaches for automated model building into cryo-EM maps using Buccaneer with CCP-EM

Hoh, S. W., Burnley, B. T., & Cowtan, K. D. (2020) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798320005513 · Cited by 31 · see citing works

Expansion of phycobilisome linker gene families in mesophilic red algae

Lee, J.-M., Kim, D., Bhattacharya, D., & Yoon, H. (2019) · Nature Communications · doi:10.1038/s41467-019-12779-1 · Cited by 30 · see citing works

Live Analysis and Reconstruction of Single-Particle Cryo-Electron Microscopy Data with CryoFLARE

Schenk, A. D., Cavadini, S., Thomä, N. H., & Genoud, C. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01102 · Cited by 30 · see citing works

Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps

Nakamura, A., Meng, H., Zhao, M., Wang, F., Hou, J., Cao, R., & Si, D. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac632 · Cited by 27 · see citing works

Errors in structural biology are not the exception

Gao, Y., Thorn, V., & Thorn, A. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798322011901 · Cited by 18 · see citing works

SPREAD: A Fully Automated Toolkit for Single-Particle Cryogenic Electron Microscopy Data 3D Reconstruction with Image-Network-Aided Orientation Assignment

Xie, R., Chen, Y., Cai, J., Yang, Y., & Shen, H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01099 · Cited by 16 · see citing works

Protein identification from electron cryomicroscopy maps by automated model building and side-chain matching

Terwilliger, T. C., Sobolev, O. V., Afonine, P. V., Adams, P. D., Ho, C.-M., Li, X., & Zhou, Z. H. (2021) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798321001765 · Cited by 11 · see citing works

Progressive assembly of multi-domain protein structures from cryo-EM density maps

Xiaogen, Z., Li, Y., Zhang, C., Zheng, W., Zhang, G., & Zhang, Y. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.6348467 · Cited by 5 · see citing works

An image processing pipeline for electron cryo‐tomography in RELION ‐5

Burt, A., Toader, B., Warshamanage, R., von Kügelgen, A., Pyle, E., Zivanov, J., Kimanius, D., Bharat, T. A. M., & Scheres, S. H. W. (2024) · FEBS Open Bio · doi:10.1002/2211-5463.13873 · Cited by 254 · see citing works

Targeted protein degradation via intramolecular bivalent glues

Hsia, O., Hinterndorfer, M., Cowan, A. D., Iso, K., Ishida, T., Sundaramoorthy, R., Nakasone, M. A., Imrichova, H., Schätz, C., Rukavina, A., et al. (2024) · Nature · doi:10.1038/s41586-024-07089-6 · Cited by 222 · see citing works

Disease-specific tau filaments assemble via polymorphic intermediates

Lövestam, S., Li, D., Wagstaff, J. L., Kotecha, A., Kimanius, D., McLaughlin, S. H., Murzin, A. G., Freund, S. M. V., Goedert, M., & Scheres, S. H. W. (2023) · Nature · doi:10.1038/s41586-023-06788-w · Cited by 202 · see citing works

EMDB—the Electron Microscopy Data Bank

Turner, J., Abbott, S., Fonseca, N., Pye, R., de Oliveira, L. C., Duraisamy, A. K., Salih, O., Wang, Z., Kleywegt, G. J., Morris, K. L., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1019 · Cited by 154 · see citing works

Structural specializations of the sperm tail

Leung, M. R., Zeng, J., Wang, X., Roelofs, M. C., Huang, W., Chiozzi, R. Z., Hevler, J. F., Heck, A. J. R., Dutcher, S. K., Brown, A., et al. (2023) · Cell · doi:10.1016/j.cell.2023.05.026 · Cited by 130 · see citing works

Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information

Song, X., Bao, L., Feng, C., Huang, Q., Zhang, F., Gao, X., & Han, R. (2024) · Nature Communications · doi:10.1038/s41467-024-49858-x · Cited by 100 · see citing works

Heteromeric amyloid filaments of ANXA11 and TDP-43 in FTLD-TDP type C

Arseni, D., Nonaka, T., Jacobsen, M. H., Murzin, A. G., Cracco, L., Peak‐Chew, S., Garringer, H. J., Kawakami, I., Suzuki, H., Onaya, M., et al. (2024) · Nature · doi:10.1038/s41586-024-08024-5 · Cited by 97 · see citing works

In-cell architecture of the mitochondrial respiratory chain

Waltz, F., Righetto, R. D., Lamm, L., Salinas-Giegé, T., Kelley, R., Zhang, X., Obr, M., Khavnekar, S., Kotecha, A., & Engel, B. D. (2025) · Science · doi:10.1126/science.ads8738 · Cited by 95 · see citing works

Structure of the endosomal Commander complex linked to Ritscher-Schinzel syndrome

Healy, M. D., McNally, K. E., Butkovič, R., Chilton, M., Kato, K., Sacharz, J., McConville, C., Moody, E. R. R., Shaw, S., Planelles-Herrero, V. J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.04.003 · Cited by 90 · see citing works

Structural diversity of axonemes across mammalian motile cilia

Leung, M. R., Sun, C., Zeng, J., Anderson, J. R., Niu, Q., Huang, W., Noteborn, W. E. M., Brown, A., Zeev‐Ben‐Mordehai, T., & Zhang, R. (2025) · Nature · doi:10.1038/s41586-024-08337-5 · Cited by 80 · see citing works

Structure of the R2 non-LTR retrotransposon initiating target-primed reverse transcription

Wilkinson, M. E., Frangieh, C. J., Macrae, R. K., & Zhang, F. (2023) · Science · doi:10.1126/science.adg7883 · Cited by 79 · see citing works

Scalable protein design using optimization in a relaxed sequence space

Frank, C., Khoshouei, A., Fuβ, L., Schiwietz, D., Putz, D., Weber, L., Zhao, Z., Hattori, M., Feng, S., de Stigter, Y., et al. (2024) · Science · doi:10.1126/science.adq1741 · Cited by 79 · see citing works

Structural basis of Integrator-dependent RNA polymerase II termination

Fianu, I., Ochmann, M., Walshe, J. L., Dybkov, O., Cruz, J. N., Urlaub, H., & Cramer, P. (2024) · Nature · doi:10.1038/s41586-024-07269-4 · Cited by 73 · see citing works

A disease resistance protein triggers oligomerization of its NLR helper into a hexameric resistosome to mediate innate immunity

Madhuprakash, J., Toghani, A., Contreras, M. P., Posbeyikian, A., Richardson, J., Kourelis, J., Bozkurt, T. O., Webster, M., & Kamoun, S. (2024) · Science Advances · doi:10.1126/sciadv.adr2594 · Cited by 67 · see citing works

On the pH-dependence of α-synuclein amyloid polymorphism and the role of secondary nucleation in seed-based amyloid propagation

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Cryo-EM structure of Alzheimer’s disease tau filaments with PET ligand MK-6240

Kunach, P., Vaquer‐Alicea, J., Smith, M. S., Monistrol, J., Hopewell, R., Moquin, L., Therriault, J., Tissot, C., Rahmouni, N., Massarweh, G., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52265-x · Cited by 60 · see citing works

A structural rationale for reversible vs irreversible amyloid fibril formation from a single protein

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Structures of AT8 and PHF1 phosphomimetic tau: Insights into the posttranslational modification code of tau aggregation

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