Software

We provide access to a range of open-source software. Below is a list of the scientific software packages we offer, along with their versions, license information, link to source code, and academic references.

• CryoCloud declares that no changes have been made to the software packages we provide.
• CryoCloud does not license nor distribute the binaries of the open-source software listed below.
• CryoCloud claims no ownership over the software and provides attribution to the developers and maintaining bodies of the software.

RELION​

• Versions: 3.1.4, 4.0.1, 5.0-beta
• License: GNU General Public License v2.0
• Source code: github.com/3dem/relion
• Citations: Sjors Scheres' group publications
  https://doi.org/10.1016/j.jmb.2011.11.010
  Scheres SH. A Bayesian view on cryo-EM structure determination. J Mol Biol. 2012 Jan 13;415(2):406-18. Epub 2011 Nov 12.

PyTOM GPU TM​

• Version: 0.2
• License: GNU General Public License v2.0
• Source code: github.com/SBC-Utrecht/pytom-template-matching-gpu
• Citation: https://doi.org/10.3390/ijms241713375
  Chaillet ML, van der Schot G, Gubins I, Roet S, Veltkamp RC, Förster F. Extensive Angular Sampling Enables the Sensitive Localization of Macromolecules in Electron Tomograms. International Journal of Molecular Sciences. 2023. 24(17):13375.

ModelAngelo​

• Version: 1.0.8
• License: MIT License
• Source code: github.com/3dem/model-angelo
• Citation: https://doi.org/10.1101/2023.05.16.541002
  Jamali K, Käll L, Zhang R, Brown A, Kimanius D, Scheres SHW. Automated model building and protein identification in cryo-EM maps. bioRxiv 2023.05.16.541002.

AlphaFold​

• Version: 2.3.2
• License: Apache License 2.0
• Source code: github.com/google-deepmind/alphafold
• Citations:
  https://doi.org/10.1038/s41586-021-03819-2
  Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
  https://doi.org/10.1101/2021.10.04.463034
  Evans R, O'Neill M, Pritzel A, et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv 2021.10.04.463034.

ColabFold​

• Version: 1.5.5
• License: MIT License
• Source code: https://github.com/sokrypton/ColabFold
• Citations:
  https://doi.org/10.1038/s41592-022-01488-1Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M. ColabFold: Making protein folding accessible to all.https://doi.org/10.1038/s41586-021-03819-2
  Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
  https://doi.org/10.1101/2021.10.04.463034
  Evans R, O'Neill M, Pritzel A, et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv 2021.10.04.463034.

Topaz​

• Version: 0.2.5
• License: GNU General Public License v3.0
• Source code: github.com/tbepler/topaz
• Citations:
  https://doi.org/10.1038/s41592-019-0575-8
  Bepler T, Morin A, Rapp M, Brasch J, Shapiro L, Noble AJ, Berger B. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat Methods 16, 1153–1160 (2019).
  https://doi.org/10.1038/s41467-020-18952-1
  Bepler T, Kelley K, Noble AJ, Berger B. Topaz-Denoise: general deep denoising models for cryoEM and cryoET. Nat Commun 11, 5208 (2020).

CTFFIND 4​

• Version: 4.1.14
• License: Janelia Open-Source Software License
• Source code: grigoriefflab.umassmed.edu/ctf_estimation_ctffind_ctftilt
• Citations: https://doi.org/10.1016/j.jsb.2015.08.008
  Rohou A, Grigorieff N. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J Struct Biol. 192: 216-221 (2015)

cryoDRGN​

• Version: 2.3
• License: GNU General Public License v3.0
• Source code: github.com/ml-struct-bio/cryodrgn
• Citations: https://doi.org/10.1038/s41592-020-01049-4
  Zhong, E.D., Bepler, T., Berger, B. et al. CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks. Nat Methods 18, 176–185 (2021)

DeepEMhancer​

• Version: 0.16
• License: Apache-2.0 License
• Source code: https://github.com/rsanchezgarc/deepEMhancer
• Citations: https://doi.org/10.1038/s42003-021-02399-1
  Sanchez-Garcia, R., Gomez-Blanco, J., Cuervo, A. et al. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun Biol 4, 874 (2021).

Servalcat​

• Version: 0.4.60
• License: Mozilla Public License Version 2.0
• Source code: https://github.com/keitaroyam/servalcat
• MonomerLibrary License: GNU Lesser General Public License v3.0
• MonomerLibrary Source code: https://github.com/MonomerLibrary/monomers
• Citations:
  https://doi.org/10.1107/S2059798323002413
  Yamashita, K., Wojdyr, M., Long, F., Nicholls, R. A., Murshudov, G. N. (2023) "GEMMI and Servalcat restrain REFMAC5" Acta Cryst. D79, 368-373
  https://doi.org/10.1107/S2059798321009475
  Yamashita, K., Palmer, C. M., Burnley, T., Murshudov, G. N. (2021) "Cryo-EM single particle structure refinement and map calculation using Servalcat" Acta Cryst. D77, 1282-1291