References

References

Every work cited across the series, numbered globally. In-text citations in the chapters link here by number.

1.
Wankowicz, S. A., Fraser, J. S. Comprehensive encoding of conformational and compositional protein structural ensembles through the mmCIF data structure. IUCrJ (2024). doi: 10.1107/S2052252524005098
2.
Rosenberg, A. A., Marx, A., Bronstein, A. M. A dataset of alternately located segments in protein crystal structures. Scientific Data (2024). doi: 10.1038/s41597-024-03595-4
3.
Kuzmanic, A., Pannu, N. S., Zagrovic, B. X-ray refinement significantly underestimates the level of microscopic heterogeneity in biomolecular crystals. Nature Communications (2014). doi: 10.1038/ncomms4220
4.
Wankowicz, S. A., Oliveira, S. H. de, Hogan, D. W., Bedem, H. van den, Fraser, J. S. Ligand binding remodels protein side-chain conformational heterogeneity. eLife (2022). doi: 10.7554/eLife.74114
5.
Ploscariu, N., Burnley, T., Gros, P., Pearce, N. M. Improving sampling of crystallographic disorder in ensemble refinement. Acta Crystallographica Section D: Structural Biology 77:1357–1364 (2021). doi: 10.1107/S2059798321010044
6.
Pearce, N. M., Gros, P. A method for intuitively extracting macromolecular dynamics from structural disorder. Nature Communications 12:5493 (2021). doi: 10.1038/s41467-021-25814-x
7.
Lane, T. J. Protein structure prediction has reached the single-structure frontier. Nature Methods (2023). doi: 10.1038/s41592-022-01760-4
8.
Raghu, R., Levy, A., Wetzstein, G., Zhong, E. D. Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data. arXiv (2025). doi: 10.48550/arXiv.2506.04490
9.
Levy, A., Chan, E. R., Fridovich-Keil, S., Poitevin, F., Zhong, E. D., Wetzstein, G. Solving inverse problems in protein space using diffusion-based priors. arXiv (2025). doi: 10.48550/arXiv.2406.04239
10.
Chung, H., Kim, J., McCann, M. T., Klasky, M. L., Ye, J. C. Diffusion posterior sampling for general noisy inverse problems. arXiv (2024). doi: 10.48550/arXiv.2209.14687
11.
Flowers, J., Echols, N., Correy, G. J., Jaishankar, P., Togo, T., Renslo, A. R., Bedem, H. van den, Fraser, J. S., Wankowicz, S. A. Expanding automated multiconformer ligand modeling to macrocycles and fragments. eLife (2025). doi: 10.7554/eLife.103797
12.
Wankowicz, S. A., Fraser, J. S. Advances in uncovering the mechanisms of macromolecular conformational entropy. Nature Chemical Biology (2025). doi: 10.1038/s41589-025-01879-3
13.
Bozovic, O., Zanobini, C., Gulzar, A., Jankovic, B., Buhrke, D., Post, M., Wolf, S., Stock, G., Hamm, P. Real-time observation of ligand-induced allosteric transitions in a PDZ domain. Proceedings of the National Academy of Sciences (2020). doi: 10.1073/pnas.2012999117
14.
Winn, M. D., Isupov, M. N., Murshudov, G. N. Use of TLS parameters to model anisotropic displacements in macromolecular refinement. Acta Crystallographica Section D: Biological Crystallography 57:122–133 (2001). doi: 10.1107/S0907444900014736
15.
Painter, J., Merritt, E. A. Optimal description of a protein structure in terms of multiple groups undergoing TLS motion. Acta Crystallographica Section D: Biological Crystallography 62:439–450 (2006). doi: 10.1107/S0907444906005270
16.
Urzhumtsev, A., Afonine, P. V., Adams, P. D. TLS from fundamentals to practice. Crystallography Reviews 19:230–270 (2013). doi: 10.1080/0889311X.2013.835806
17.
Wankowicz, S. A., Bonomi, M. From possibility to precision in macromolecular ensemble prediction. Nature Methods (2026). doi: 10.1038/s41592-026-03084-z
18.
Wankowicz, S. A., Ravikumar, A., Sharma, S., Riley, B. T., Raju, A., Hogan, D. W., Bedem, H. van den, Keedy, D. A., Fraser, J. S. Uncovering protein ensembles: Automated multiconformer model building for X-ray crystallography and cryo-EM. eLife (2024). doi: 10.7554/eLife.90606.3
19.
Riley, B. T., Wankowicz, S. A., Oliveira, S. H. P. de, Zundert, G. C. P. van, Hogan, D. W., Fraser, J. S., Keedy, D. A., Bedem, H. van den. qFit 3: Protein and ligand multiconformer modeling for X-ray crystallographic and single-particle cryo-EM density maps. Protein Science 30:270–285 (2021). doi: 10.1002/pro.4001
20.
Singhal, R., Horvitz, Z., Teehan, R., Ren, M., Yu, Z., McKeown, K., Ranganath, R. A general framework for inference-time scaling and steering of diffusion models. arXiv (2025). doi: 10.48550/arXiv.2501.06848
21.
Ingraham, J. B., Baranov, M., Costello, Z., Barber, K. W., Wang, W., Ismail, A., Frappier, V., Lord, D. M., Ng-Thow-Hing, C., Van Vlack, E. R., others. Illuminating protein space with a programmable generative model. Nature 623:1070–1078 (2023). doi: 10.1038/s41586-023-06728-8
22.
Venkatakrishnan, S. V., Bouman, C. A., Wohlberg, B. Plug-and-play priors for model based reconstruction. 2013 IEEE Global Conference on Signal and Information Processing:945–948 (2013). doi: 10.1109/GlobalSIP.2013.6737048
23.
Bedem, H. van den, Fraser, J. S. Integrative, dynamic structural biology at atomic resolution—it’s about time. Nature Methods 12:307–318 (2015). doi: 10.1038/nmeth.3324
24.
Hilser, V. J., Garcı́a-Moreno E., B., Oas, T. G., Kapp, G., Whitten, S. T. A statistical thermodynamic model of the protein ensemble. Chemical Reviews 106:1545–1558 (2006). doi: 10.1021/cr040423+
25.
Wojdyr, M. GEMMI: A library for structural biology. Journal of Open Source Software 7:4200 (2022). doi: 10.21105/joss.04200
26.
Emsley, P., Lohkamp, B., Scott, W. G., Cowtan, K. Features and development of Coot. Acta Crystallographica Section D 66:486–501 (2010). doi: 10.1107/S0907444910007493
27.
Williams, C. J., Headd, J. J., Moriarty, N. W., Prisant, M. G., Videau, L. L., Deis, L. N., Verma, V., Keedy, D. A., Hintze, B. J., Chen, V. B., Jain, S., Lewis, S. M., Arendall, W. B., Snoeyink, J., Adams, P. D., Lovell, S. C., Richardson, J. S., Richardson, D. C. MolProbity: More and better reference data for improved all-atom structure validation. Protein Science 27:293–315 (2018). doi: 10.1002/pro.3330
28.
Liebschner, D., Afonine, P. V., Baker, M. L., Bunkóczi, G., Chen, V. B., Croll, T. I., others. Macromolecular structure determination using X-rays, neutrons and electrons: Recent developments in Phenix. Acta Crystallographica Section D 75:861–877 (2019). doi: 10.1107/S2059798319011471
29.
Joosten, R. P., Long, F., Murshudov, G. N., Perrakis, A. The PDB_REDO server for macromolecular structure model optimization. IUCrJ 1:213–220 (2014). doi: 10.1107/S2052252514009324
30.
Pettersen, E. F., Goddard, T. D., Huang, C. C., Meng, E. C., Couch, G. S., Croll, T. I., Morris, J. H., Ferrin, T. E. UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Science 30:70–82 (2021). doi: 10.1002/pro.3943
31.
Zhong, E. D., Bepler, T., Berger, B., Davis, J. H. CryoDRGN: Reconstruction of heterogeneous cryo-EM structures using neural networks. Nature Methods 18:176–185 (2021). doi: 10.1038/s41592-020-01049-4
32.
Sehnal, D., Bittrich, S., Deshpande, M., Svobodová, R., Berka, K., Bazgier, V., Velankar, S., Burley, S. K., Koča, J., Rose, A. S. Mol* viewer: Modern web app for 3D visualization and analysis of large biomolecular structures. Nucleic Acids Research 49:W431–W437 (2021). doi: 10.1093/nar/gkab314
33.
Zarr Developers. Zarr: Chunked, compressed, N-dimensional arrays. https://github.com/zarr-developers/zarr-python
34.
Open Microscopy Environment. OME-Zarr / NGFF: Next-generation file format for bioimaging. https://ngff.openmicroscopy.org/
35.
copick contributors. Copick: A storage-agnostic API for cryo-ET datasets and annotations. https://github.com/copick/copick
36.
TileDB, Inc. TileDB: The universal storage engine for multi-dimensional arrays. https://github.com/TileDB-Inc/TileDB
37.
Apache Software Foundation. Apache Arrow DataFusion: An extensible query engine. https://datafusion.apache.org/
38.
The LLVM Project. The LLVM compiler infrastructure. https://llvm.org/
39.
The Linux Foundation. ONNX: Open neural network exchange. https://onnx.ai/
40.
H5MD developers. H5MD: An HDF5-based file format for molecular simulation data. https://github.com/h5md/h5md
41.
IHM working group. IHMCIF: A data dictionary for integrative/hybrid methods structures. https://github.com/ihmwg/IHMCIF
42.
Naef, L., Bronstein, M. M. Black-box data: A new paradigm for biomedicine in the AI era. Chemical Science (2026). doi: 10.1039/d6sc01189f