MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures
Abstract At sufficiently high resolution, x-ray crystallography and cryogenic electron microscopy are capable of resolving small spherical map features corresponding to either water or ions. Correct classification of these sites provides crucial insight for understanding structure and function as we...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61315-x |
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| author | Laura Shub Wenjin Liu Georgios Skiniotis Michael J. Keiser Michael J. Robertson |
| author_facet | Laura Shub Wenjin Liu Georgios Skiniotis Michael J. Keiser Michael J. Robertson |
| author_sort | Laura Shub |
| collection | DOAJ |
| description | Abstract At sufficiently high resolution, x-ray crystallography and cryogenic electron microscopy are capable of resolving small spherical map features corresponding to either water or ions. Correct classification of these sites provides crucial insight for understanding structure and function as well as guiding downstream design tasks, including structure-based drug discovery and de novo biomolecule design. However, direct identification of these sites from experimental data can prove challenging, and existing empirical approaches leveraging the local environment can only characterize limited ion types. We present a representation of chemical environments using interaction fingerprints and develop a machine learning model to predict the identity of input water and ion sites. We validate the method, named Metric Ion Classification (MIC), on a wide variety of biomolecular examples to demonstrate its utility, identifying many probable mismodeled ions deposited in the PDB. Compared to existing methods, MIC achieves superior accuracy for uniquely classifying water/ion sites while expanding the set of potential site identities. Finally, we collect all steps of this approach into an easy-to-use open-source package that can integrate with existing structure determination pipelines, and we provide a ChimeraX implementation to further enable use of the tool. |
| format | Article |
| id | doaj-art-c80ab8236cc147d498f45300681c4f49 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-c80ab8236cc147d498f45300681c4f492025-08-20T03:03:33ZengNature PortfolioNature Communications2041-17232025-07-0116111410.1038/s41467-025-61315-xMIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structuresLaura Shub0Wenjin Liu1Georgios Skiniotis2Michael J. Keiser3Michael J. Robertson4Department of Pharmaceutical Chemistry; Institute for Neurodegenerative Diseases, University of California, San FranciscoDepartment of Pharmaceutical Chemistry; Institute for Neurodegenerative Diseases, University of California, San FranciscoDepartment of Structural Biology, St. Jude Children’s Research HospitalDepartment of Pharmaceutical Chemistry; Institute for Neurodegenerative Diseases, University of California, San FranciscoVerna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of MedicineAbstract At sufficiently high resolution, x-ray crystallography and cryogenic electron microscopy are capable of resolving small spherical map features corresponding to either water or ions. Correct classification of these sites provides crucial insight for understanding structure and function as well as guiding downstream design tasks, including structure-based drug discovery and de novo biomolecule design. However, direct identification of these sites from experimental data can prove challenging, and existing empirical approaches leveraging the local environment can only characterize limited ion types. We present a representation of chemical environments using interaction fingerprints and develop a machine learning model to predict the identity of input water and ion sites. We validate the method, named Metric Ion Classification (MIC), on a wide variety of biomolecular examples to demonstrate its utility, identifying many probable mismodeled ions deposited in the PDB. Compared to existing methods, MIC achieves superior accuracy for uniquely classifying water/ion sites while expanding the set of potential site identities. Finally, we collect all steps of this approach into an easy-to-use open-source package that can integrate with existing structure determination pipelines, and we provide a ChimeraX implementation to further enable use of the tool.https://doi.org/10.1038/s41467-025-61315-x |
| spellingShingle | Laura Shub Wenjin Liu Georgios Skiniotis Michael J. Keiser Michael J. Robertson MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures Nature Communications |
| title | MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures |
| title_full | MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures |
| title_fullStr | MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures |
| title_full_unstemmed | MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures |
| title_short | MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures |
| title_sort | mic a deep learning tool for assigning ions and waters in cryo em and crystal structures |
| url | https://doi.org/10.1038/s41467-025-61315-x |
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