CrysFormer: Protein structure determination via Patterson maps, deep learning, and partial structure attention

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dc.citation.articleNumber44701en_US
dc.citation.issueNumber4en_US
dc.citation.journalTitleStructural Dynamicsen_US
dc.citation.volumeNumber11en_US
dc.contributor.authorPan, Tomen_US
dc.contributor.authorDun, Chenen_US
dc.contributor.authorJin, Shikaien_US
dc.contributor.authorMiller, Mitchell D.en_US
dc.contributor.authorKyrillidis, Anastasiosen_US
dc.contributor.authorPhillips, George N., Jr.en_US
dc.date.accessioned2024-09-10T19:29:02Zen_US
dc.date.available2024-09-10T19:29:02Zen_US
dc.date.issued2024en_US
dc.description.abstractDetermining the atomic-level structure of a protein has been a decades-long challenge. However, recent advances in transformers and related neural network architectures have enabled researchers to significantly improve solutions to this problem. These methods use large datasets of sequence information and corresponding known protein template structures, if available. Yet, such methods only focus on sequence information. Other available prior knowledge could also be utilized, such as constructs derived from x-ray crystallography experiments and the known structures of the most common conformations of amino acid residues, which we refer to as partial structures. To the best of our knowledge, we propose the first transformer-based model that directly utilizes experimental protein crystallographic data and partial structure information to calculate electron density maps of proteins. In particular, we use Patterson maps, which can be directly obtained from x-ray crystallography experimental data, thus bypassing the well-known crystallographic phase problem. We demonstrate that our method, CrysFormer, achieves precise predictions on two synthetic datasets of peptide fragments in crystalline forms, one with two residues per unit cell and the other with fifteen. These predictions can then be used to generate accurate atomic models using established crystallographic refinement programs.en_US
dc.identifier.citationPan, T., Dun, C., Jin, S., Miller, M. D., Kyrillidis, A., & Phillips, G. N., Jr. (2024). CrysFormer: Protein structure determination via Patterson maps, deep learning, and partial structure attention. Structural Dynamics, 11(4), 044701. https://doi.org/10.1063/4.0000252en_US
dc.identifier.digital044701_1_4-0000252en_US
dc.identifier.doihttps://doi.org/10.1063/4.0000252en_US
dc.identifier.urihttps://hdl.handle.net/1911/117857en_US
dc.language.isoengen_US
dc.publisherAIP Publishing LLCen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleCrysFormer: Protein structure determination via Patterson maps, deep learning, and partial structure attentionen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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