[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124999-en":3,"doc-seo-124999-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124999,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Bridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning - Journal Article - Abstract and Key Findings","Metalloproteins rely on metal ions as cofactors to drive highly efficient and specific reactions, frequently involving oxidation-state changes in metal active sites during electron transfer. Cryo-EM, X-ray crystallography, and XFEL crystallography help reveal mechanisms, yet radiation damage and experimental variability can distort oxidation states. Machine learning models trained on Cambridge Crystallographic Data Centre data predict metal oxidation states with high accuracy (82–94%). Applied to over 30,000 metal clusters in metalloproteins (Fe, Mn, Co, Cu), the results show predominantly lower oxidation states (Fe2+ 77%, Mn2+ 85%, Co2+ 65%, Cu+ 64%) that align with standard reduction potentials. No clear relationship appears with structure resolution, supporting robust cross-technique evaluation. The associated data and code are available publicly.","University of Groningen  \nBridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning  \nKapuścińska, Katarzyna; Dukała, Zofia; Doha, Mekhola; Ansari, Eman; Wang, Jimin; Brudvig,  \nGary W. ; Brooks, Bernand; Amin, Muhamed Published in:  \nJournal of chemical information and modeling  \nDOI:  \n10.1021/acs.jcim.3c01564  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2024  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nKapuścińska, K. , Dukała, Z. , Doha, M. , Ansari, E. , Wang, J. , Brudvig, G. W. , Brooks, B. , & Amin, M. (2024) . Bridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning.  \nJournal of chemical information and modeling, 64(7), 2586-2593 . [https://doi.org/10.1021/acs.jcim.3c01564](https://doi.org/10.1021/acs.jcim.3c01564)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 29-12-2025  \n[pubs.acs.org/jcim](pubs.acs.org/jcim)  Article   \nBridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning  \nKatarzyna Kapúscínska,⊥ Zofia Dukała,⊥ Mekhola Doha,⊥ Eman Ansari,⊥ Jimin Wang, Gary W. Brudvig, Bernand Brooks, and Muhamed Amin*  \n Cite This: J. Chem. Inf. Model. 2024, 64, 2586−2593  \nRead Online  \nDownloaded via UNIV GRONINGEN on May 14, 2024 at 11:38:02 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nACCESS  \n Metrics & More  \n Article Recommendations  \n*sı   \nSupporting Information  \nABSTRACT: Metalloproteins require metal ions as cofactors to catalyze specific reactions with remarkable efficiency and specificity. In various electron transfer reactions, metals in the active sites change their oxidation states to facilitate the biochemical reactions. Cryogenic electron microscopy, X-ray, and X-ray free electron laser (XFEL) crystallography are used to image metalloproteins to understand the reaction mechanisms. However, radiation damage in cryoEM and X-ray crystallography, and the challenge of generating homogeneous crystals and keeping the appropriate experimental conditions for all the crystals in XFEL crystallography, may alter the oxidation states. Here, we build  \nmachine learning models trained on a large data set from the Cambridge Crystallographic Data Center to evaluate the metal oxidation states. The models yield high accuracy scores (from 82% to 94%) for all metals in the small molecules. Then, they were used to predict the oxidation states of more th","cbCaiuRt178xtcCT","https://ap.wps.com/l/cbCaiuRt178xtcCT","pdf",5618496,1,9,"English","en",105,"# Abstract\n## Modeling metal oxidation states\n## Training data and predictive performance\n## Application to metalloprotein metal clusters\n## Observed oxidation-state distributions\n## Relation to reduction potentials and resolution\n## Data and code availability","[{\"question\":\"Why is predicting oxidation states in metalloproteins important?\",\"answer\":\"Metalloproteins require metal ions to catalyze reactions, and electron transfer often involves oxidation-state changes at active sites that affect biochemical outcomes.\"},{\"question\":\"What data and methods are used to train the machine learning models?\",\"answer\":\"The models are trained on a large dataset from the Cambridge Crystallographic Data Centre and then used to evaluate metal oxidation states in small molecules.\"},{\"question\":\"How do the model predictions relate to experimental structure resolution?\",\"answer\":\"The study reports no clear correlation between predicted oxidation-state populations and the resolution of the structures, suggesting limited dependence on resolution.\"}]","Bridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning - 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