[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118047-en":3,"doc-seo-118047-105":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":4,"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},118047,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality","Machine learning map modification tools are assessed for their differential impact on cryogenic electron microscopy (cryoEM) densities. The perspective evaluates how ML-based methods affect biomacromolecules versus ligands, reporting generally improved density quality for biomacromolecules alongside unpredictable outcomes for ligands. Effects are characterized through both quantitative map-quality metrics and qualitative inspection of modified maps. The work emphasizes the power of ML in cryoEM while also outlining risks that may arise from unexamined use.","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \nMachine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality.  \nPermalink  \n[https://escholarship.org/uc/item/68n0c77j](https://escholarship.org/uc/item/68n0c77j)  \nAuthors  \nBerkeley, Raymond  \nCook, Brian Herzik, Mark  \nPublication Date  \n2024  \nDOI  \n10.3389/fmolb.2024.1404885  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nTYPE Perspective  \nPUBLISHED 18 April 2024  \nDOI 10.3389/fmolb.2024.1404885  \nOPEN ACCESS  \nEDITED BY  \nEdward T. Eng,  \nNew York Structural Biology Center, United States  \nREVIEWED BY  \nKiefer Ramberg,  \nTrinity College Dublin, Ireland  \n*CORRESPONDENCE  \nMark A. Herzik Jr,  [mherzik@ucsd.edu](mherzik@ucsd.edu)  \n†These authors have contributed equally to this work  \nRECEIVED 21 March 2024  \nACCEPTED 03 April 2024  \nPUBLISHED 18 April 2024  \nCITATION  \nBerkeley RF, Cook BD, Herzik MA Jr. (2024), Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality. Front. Mol. Biosci. 11:1404885 .  \ndoi: 10.3389/fmolb.2024.1404885  \nCOPYRIGHT  \n© 2024 Berkeley, Cook and Herzik. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality  \nRaymond F. Berkeley†, Brian D. Cook† and Mark A. Herzik Jr. *†  \nDepartment of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, United States  \nThe application of machine learning to cryogenic electron microscopy (cryoEM) data analysis has added a valuable set of tools to the cryoEM data processing pipeline. As these tools become more accessible and widely available, the implications of their use should be assessed. We noticed that machine learning map modification tools can have differential effects on cryoEM densities. In this perspective, we evaluate these effects to show that machine learning tools generally improve densities for biomacromolecules while generating unpredictable results for ligands. This unpredictable behavior manifests both in quantitative metrics of map quality and in qualitative investigations of modified maps. The results presented here highlight the power and potential of machine learning tools in cryoEM, while also illustrating some of the risks of their unexamined use.  \nKEYWORDS  \ncryogenic electron microscopy, cryoEM, density modification, model building, machine learning  \n1 Introduction  \nOver the past decade, cryogenic electron microscopy (cryoEM) has matured into a leading method for determining the three-dimensional (3-D) structures of dynamic macromolecular complexes (DiIorio and Kulczyk, 2022) . Reductions in the cost of GPU-accelerated computing coupled with improvements in both microscope and detector hardware have facilitated the acquisition of high-quality, high-resolution cryoEM data (Nakane et al., 2020; Yip et al., 2020; Fréchin et al., 2023) . Hardware improvements have been complemented by concurrent developments in cryoEM data processing software, drastically improving both the performance and accessibility of the cryoEM data processing pipeline (Scheres, 2012; Punjani et al., 2017; Tegunov and Cramer, 2019) . These efforts have enabled the democratization of cryoEM, allowing researchers with a range of scientific backgrounds to determine high-resolution structures of diverse biomacromolecules using cryoEM (Baldwin et al., 2018) .  \nThe ","cbCaitfioq1EXYbD","https://ap.wps.com/l/cbCaitfioq1EXYbD","pdf",2504330,1,9,"English","en",105,"# Introduction\n## CryoEM progress and workflow growth\n## Machine learning across cryoEM pipeline\n## Map modification benefits and potential risks","[{\"question\":\"What is the main focus of this perspective on cryoEM density modification?\",\"answer\":\"It evaluates how machine learning map modification tools differentially affect cryoEM densities, comparing impacts on biomacromolecules versus ligands.\"},{\"question\":\"How do machine learning methods typically influence biomacromolecule density quality?\",\"answer\":\"Machine learning tools generally improve densities for biomacromolecules, yielding better map quality.\"},{\"question\":\"Why are ligand results described as unpredictable?\",\"answer\":\"The perspective notes that ML-based map modification can produce unpredictable behavior for ligands, reflected in both quantitative quality metrics and qualitative changes in modified maps.\"}]","Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality | 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