[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290934-105":3,"doc-detail-290934-en":81,"detail-sidebar-cat-0-en-105":98},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","characterization-of-rna-polymerase-ii-trigger-loop-mutations-using-molecular-dynamics-simulations-and-machine-learning","Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning","","This study investigates how mutations in the trigger loop (TL) of multisubunit RNA polymerase II affect transcriptional catalysis, fidelity, and substrate interactions. Molecular dynamics simulations and machine learning are used to link individual TL residue changes to phenotypes in the Saccharomyces cerevisiae Pol II system. Fitness values across stress conditions enable modeling phenotypes on a continuum. Machine learning predicts phenotypes from amino-acid sequences with 0.68 R2, while MD-derived features support clustering via a variational auto-encoder.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/characterization-of-rna-polymerase-ii-trigger-loop-mutations-using-molecular-dynamics-simulations-and-machine-learning/290934/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/characterization-of-rna-polymerase-ii-trigger-loop-mutations-using-molecular-dynamics-simulations-and-machine-learning/290934.png","ImageObject",300,407,{"name":42,"@type":43},"Eden","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-03","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",14,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the trigger loop (TL) and why do its mutations matter?","Question",{"text":63,"@type":64},"The trigger loop is a conserved active-site domain in RNA polymerase II that enables transcription through conformational changes and interactions with NTP substrates. TL residue mutations can produce different effects on catalysis and fidelity.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How do molecular dynamics simulations and machine learning contribute to this work?",{"text":68,"@type":64},"Molecular dynamics simulations characterize structural alterations associated with TL mutations, while machine learning models relationships between mutations and phenotypes using fitness values. The study compares prediction performance using sequence-only information versus MD-derived inputs.",{"name":70,"@type":61,"acceptedAnswer":71},"What were the main results about how mutations change TL–substrate and structural interactions?",{"text":72,"@type":64},"Loss-of-function and lethal mutations often increase distances between TL residues and the NTP substrate. Other loss-of-function and lethal substitutions can increase distances between TL and the bridge helix, while some gain-of-function mutants disrupt hydrophobic contacts among TL and nearby helices.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290934,1789642987,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":92,"language":93,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":94,"faqs":95,"seo_title":96,"seo_description":12,"update_tm":80,"read_time":97,"preview_page_count":4},1374391974468,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"UC Merced  \nUC Merced Previously Published Works  \nTitle  \nCharacterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning.  \nPermalink  \n[https://escholarship.org/uc/item/4dd7h4wr](https://escholarship.org/uc/item/4dd7h4wr)  \nJournal  \nPLoS Computational Biology, 19(3)  \nAuthors  \nDutagaci, Bercem  \nDuan, Bingbing Qiu, Chenxi et al.  \nPublication Date  \n2023-03-01  \nDOI  \n10.1371/journal.pcbi.1010999  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nPLOS COMPUTATIONAL BIOLOGY  \nOPEN ACCESS  \nCitation: Dutagaci B, Duan B, Qiu C, Kaplan CD, Feig M (2023) Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning. PLoS Comput Biol 19(3): e1010999 . [https://doi.org/10.1371/](https://doi.org/10.1371/)[ ](https://doi.org/10.1371/)[journal.pcbi.1010999](journal.pcbi.1010999)  \nEditor: Guanghong Wei, Fudan University, CHINA  \nReceived: August 15, 2022  \nAccepted: March 6, 2023  \nPublished: March 22, 2023  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pcbi.1010999](https://doi.org/10.1371/journal.pcbi.1010999)  \n[Copyright:](Copyright:) © [2023](2023) Dutagaci et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: The experimental data is in S1 Spreadsheet, average distances extracted from MD are in S2 Spreadsheet. The ML models and protocols are found at: [https://github](https://github). com/bercemd/PolII-mutants All derivative analysis  \nRESEARCH ARTICLE  \nCharacterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning  \nBercem Dutagaci1 *, Bingbing Duan2, Chenxi Qiu3, Craig D. Kaplan2, Michael Feig4 *  \n1 Department of Molecular and Cell Biology, University of California Merced, Merced, California, United States of America, 2 Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, 3 Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America, 4 Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America  \n* [bercemdutagaci@gmail.com](bercemdutagaci@gmail.com) (BD); [mfeiglab@gmail.com](mfeiglab@gmail.com) (MF)  \nAbstract  \nCatalysis and fidelity of multisubunit RNA polymerases rely on a highly conserved active site domain called the trigger loop (TL), which achieves roles in transcription through conformational changes and interaction with NTP substrates. The mutations of TL residues cause distinct effects on catalysis including hypo-and hyperactivity and altered fidelity. We applied molecular dynamics simulation (MD) and machine learning (ML) techniques to characterize TL mutations in the Saccharomyces cerevisiae RNA Polymerase II (Pol II) system. We did so to determine relationships between individual mutations and phenotypes and to associate phenotypes with MD simulated structural alterations. Using fitness values of mutants under various stress conditions, we modeled phenotypes along a spectrum of continual values. We found that ML could predict the phenotypes with 0.68 R2 correlation from amino acid sequences alone. It was more difficult to incorporate MD data to improve predictions from machine learning, presumably because MD data is too noisy and possibly incomplete to directly infer functional phenotypes. However, a variational auto-encoder m","cbCaietIcqko5FlD","https://ap.wps.com/l/cbCaietIcqko5FlD","pdf",4007750,28,"English","# Abstract\n## Methods\n## Modeling and Prediction\n## Key Findings","[{\"question\":\"What is the trigger loop (TL) and why do its mutations matter?\",\"answer\":\"The trigger loop is a conserved active-site domain in RNA polymerase II that enables transcription through conformational changes and interactions with NTP substrates. TL residue mutations can produce different effects on catalysis and fidelity.\"},{\"question\":\"How do molecular dynamics simulations and machine learning contribute to this work?\",\"answer\":\"Molecular dynamics simulations characterize structural alterations associated with TL mutations, while machine learning models relationships between mutations and phenotypes using fitness values. The study compares prediction performance using sequence-only information versus MD-derived inputs.\"},{\"question\":\"What were the main results about how mutations change TL–substrate and structural interactions?\",\"answer\":\"Loss-of-function and lethal mutations often increase distances between TL residues and the NTP substrate. Other loss-of-function and lethal substitutions can increase distances between TL and the bridge helix, while some gain-of-function mutants disrupt hydrophobic contacts among TL and nearby helices.\"}]","Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning | PDF",71,{"code":4,"msg":82,"data":99},[100,104,108,112,117,122,127,130,135,138,142],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":105,"show_sort_weight":106,"slug":107},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},"Exam",70,"exam",{"id":113,"doc_module":4,"doc_module_name":25,"category_name":114,"show_sort_weight":115,"slug":116},5,"Comic",60,"comic",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":119,"show_sort_weight":120,"slug":121},6,"Technology",50,"technology",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":125,"slug":126},7,"Healthcare",40,"healthcare",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":128,"slug":129},30,"research-report",{"id":131,"doc_module":4,"doc_module_name":25,"category_name":132,"show_sort_weight":133,"slug":134},9,"Religion & Spirituality",20,"religion-spirituality",{"id":133,"doc_module":4,"doc_module_name":25,"category_name":136,"show_sort_weight":133,"slug":137},"World Cup","world-cup",{"id":139,"doc_module":4,"doc_module_name":25,"category_name":140,"show_sort_weight":139,"slug":141},10,"Lifestyle","lifestyle",{"id":143,"doc_module":4,"doc_module_name":25,"category_name":144,"show_sort_weight":113,"slug":145},19,"General","general"]