[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126983-en":3,"doc-seo-126983-105":30,"detail-sidebar-cat-0-en-105":95},{"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},126983,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Recent Methods from Statistical Inference and Machine Learning to Improve Integrative Modeling of Macromolecular Assemblies - Research overview","Recent methods from statistical inference and machine learning focus on improving integrative modeling of macromolecular assemblies, especially when experiments provide complementary but incomplete information. The material surveys integrative structure modeling approaches spanning static structure modeling and dynamical modeling, including platforms and tools such as IMP, Assembline, HADDOCK, PLUMED-ISDB, and BioEn. It also reviews recent applications across chromatin-modifying, ciliary/flagellar, membrane-trafficking, nuclear pore, and immune-response complexes. Emphasis is placed on advances in EM and XLMS data handling, crosslink-derived constraints, improved model representations, multi-scale and multi-state modeling, and validation algorithms, followed by frontiers including AI-assisted experiments, cryo-ET-driven modeling, and spatio-temporal whole-cell models.","Recent methods from statistical inference and machine learning to improve integrative modeling of macromolecular assemblies  \nShreyas Arvindekar 1,* , Kartik Majila 1,* , Shruthi Viswanath 1,\\#  \n1 National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065.  \n*Contributed equally. Author order was determined by a coin toss.  \n\\#Corresponding author. Email: [shruthiv@ncbs.res.in](shruthiv@ncbs.res.in)  \nContents  \nList of symbols and abbreviations ............................................................................... 4  \nAbstract .......................................................................................................................... 7  \nIntroduction to Integrative Structure Modeling ........................................................... 7  \nStatic structure modeling ....................................................................................................... 8  \nIntegrative Modeling Platform (IMP) .....................................................................................................8  \nAssembline ......................................................................................................................................... 11  \nHADDOCK.......................................................................................................................................... 12  \nIntegrative Structure Determination using Density Maps ................................................................... 13  \nDynamical structure modeling............................................................................................. 15  \nPlumed Integrative Structural and Dynamical Biology (PLUMED-ISDB) ............................................15  \nBioEn .................................................................................................................................................. 16  \nRecent Applications of Integrative Structure Modeling ........................................... 16  \nChromatin-modifying complexes ........................................................................................ 16  \nWDR76-SPIN1-nucleosome complex ................................................................................................ 17  \nRvb1-Rvb2-INO80 complex ................................................................................................................ 17  \nSmc5/6 complex ................................................................................................................................. 18  \nNuRD complex ................................................................................................................................... 18  \nCiliary and flagellar complexes............................................................................................ 19  \nCell-cell junctions.................................................................................................................. 20  \nMembrane-trafficking complexes ........................................................................................ 20  \nNuclear pore complexes .....................................................................................................................20  \nType III Secretion System ..................................................................................................................22  \nComplexes involved in immune response.......................................................................... 22  \nRecent Advances in Integrative Modeling Methods ................................................. 23  \nNew sources of data ............................................................................................................. 23  \nImproved methods for modeling with EM and XLMS data ................................................ 25  \nNew methods for Electron-Microscopy (EM) data ........................................................................","cbCaiovWFJ7oBVBd","https://ap.wps.com/l/cbCaiovWFJ7oBVBd","pdf",1178806,1,62,"English","en",105,"# Introduction to Integrative Structure Modeling\n## Static structure modeling\n## Dynamical structure modeling\n# Integrative Structure Determination using Density Maps\n# Recent Applications of Integrative Structure Modeling\n## Chromatin-modifying complexes\n## Ciliary and flagellar complexes\n## Membrane-trafficking complexes\n## Nuclear pore complexes\n# Recent Advances in Integrative Modeling Methods\n## New sources of data\n## Improved methods for EM and XLMS data\n## New methods for chemical crosslinks\n## Advances in model representation\n## Multi-scale modeling\n## Multi-state modeling\n## Sampling, analysis, and validation algorithms\n# Frontiers in integrative modeling\n## Integrating AI-based structure predictions with experiments\n## Using cryo electron-tomography data for integrative modeling\n## Spatio-temporal models of cells","[{\"question\":\"What does integrative structure modeling aim to achieve for macromolecular assemblies?\",\"answer\":\"It combines complementary sources of experimental and computational information to determine assembly structures. The document frames this as improving both structural and, in some cases, dynamical understanding of macromolecular systems.\"},{\"question\":\"Which tools and platforms are discussed for static and dynamical modeling?\",\"answer\":\"The text lists IMP, Assembline, and HADDOCK for integrative structure modeling, and PLUMED-ISDB and BioEn for integrative structural and dynamical biology workflows.\"},{\"question\":\"How do EM, XLMS, and chemical crosslinks contribute to better models?\",\"answer\":\"The document highlights improved methods for modeling with EM and XLMS data and new approaches that incorporate chemical crosslinks. It also explains how crosslinks can validate and improve AI-driven structure predictions.\"},{\"question\":\"What are the main frontiers mentioned for future integrative modeling?\",\"answer\":\"Frontiers include integrating AI-based structure predictions with experiments, using cryo electron-tomography data for integrative modeling, and building spatio-temporal models of cells. The material links these goals to AI-based localization and identification in tomograms.\"}]","Recent Methods from Statistical Inference and Machine Learning to Improve Integrative Modeling of Macromolecular Assemblies - Research overview | PDF",1785936033,156,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"recent-methods-from-statistical-inference-and-machine-learning-to-improve-integrative-modeling-of-macromolecular-assemblies-research-overview","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/recent-methods-from-statistical-inference-and-machine-learning-to-improve-integrative-modeling-of-macromolecular-assemblies-research-overview/126983/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What does integrative structure modeling aim to achieve for macromolecular assemblies?","Question",{"text":75,"@type":76},"It combines complementary sources of experimental and computational information to determine assembly structures. The document frames this as improving both structural and, in some cases, dynamical understanding of macromolecular systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which tools and platforms are discussed for static and dynamical modeling?",{"text":80,"@type":76},"The text lists IMP, Assembline, and HADDOCK for integrative structure modeling, and PLUMED-ISDB and BioEn for integrative structural and dynamical biology workflows.",{"name":82,"@type":73,"acceptedAnswer":83},"How do EM, XLMS, and chemical crosslinks contribute to better models?",{"text":84,"@type":76},"The document highlights improved methods for modeling with EM and XLMS data and new approaches that incorporate chemical crosslinks. It also explains how crosslinks can validate and improve AI-driven structure predictions.",{"name":86,"@type":73,"acceptedAnswer":87},"What are the main frontiers mentioned for future integrative modeling?",{"text":88,"@type":76},"Frontiers include integrating AI-based structure predictions with experiments, using cryo electron-tomography data for integrative modeling, and building spatio-temporal models of cells. 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