[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121850-en":3,"doc-seo-121850-105":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},121850,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Unlocking novel therapies - cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning","Existing therapies for neurodegenerative diseases address symptoms rather than preventing onset, often hindered by limited selectivity, stability, and bioavailability. Cyclic peptide therapeutics offer improved in vivo stability and antibody-like binding affinity, yet de novo design remains difficult due to missing druggable pockets, incomplete conformational sampling, unknown binding sites, and large combinatorial sequence space. This article reviews computational advances and proposes integrated experimental–simulation–machine-learning strategies to design cyclic peptide inhibitors that curb toxic amyloid propagation and support template generation of programmable (bio)materials.","UvA-DARE (Digital Academic Repository)  \nUnlocking novel therapies  \ncyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning  \nde Raffele, D. ; Ilie, I. M.  \nDOI  \n10.1039/d3cc04630c  \nPublication date  \n2024  \nDocument Version  \nFinal published version  \nPublished in  \nChemical Communications  \nLicense  \nCC BY  \nLink to publication  \nCitation for published version (APA):  \nde Raffele, D. , & Ilie, I. M. (2024) . Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning. Chemical Communications, 60(6), 632-645 . [https://doi.org/10.1039/d3cc04630c](https://doi.org/10.1039/d3cc04630c)  \nGeneral rights  \nIt 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), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:18 Oct 2024  \nOpen Access Article . Pu on 07 Decemberblished 2023. Downloaded on 4/ 1 1/2024 11:50:08 AM .  \nChemComm  \n|  |  |  |\n| --- | --- | --- |\n|  | HIGHLIGHT View Article Online |  |\n|  | View Journal | View Issue |  |\n\nCite this: Chem. Commun., 2024, 60, 632  \nReceived 18th September 2023, Accepted 6th December 2023  \nDOI: 10.1039/d3cc04630c[rsc.li/chemcomm](rsc.li/chemcomm)  \nUnlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning  \nDaria de Raffeleab and Ioana M. Ilie  *ab  \nExisting therapies for neurodegenerative diseases like Parkinson’s and Alzheimer’s address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. Wereview the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experi","cbCaifClf0XVgaXV","https://ap.wps.com/l/cbCaifClf0XVgaXV","pdf",2247885,1,15,"English","en",105,"# Introduction\n## Therapeutic challenges for neurodegenerative diseases\n## Cyclic peptide therapeutics and design bottlenecks\n## Integrated strategies: experiments, simulations, and machine learning\n## Computational review focus and proposed approaches","[{\"question\":\"Why are current neurodegenerative therapies often insufficient?\",\"answer\":\"They mainly treat symptoms and do not prevent disease onset. Common agents also face issues with selectivity, stability, and bioavailability, leading to poor clinical performance.\"},{\"question\":\"What makes cyclic peptides attractive for targeting amyloidogenic processes?\",\"answer\":\"They can provide long in vivo stability while retaining robust, antibody-like binding affinity, making them promising peptide-based therapeutics.\"},{\"question\":\"What challenges complicate de novo cyclic peptide design?\",\"answer\":\"Design is hindered by lack of long-lived druggable pockets, incomplete conformational distributions of target and binder, unknown binding sites, methodological limitations, and constraints related to failed trials, time, and cost, alongside a vast sequence search space.\"}]","Unlocking novel therapies - cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning | PDF",1785807228,38,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"unlocking-novel-therapies-cyclic-peptide-design-for-amyloidogenic-targets-through-synergies-of-experiments-simulations-and-machine-learning","",{"@graph":36,"@context":85},[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/unlocking-novel-therapies-cyclic-peptide-design-for-amyloidogenic-targets-through-synergies-of-experiments-simulations-and-machine-learning/121850/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are current neurodegenerative therapies often insufficient?","Question",{"text":75,"@type":76},"They mainly treat symptoms and do not prevent disease onset. Common agents also face issues with selectivity, stability, and bioavailability, leading to poor clinical performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes cyclic peptides attractive for targeting amyloidogenic processes?",{"text":80,"@type":76},"They can provide long in vivo stability while retaining robust, antibody-like binding affinity, making them promising peptide-based therapeutics.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges complicate de novo cyclic peptide design?",{"text":84,"@type":76},"Design is hindered by lack of long-lived druggable pockets, incomplete conformational distributions of target and binder, unknown binding sites, methodological limitations, and constraints related to failed trials, time, and cost, alongside a vast sequence search space.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]