Hauptseite > Publikationsdatenbank > Influence of Contact Map Topology on RNA Structure Prediction > print

001     1048746
005     20251222202220.0
024 7 _ |a 10.1093/nar/gkaf1370
|2 doi
024 7 _ |a 10.34734/FZJ-2025-04861
|2 datacite_doi
037 _ _ |a FZJ-2025-04861
082 _ _ |a 570
100 1 _ |a Faber, Christian
|0 P:(DE-Juel1)188661
|b 0
|u fzj
245 _ _ |a Influence of Contact Map Topology on RNA Structure Prediction
260 _ _ |a Oxford
|c 2025
|b Oxford Univ. Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1766383945_30590
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a The available sequence data of RNA molecules have greatly increased in the past years. Unfortunately, while computational power is still under exponential growth, the computer prediction quality from sequence to final structure is still inferior to labour-intensive experimental work. Although a reliable end-to-end procedure has already been developed for proteins since Alphafold2, while its successor AlphaFold3 can also predict RNA, its confidence, in particular for novel sequences and folds, still appears limited. Another strategy entails two steps: (i) predicting potential contacts in the form of a contact map from evolutionary data; and (ii) simulating the molecule with a physical force field while using the contact map as restraint. However, the quality of the structure prediction crucially depends on the quality of the contact map. Until now, only the proportion of true positive contacts was considered as a quality characteristic. We propose to also include the distribution of these contacts, and have done so in our recent studies. We observed that the clustering of contacts, as is common for many artificial intelligence algorithms, has a negative impact on prediction quality. In contrast, a more distributed topology is beneficial. We have applied these findings from computer experiments to current algorithms and introduced a measure of distribution, the Gaussian score.
536 _ _ |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5111
|c POF4-511
|f POF IV
|x 0
588 _ _ |a Dataset connected to DataCite
700 1 _ |a Upadhyay, Utkarsh
|0 P:(DE-Juel1)195915
|b 1
|u fzj
700 1 _ |a Taubert, Oskar
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Schug, Alexander
|0 P:(DE-Juel1)173652
|b 3
|e Corresponding author
773 _ _ |a 10.1093/nar/gkaf1370
|0 PERI:(DE-600)1472175-2
|p gkaf1370
|t Nucleic acids research
|v 53
|y 2025
|x 0305-1048
856 4 _ |u https://juser.fz-juelich.de/record/1048746/files/Invoice_SOA25LT015730.pdf
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/1048746/files/gkaf1370.pdf
909 C O |o oai:juser.fz-juelich.de:1048746
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)188661
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)195915
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)173652
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5111
|x 0
914 1 _ |y 2025
915 p c |a APC keys set
|0 PC:(DE-HGF)0000
|2 APC
915 p c |a DOAJ Journal
|0 PC:(DE-HGF)0003
|2 APC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2024-12-10
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2024-12-10
915 _ _ |a IF >= 10
|0 StatID:(DE-HGF)9910
|2 StatID
|b NUCLEIC ACIDS RES : 2022
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2024-04-03T10:37:02Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2024-04-03T10:37:02Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2024-12-10
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-10
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2024-04-03T10:37:02Z
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2024-12-10
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NUCLEIC ACIDS RES : 2022
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-10
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2024-12-10
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-10
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a APC
980 1 _ |a APC
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21