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CoCoNet—boosting RNA contact prediction by convolutional neural networks

Zerihun, Mehari B.; Pucci, Fabrizio; Schug, Alexander


Co-evolutionary models such as direct coupling analysis (DCA) in combination with machine learning (ML) techniques based on deep neural networks are able to predict accurate protein contact or distance maps. Such information can be used as constraints in structure prediction and massively increase prediction accuracy. Unfortunately, the same ML methods cannot readily be applied to RNA as they rely on large structural datasets only available for proteins. Here, we demonstrate how the available smaller data for RNA can be used to improve prediction of RNA contact maps. We introduce an algorithm called CoCoNet that is based on a combination of a Coevolutionary model and a shallow Convolutional Neural Network. Despite its simplicity and the small number of trained parameters, the method boosts the positive predictive value (PPV) of predicted contacts by about 70% with respect to DCA as tested by cross-validation of about eighty RNA structures. However, the direct inclusion of the CoCoNet contacts in 3D modeling tools does not result in a proportional increase of the 3D RNA structure prediction accuracy. Therefore, we suggest that the field develops, in addition to contact PPV, metrics which estimate the expected impact for 3D structure modeling tools better. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000142303
Veröffentlicht am 25.01.2022
DOI: 10.1093/nar/gkab1144
Zitationen: 3
Web of Science
Zitationen: 2
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Steinbuch Centre for Computing (SCC)
Universität Karlsruhe (TH) – Zentrale Einrichtungen (Zentrale Einrichtungen)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2021
Sprache Englisch
Identifikator ISSN: 0305-1048, 1362-4962
KITopen-ID: 1000142303
HGF-Programm 46.21.01 (POF IV, LK 01) Domain-Specific Simulation & SDLs and Research Groups
Erschienen in Nucleic acids research
Verlag Oxford University Press (OUP)
Band 49
Heft 22
Seiten 12661–12672
Vorab online veröffentlicht am 06.12.2021
Nachgewiesen in Dimensions
Web of Science
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