FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387583274> ?p ?o ?g. }

• W4387583274 abstract "Abstract The interactions between nucleic acids and proteins are important in diverse biological processes. The high-quality prediction of nucleic-acid-binding sites continues to pose a significant challenge. Presently, the predictive efficacy of sequence-based methods is constrained by their exclusive consideration of sequence context information, whereas structure-based methods are unsuitable for proteins lacking known tertiary structures. Though protein structures predicted by AlphaFold2 could be used, the extensive computing requirement of AlphaFold2 hinders its use for genome-wide applications. Based on the recent breakthrough of ESMFold for fast prediction of protein structures, we have developed GLMSite, which accurately identifies DNA- and RNA-binding sites using geometric graph learning on ESMFold predicted structures. Here, the predicted protein structures are employed to construct protein structural graph with residues as nodes and spatially neighboring residue pairs for edges. The node representations are further enhanced through the pre-trained language model ProtTrans. The network was trained using a geometric vector perceptron, and the geometric embeddings were subsequently fed into a common network to acquire common binding characteristics. Finally, these characteristics were input into two fully connected layers to predict binding sites with DNA and RNA, respectively. Through comprehensive tests on DNA/RNA benchmark datasets, GLMSite was shown to surpass the latest sequence-based methods and be comparable with structure-based methods. Moreover, the prediction was shown useful for inferring nucleic-acid-binding proteins, demonstrating its potential for protein function discovery. The datasets, codes, and trained models are available at https://github.com/biomed-AI/nucleic-acid-binding." @default.
• W4387583274 created "2023-10-13" @default.
• W4387583274 creator A5023539493 @default.
• W4387583274 creator A5026379331 @default.
• W4387583274 creator A5076568855 @default.
• W4387583274 creator A5079232368 @default.
• W4387583274 date "2023-09-22" @default.
• W4387583274 modified "2023-10-14" @default.
• W4387583274 title "Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures" @default.
• W4387583274 cites W1593880960 @default.
• W4387583274 cites W1978113501 @default.
• W4387583274 cites W2008708467 @default.
• W4387583274 cites W2018661561 @default.
• W4387583274 cites W2075716829 @default.
• W4387583274 cites W2076048958 @default.
• W4387583274 cites W2078176948 @default.
• W4387583274 cites W2108067237 @default.
• W4387583274 cites W2122137509 @default.
• W4387583274 cites W2126592262 @default.
• W4387583274 cites W2130479394 @default.
• W4387583274 cites W2146341019 @default.
• W4387583274 cites W2169837652 @default.
• W4387583274 cites W2180891129 @default.
• W4387583274 cites W2531919408 @default.
• W4387583274 cites W2582176104 @default.
• W4387583274 cites W2617750324 @default.
• W4387583274 cites W2722005050 @default.
• W4387583274 cites W2807540482 @default.
• W4387583274 cites W2892720054 @default.
• W4387583274 cites W2933350524 @default.
• W4387583274 cites W2950954328 @default.
• W4387583274 cites W2982552606 @default.
• W4387583274 cites W3005769002 @default.
• W4387583274 cites W3121281384 @default.
• W4387583274 cites W3126876258 @default.
• W4387583274 cites W3146944767 @default.
• W4387583274 cites W3177500196 @default.
• W4387583274 cites W3183967699 @default.
• W4387583274 cites W3197696221 @default.
• W4387583274 cites W4205989901 @default.
• W4387583274 cites W4281557145 @default.
• W4387583274 cites W4307180312 @default.
• W4387583274 cites W4327550249 @default.
• W4387583274 cites W4360938460 @default.
• W4387583274 doi "https://doi.org/10.1093/bib/bbad360" @default.
• W4387583274 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37824738" @default.
• W4387583274 hasPublicationYear "2023" @default.
• W4387583274 type Work @default.
• W4387583274 citedByCount "0" @default.
• W4387583274 crossrefType "journal-article" @default.
• W4387583274 hasAuthorship W4387583274A5023539493 @default.
• W4387583274 hasAuthorship W4387583274A5026379331 @default.
• W4387583274 hasAuthorship W4387583274A5076568855 @default.
• W4387583274 hasAuthorship W4387583274A5079232368 @default.
• W4387583274 hasConcept C104317684 @default.
• W4387583274 hasConcept C11413529 @default.
• W4387583274 hasConcept C119857082 @default.
• W4387583274 hasConcept C126142528 @default.
• W4387583274 hasConcept C132525143 @default.
• W4387583274 hasConcept C151730666 @default.
• W4387583274 hasConcept C154945302 @default.
• W4387583274 hasConcept C207060522 @default.
• W4387583274 hasConcept C24107716 @default.
• W4387583274 hasConcept C2779343474 @default.
• W4387583274 hasConcept C2986374874 @default.
• W4387583274 hasConcept C41008148 @default.
• W4387583274 hasConcept C41282012 @default.
• W4387583274 hasConcept C55493867 @default.
• W4387583274 hasConcept C67705224 @default.
• W4387583274 hasConcept C70721500 @default.
• W4387583274 hasConcept C80444323 @default.
• W4387583274 hasConcept C86803240 @default.
• W4387583274 hasConceptScore W4387583274C104317684 @default.
• W4387583274 hasConceptScore W4387583274C11413529 @default.
• W4387583274 hasConceptScore W4387583274C119857082 @default.
• W4387583274 hasConceptScore W4387583274C126142528 @default.
• W4387583274 hasConceptScore W4387583274C132525143 @default.
• W4387583274 hasConceptScore W4387583274C151730666 @default.
• W4387583274 hasConceptScore W4387583274C154945302 @default.
• W4387583274 hasConceptScore W4387583274C207060522 @default.
• W4387583274 hasConceptScore W4387583274C24107716 @default.
• W4387583274 hasConceptScore W4387583274C2779343474 @default.
• W4387583274 hasConceptScore W4387583274C2986374874 @default.
• W4387583274 hasConceptScore W4387583274C41008148 @default.
• W4387583274 hasConceptScore W4387583274C41282012 @default.
• W4387583274 hasConceptScore W4387583274C55493867 @default.
• W4387583274 hasConceptScore W4387583274C67705224 @default.
• W4387583274 hasConceptScore W4387583274C70721500 @default.
• W4387583274 hasConceptScore W4387583274C80444323 @default.
• W4387583274 hasConceptScore W4387583274C86803240 @default.
• W4387583274 hasFunder F4320321001 @default.
• W4387583274 hasFunder F4320335777 @default.
• W4387583274 hasIssue "6" @default.
• W4387583274 hasLocation W43875832741 @default.
• W4387583274 hasLocation W43875832742 @default.
• W4387583274 hasOpenAccess W4387583274 @default.
• W4387583274 hasPrimaryLocation W43875832741 @default.
• W4387583274 hasRelatedWork W1472344963 @default.
• W4387583274 hasRelatedWork W2006649932 @default.
• W4387583274 hasRelatedWork W2071735128 @default.

• next