Evidence map›Paper›PMID 42377576›Full record

ArticleJournal of molecular modeling2026

RGTBind: RBF-gate graph transformer with spatially biased attention for protein-DNA binding-site prediction.

Yi Qiu, Duo Zhao, Ying Ye, Jing Chen, Hongjie Wu

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Article in Journal of molecular modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Yi QiuSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, 215009, China.
Duo ZhaoThe Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, 200137, China.
Ying YeThe Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, 200137, China. yeying@hosno7.com.
Jing ChenSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, 215009, China. jingchen@usts.edu.cn.
Hongjie WuSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, 215009, China. hongjiewu@usts.edu.cn.

Funding

National Natural Science Foundation of China 62502334National Research Project 2020YFC2006602Opening Topic Fund of Big Data Intelligent Engineering Laboratory of Jiangsu Province SDGC2157Postgraduate Research &; Practice Innovation Program of Jiangsu Province SJCX25_1835Suzhou Blockchain Data Privacy Protection Innovation Application Laboratory, Provincial Key Laboratory for Computer Information Processing Technology, Soochow University KJS2166
6 · The paper itself

Abstract

contextProtein-DNA binding-site prediction is essential for understanding gene regulation and protein function, but remains difficult because DNA recognition depends on both sequence context and three-dimensional structure. We developed RGTBind, a graph transformer that combines multi-scale radial basis function distance encoding with a learnable threshold-gating mechanism to model spatially informative residue interactions. On the independent Test_129 and Test_181 benchmarks, RGTBind achieved the best F1, AUC, and MCC among the compared methods, supporting the value of distance-aware attention with structure-guided neighbor selection for residue-level protein-DNA binding-site prediction.

methodsEach protein was represented as a residue-level graph derived from AlphaFold2-predicted structures. Residue features included AlphaFold2 single representations, DSSP-derived structural descriptors, PSI-BLAST position-specific scoring matrices (PSSM), and HHblits hidden Markov model (HMM) profiles. Pairwise C

Indexed as

Computational BiologyDNADNA-Binding ProteinsSoftwareAlgorithmsBinding SitesModels, MolecularProtein BindingDNADNA-Binding ProteinsAdaptive threshold gatingDistance-biased self-attentionGraph transformerMulti-scale RBF distance encodingProtein–DNA binding-site prediction

Identifiers

PMID42377576

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.