NABhClassifier Server: A Tool for the Identification of Helical Nucleic Acid-Binding Sequences in Proteins

Engineered proteins capable of binding and transporting nucleic acids hold significant potential for advancing disease control in both the medical and agricultural fields. However, identifying small nucleic acid-binding domains remains challenging, as existing predictors primarily classify entire proteins as binders or nonbinders rather than targeting specific binding regions. Here, we introduce NABhClassifier, a highly efficient and precise web server designed to detect small helical sequences with nucleic acid-binding potential. Featuring an intuitive interface and a fully automated prediction pipeline, NABhClassifier integrates eight machine learning models for rapid analysis, delivering results in seconds per protein sequence. Predictions are summarized in the NABh index, a consensus score that combines outputs from all models for enhanced reliability. The server's accuracy has been validated on data sets of DNA-binding and single- and double-stranded RNA-binding proteins from various species. NABhClassifier provides a powerful tool for identifying small helices with nucleic acid-binding capacity, facilitating the discovery of novel biotechnological applications. The server, along with tutorials, is freely accessible at http://143.54.25.149.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-02-22
DOI
https://doi.org/10.1021/acs.jcim.4c02244
Citations
1
Primary Topic
RNA and protein synthesis mechanisms
Type
article
Field-Weighted Citation Impact
0.81

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article

NABhClassifier Server: A Tool for the Identification of Helical Nucleic Acid-Binding Sequences in Proteins

Rogério Margis, Nureyev Ferreira Rodrigues, Fernanda Lazzarotto, Geancarlo Zanatta et al.
1 citations
Journal of Chemical Information and Modeling
RNA and protein synthesis mechanisms
0.81
article

NABhClassifier Server: A Tool for the Identification of Helical Nucleic Acid-Binding Sequences in Proteins

Rogério Margis, Nureyev Ferreira Rodrigues, Fernanda Lazzarotto, Geancarlo Zanatta, Diego Trindade de Souza, Iara Ferreira De Macedo, Mateus Dias-Oliveira
article en
1 citations

Abstract

Engineered proteins capable of binding and transporting nucleic acids hold significant potential for advancing disease control in both the medical and agricultural fields. However, identifying small nucleic acid-binding domains remains challenging, as existing predictors primarily classify entire proteins as binders or nonbinders rather than targeting specific binding regions. Here, we introduce NABhClassifier, a highly efficient and precise web server designed to detect small helical sequences with nucleic acid-binding potential. Featuring an intuitive interface and a fully automated prediction pipeline, NABhClassifier integrates eight machine learning models for rapid analysis, delivering results in seconds per protein sequence. Predictions are summarized in the NABh index, a consensus score that combines outputs from all models for enhanced reliability. The server's accuracy has been validated on data sets of DNA-binding and single- and double-stranded RNA-binding proteins from various species. NABhClassifier provides a powerful tool for identifying small helices with nucleic acid-binding capacity, facilitating the discovery of novel biotechnological applications. The server, along with tutorials, is freely accessible at http://143.54.25.149.

Journal of Chemical Information and ModelingVol. 65(5)
Universidade Federal do Rio Grande do Sul (BR), University of Rio Grande and Rio Grande Community College (US)
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul
Zero hunger
Openalex Percentile: Top 31%
RNA and protein synthesis mechanisms
0.81
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