Rogerio Margis, Iara Macedo, Nureyev F Rodrigues, Mateus Dias-Oliveira, Fernanda Lazzarotto, Diego Trindade de Souza, Geancarlo Zanatta
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引用次数: 0
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.
期刊介绍:
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
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