CNRBind: Small Molecule-RNA Binding Sites Recognition via Site Significant from Nucleotide and Complex Network Information

Background: Small molecule-RNA binding sites play a significant role in developing drugs for disease treatment. However, it is a challenge to propose accurate computational tools for identifying these binding sites. Method: In this study, an accurate prediction model named CNRBind was constructed by extracting site significant information from nucleotide and complex networks. We designed complex networks and calculated three topological structural parameters according to RNA tertiary structure. Acknowledging nucleotide interdependence, a sliding window was selected to integrate the influence of adjacent sites. Finally, the model was constructed using a random forest classifier. Results: Compared to the other computational tools, CNRBind was competitive and had excellent discriminative ability for metal ion-binding site prediction. Furthermore, statistic analysis revealed significant differences between CNRBind and existing methods. Additionally, CNRBind is a promising predictor in cases where experimental tertiary structure is unavailable. Conclusion: These results show that CNRBind is effective because of the proposed site significant information encoding strategy. The approach provides a reasonable supplement for biology researches. The dataset and resource codes can be accessed at: https://github.com/Kangxiaoneuq/CNRBind.