Bioinformatics Portal for Predicting Binding Regions and Modes in Protein–Nucleic Acid Interactions

Protein-nucleic acid interactions (PNIs) are essential for biological processes, including gene regulation, DNA repair, and viral infection. The changes in binding regions and modes in PNIs are vital for understanding the action mechanism and detecting abnormalities. Computational methods, particularly those leveraging machine learning (ML) and deep learning (DL), have become powerful tools for predicting PNI binding sites and structural features. However, systematic evaluation is needed to ensure the reliability and promote innovation in these bioinformatics resources. Here, we present a comprehensive toolbox for PNIs. It includes curated databases detailing interaction types, sources, cross-domain interactions, and potential applications. Then, we investigated a toolbox that leveraged ML and DL-based algorithms to predict binding sites and conformational dynamics, with the aim of uncovering the molecular mechanisms underlying PNIs. Additionally, we discussed the potential applications of drug research related to PNIs. This study introduces a suite of advanced predictive tools that utilize computational modeling to enhance the design of nucleic acid therapeutics forward. We've streamlined these tools into a user-friendly online platform accessible for academic use at http://rv.agroda.cn/pni_portal.