Challenges in Applying DNA-Binding Protein Predictors to Biological Research.

Abstract

DNA binding proteins play a crucial role in regulating gene expression, DNA replication, and chromatin organization. While many DNA-binding proteins have been identified, many unique DNA-binding proteins in non-model organisms and recently evolved lineage- or species-specific proteins remain uncharacterized or often lack experimental validation. In addition, genetic variants may alter previously known DNA-binding proteins, leading to loss of binding ability. To address this gap, various computational tools have been developed to predict DNA-binding proteins from protein sequences or structures. Yet, their real-world utility in biological research remains uncertain. To evaluate their effectiveness, we assessed the availability and predictive performance of existing tools using five real-world case studies. We found that most tools were web-based, offering accessibility to researchers without computational expertise. However, many suffered from poor maintenance, including frequent server connection problems, input errors, and long processing times. Among the ten tools that were functional and practical, we found that prediction scores often failed to reflect incorrect outputs, and multiple methods frequently produced the same erroneous predictions. Overall, even a small number of misclassifications can significantly distort biological interpretation, indicating that current DNA-binding prediction tools are not yet sufficiently reliable for empirical research.