Evaluation of protein–RNA Docking Web Servers for Template-Free Docking and Comparison with the AlphaFold Server

Protein–RNA docking is a valuable
tool for predicting the
structures of protein–RNA complexes, which allow us to understand
the structural basis for gene expression and regulation, thus facilitating
drug development. Despite the development of several protein–RNA
docking programs, the field remains relatively underdeveloped compared
to protein–protein docking, and a systematic comparison of
these programs in terms of accuracy and efficiency is still lacking.
Recent advances in deep learning-based structure prediction, such
as AlphaFold 3, offer a promising alternative for modeling protein–RNA
complexes. Here, we have compiled a consolidated benchmark data set
of 235 protein–RNA complexes (freely available at https://github.com/tanys-group/protein-rna-docking-benchmark), which were curated from PDB structures deposited up to July 2024,
to assess the performance of five template-free docking web servers
and the AlphaFold Server. Among the docking web servers, HDOCK performed
the best, achieving success rates of 31.1% and 44.7% within the top
1 and top 5 predictions, respectively, as assessed by CAPRI (Critical
Assessment of PRedicted Interactions) metrics. Although AlphaFold
3 outperformed all the docking web servers with an overall success
rate of 87.0% in its top 5 predictions, it failed in nine cases where
docking approaches succeeded and showed a markedly lower success rate
of 40% for protein–RNA complexes outside its training set,
comparable to that of HDOCK (35%). Our study provides valuable insights
into the strengths and limitations of current protein–RNA docking
servers and AlphaFold 3, offering practical guidance for selecting
the appropriate tool for protein–RNA complex structure prediction.
These results also suggest that hybrid approaches combining physics-based
and machine learning methods hold significant promise for achieving
higher prediction accuracy.