Improved Accuracy in RNA–Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function

RNA–protein interactions play fundamental roles
in many biological processes. To understand these interactions, it
is necessary to know the three-dimensional structures of RNA–protein
complexes. However, determining the tertiary structure of these complexes
is often difficult, suggesting that an accurate rigid body docking
for RNA–protein complexes is needed. In general, the rigid
body docking process is divided into two steps: generating candidate
structures from the individual RNA and protein structures and then
narrowing down the candidates. In this study, we focus on the former
problem to improve the prediction accuracy in RNA–protein docking.
Our method is based on the integration of physicochemical information
about RNA into ZDOCK, which is known as one of the most successful
computer programs for protein–protein docking. Because recent
studies showed the current force field for molecular dynamics simulation
of protein and nucleic acids is quite accurate, we modeled the physicochemical
information about RNA by force fields such as AMBER and CHARMM. A
comprehensive benchmark of RNA–protein docking, using three
recently developed data sets, reveals the remarkable prediction accuracy
of the proposed method compared with existing programs for docking:
the highest success rate is 34.7% for the predicted structure of the
RNA–protein complex with the best score and 79.2% for 3,600
predicted ones. Three full atomistic force fields for RNA (AMBER94,
AMBER99, and CHARMM22) produced almost the same accurate result, which
showed current force fields for nucleic acids are quite accurate.
In addition, we found that the electrostatic interaction and the representation
of shape complementary between protein and RNA plays the important
roles for accurate prediction of the native structures of RNA–protein
complexes.