Predicting Small Molecule Ligand/RNA Binding Pocket Binding Modes Using Metadynamics

Understanding the structural dynamics of how small molecule ligand recognize its RNA binding pocket is always a crucial determinant in pharmaceutical research. Molecular dynamics (MD) simulation is often used to interpretate this process at atomic resolution. However, the insurmountable high energy barriers in the binding pathway results in the nonergodic dynamics for unbiased MD sampling. To address this limitation, we applied well-tempered metadynamics coupled with upper wall restrain in this work, therefore providing an novel modeling approach for sampling the multiple state transitions during this binding process and probing the most energy favorable binding modes through two-dimensional free energy landscape reconstructed by incorporating couple possible hydrogen binding interactions between small molecule ligand and its RNA binding pocket as collective variables (CVs). Our computational predictions of binding modes for all five cases studied are in quantitative agreement with structures solved by X-ray crystallography or NMR with RMSD less than 2.0 [A]. In addition, we presented the first molecular dynamics binding pathway and binding mechanism for the three cases of in vitro selected RNA aptamer. Our study demonstrated that metadynamics can be applied to effectively sampling state transitions of ligand binding events. By coupling with upper wall restrain, we have enabled fast free energy profile calculation and binding mode prediction for small molecule-RNA binding process, facilitating RNA-ligand binding investigation. This method therefore could be much-needed in computer-aided drug design pipelines of RNA-targeted small molecule compounds. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/560960v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@8049b0org.highwire.dtl.DTLVardef@65ed27org.highwire.dtl.DTLVardef@1b4b54aorg.highwire.dtl.DTLVardef@ea2ef9_HPS_FORMAT_FIGEXP M_FIG C_FIG