MINT: software to identify motifs and short-range interactions in trajectories of nucleic acids

Abstract

Structural biology experiments and structure prediction tools have provided many high-resolution three-dimensional structures of nucleic acids. Also, molecular dynamics force field parameters have been adapted to simulating charged and flexible nucleic acid structures on microsecond time scales. Therefore, we can generate the dynamics of DNA or RNA molecules, but we still lack adequate tools for the analysis of the resulting huge amounts of data. We present MINT (Motif Identifier for Nucleic acids Trajectory) - an automatic tool for analyzing three-dimensional structures of RNA and DNA, and their full-atom molecular dynamics trajectories or other conformation sets (e.g. X-ray or nuclear magnetic resonance-derived structures). For each RNA or DNA conformation MINT determines the hydrogen bonding network resolving the base pairing patterns, identifies secondary structure motifs (helices, junctions, loops, etc.) and pseudoknots. MINT also estimates the energy of stacking and phosphate anion-base interactions. For many conformations, as in a molecular dynamics trajectory, MINT provides averages of the above structural and energetic features and their evolution. We show MINT functionality based on all-atom explicit solvent molecular dynamics trajectory of the 30S ribosomal subunit.

Figure 1.

MINT workflow. The main function implements the analysis of a single frame. For a trajectory, the function first creates a table of all atoms from a .pdb file with their coordinates from the entire trajectory. While analyzing trajectory frames, the coordinates are read from the created table.

Figure 2.

Nucleotide edges with hydrogen donors in green, acceptors in blue and atoms that serve as both hydrogen donor and acceptor in orange (27).

Figure 3.

The phosphate group oxygen atom ‘stacking’ over the guanine base. The oxygen and base atoms are shown as spheres of sizes corresponding to their VDW radii. A fragment is from the 4GD2.pdb file.

Figure 4.

An example of a list-representation of RNA secondary structure. Every cell of the matrix contains the nucleotide number that is WC-edge paired with the nucleotide indicated by the matrix index (above the cell). Therefore, nucleotide no 1 pairs with nucleotide no 25, nucleotide no 2 with 24 and so on. Base pairing is marked by black curved lines and the WC-edge interactions creating a pseudoknot by red curves.

Figure 5.

A scheme of the algorithm traveling around the secondary structure of RNA in the graph and list representation. The arrows and their numbers indicate sequential steps of the algorithm. Blue arrows mark helices and green arrows other structural motifs. Orange lines show the jumps that the algorithm takes after distinguishing a motif. The STOP sign indicates the position where the algorithm terminates.

Figure 6.

Secondary and tertiary structures of a 16S rRNA fragment (nucleotides 500–545) colored based on various descriptors calculated per nucleotide and averaged over the trajectory.

Figure 7.

Prevalent hydrogen bonding pattern of A535. The conformation is from the 9.49 ns frame with hydrogen bonds marked as blue dashed lines. Nucleotides are listed in Table 2. The structural context of this arrangement is shown and explained in Supplementary Figure S5.

Figure 8.

Heat map of the ϕ correlation coefficient for the 503–542 16S rRNA region from the MD simulation. The inset shows the secondary structure (43) with nucleotides creating a pseudoknot in green. Axes labels stand for nucleotide numbers. The ϕ coefficients larger than +0.4 (the cutoff for the color scale is defined by the user) are in red, lower than −0.4 in blue and the rest is in white. Nucleotides correlate with themselves so the diagonal is red. Every paired nucleotide and its neighboring pairs have ϕ above 0.4, indicating positive correlations in accord with their synchronous movement.

Figure 9.

Schematic representation of clustering of motifs listed in Table 5. Cluster 0 consists of motif 0 with code 4, cluster 1 of motif 1 with codes 0–6, and cluster 2 of motifs no. 2, 3 and 4. For motif codes see Supplementary Figure S1. The scheme presents two secondary structures of the same RNA fragment that was simulated. Motifs engaging the same nucleotides fall in the same clusters.