HBexplore—a new tool for identifying and analysing hydrogen bonding patterns in biological macromolecules

Klaus Lindauer, Cezar Bendic, Jürgen Sühnel
1996 Bioinformatics  
The program HBexplore is a new tool for identifying and analyzing hydrogen bonding patterns in biological macromolecules. It selects all potential hydrogen bonds according to geometrical criteria. The hydrogen bond table can then be subjected to further automatic or interactive analysis tools. These tools include the calculation of mean values and distributions of geometrical hydrogen bond parameters for parts of a single structure, for complete single structures and for structure sets, the
more » ... sification of each H-bond according to the participation of backbone, sidechain or base, ligand and water parts of nucleic acids or proteins, identification of Watson-Crick nucleotide pairs and of H-bonded pairs of equal nucleotides, the calculation of the mean number of H-bonds per residue and of the fraction of potential donor and acceptor atoms involved in H-bonds. HBexplore generates further automatically a H-bond residue interaction table. This table lists for all residues of the structure the other residues, ligands or water molecules directly connected via an H-bond. By means of a binary tree search algorithm this table is then converted into a H-bond cluster table. Clusters are understood here as an uninterrupted network of H-bonded residues. For nucleic acids secondary structures and tertiary interactions are automatically derived from the hydrogen bonding pattern. HBexplore is applied to two example RNA structures, a pseudoknot and a hairpin. It provides a comprehensive listing of individual hydrogen bonds and statistical information for larger structure sets. In addition, it can identify interesting new H-bond motifs. One example is a penta-nucleotide base-base H-bond interaction motif in the RNA pseudoknot. HBexplore is intended to contribute both to the elucidation of general principles of the architecture of biological macromolecules and to prediction and refinement of single structures.
doi:10.1093/bioinformatics/12.4.281 fatcat:qmeme4t63rhqzbj5acfoi5ky4q